DIGITAL IN HEALTHCARE · The HealthCare Futurists (HCFs) are an international and independent network, think-tank, make-tank, incubator, catalyst and consulting hub for innovation

DIGITAL TRANSFORMATION

IN HEALTHCARE

2017

a whitepaper of the HealthCare Futurists GmbH

This whitepaper contains the current state of the art assessment on

Digital Transformation in Healthcareas explored in November 2016 by leading experts in the field,

who have come together under the umbrella of the HealthCare Futurists.

The HealthCare Futurists (HCFs) are an international and independent network, think-tank, make-tank, incubator, catalyst and consulting hub for innovation in healthcare, life sciences and medicine. Our

mission is to collaboratively challenge the status quo and passionately push the limits of current thinking and practice in healthcare. We are renowned experts and thriving professionals of diverse disciplines, all

related to healthcare. We share a persistent passion for patient-centric, client-centric and customer-centric innovation and consider ourselves

to be Change Agents of Innovation and healthcare’s custom shop.

http://www.healthcarefuturists.com

HealthCare Futurists GmbHDüsseldorf OfficePlange Mühle 3

40221 DüsseldorfGermany

[email protected]

Twitter: @hcfuturists

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including responsibility for negligence is accepted by the HealthCare Futurists GmbH, its servants or agents. All information gathered is believed correct as of December 2016. All corrections should be sent

to the HealthCare Futurists GmbH for future editions.

From Disruption to Transformation: It will not happen tomorrow... 2

1. Digital Health in General with a Special Focus on the Medical Affairs ... 41.1 Overview of the Current State of Digital Health 51.2 Current Streams of Engagement and Official Interest 8

1.2.2 Co-Creation in Healthcare 13

1.2.3 Predictive Analytics 14

1.3 Data Privacy, Data Security, Data Ownership 17

1.3.1 Data Ownership in German Case Law 17

1.3.2 Federal Supreme Court of Germany (Bundesgerichtshof) 17

1.3.3 Academic Discussion on Data Ownership 18

1.3.4 Data Privacy in General 20

1.3.5 Right to Data Portability 20

1.3.6 Data Privacy and Data Protection 211.4 Dedicated Section on Health Apps and Tracking Devices... 22

1.4.1 Market Penetration of Health Apps: An Overview 22

1.4.2 Description of Health App Segments 25

1.4.3 Diffusion of Health Apps in Germany 26

1.4.4 Clear Settings and Associated Certifications 27

1.4.5 Evidence of Clinical Efficacy and Economic Feasibility 27

1.4.6 Data Security Compliance 27

1.4.7 Conclusion 281.5 The Digitally Embedded Patient: How Does the Patient of the Future .... 29

1.5.1 The Changing Roles of Doctors and Patients 28

1.5.2 Digital Levers to Engage Patients in Health Care Processes 29

1.5.3 Digital Patient Deliberation and Support 30

1.5.4 Digital Solutions to Increase Patients’ Self-Responsibility in Managing ... 30

1.5.5 Digital Solutions to Facilitate Patients’ Interactions with the Healthcare ... 31

1.5.6 The Current Digital Patient – Usage and Usage Barriers to Innovative... 321.6 Future Developments in Digital Health 34

1.6.1 Outlook on the Pharmaceutical Industry with an Emphasis... 341.6.2 The Issue of Data Ownership: What Kind of New Business Models... 351.6.3 The Issue of Data Security and Data Safety : where are Data Being Stored... 37

2. Special focus on Big Data Potential Assessment and Exploitation in Healthcare 39

2.1 Current State-of-the-Art and Application Examples 40

2.1.1 Introduction 40

2.1.2 Big Data 41

2.2 Deep Learning 44

2.3 Description and Assessment of Tools Used to Work on Huge Data Sets 54

2.4 The Possible Futures of Big Data in Healthcare 67

2.4.1 How Will Big Data-Driven Healthcare eventually be able to change... 67

2.4.2 Guiding RCTs: Generating Promising Hypotheses / Quickly Testing... 70

2.4.3 Complementing RCTs 71

2.4.4 What Kind of Impact will Ubiquitous Computing and Wearables.... 72

2.5 The Internet of Healthy Things (IOHT) 742.6 Augmented Reality: An Extraordinary Evolution of Technology Tools... 76

Table of Contents

2.6.1 Introduction 76

2.6.2 Market and Pharmaceutical Marketing 76

2.6.3 Medical Education with Augmented Reality 77

2.6.4 Augmented Reality within the Hospital/Private Practice 77

2.6.5 Augmented Reality within the Surgical Theatre 78

2.6.6 Future Uses of AR 78

2.7 Competitor Analysis 79

2.7.1 What are Other Companies Doing and How Successful are They? 79

2.7.2 Roche 79

2.7.3 Pfizer 80

2.7.4 Novartis 80

2.7.5 Merck 81

2.7.6 Mylan and Allergan 81Appendix 82

Concluding Remarks 84

Digital Transformation in HealthcareA Whitepaper of the Healthcare Futurists GmbH

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From Disruption to Transformation: It will not happen tomorrow if you expect others to do it

Disruption has recently replaced the word innovation, as if disruption were some kind of overhauled innovation on steroids. Things need to be super-new, not just new. They need to tilt and twist, not just work. Innovation has become mainstream and hence is seen as a lame duck. It seems we do not trust new things unless the old things are subject to almost profound destruction. Radical things are what we want in terms of change. However, change is good as long as for me everything remains the same; and then again we live in times of radical change and accompanying global insecurity. Language over time seems to acquire a tendency to reach out to superlative words like disappointed voters to overpromisers. Getting noticed in the age of attention span deficit has become a difficult challenge. In this context, an in-depth whitepaper such as this one seems to resemble a futile assault of nostalgia. A digital disruption difficulty, in fact.

Disruption, however, has been excessively quoted in consulting, C-Level lingo and startup pitches alike. It has therefore acquired a well-deserved place in the Olympic ranks of words whose unsolicited use obviously tries to veil the fundamental cluelessness of the user. The frequency of use is directly proportional to the meaninglessness of the content. It hence shares its fate with terms such as “sustainability” or “optimization”. Since this paper’s goal is not the semantic definition of words that refer to the field of further development of technology, advancement in the humanities, or progress in ethics – even though intellectually this would be worthwhile an endeavor - we decided not to dwell on the plethora of possible explanations seeking to make innovation commonly accessible. To solve this quest and discover this uncharted land, others might set sail. We have discovered notable serendipity in refraining from such great words and humbly gazing at what we have at hand in terms of change. Thus, we decided to name what we see with the descriptive term of transformation.

Disruption is spot on, the one, mind boggling intellectual supernova, the unicorn cantering by, leaving the professional in shock and the crowd in awe. This does usually not endure but is rather an ephemeral event. At the HealthCare Futurists, despite our denomination, we prefer to consider earthly aspects of business life and leave the shining stars aside. We claim to look at what we think is going to stay for good and change the world into a better place. These are the tiptoeing technologies which will soon come to a firm stand. It is not from a fashion perspective or a trendy approach that we have selected our views. We are looking at things that sometimes circuitously transgress from one vertical to the other and will shape our future and the way we practice and perceive healthcare.

It is this slow and yet persistent change of form, before anything else, that we currently see going on in several industries. To this round dance of transformation, healthcare, medicine and life-sciences are quite new aspiring bachelors. One might ask in bewilderment, how this had come about. It is amongst others, that healthcare still is, but in the past even more so was, the domain of regulation. Changes, challenges and chances were either funded by public money or governmental contribution, or they did not happen at all. The doors were guarded by professions and professionals who made sure nothing would get between them and the patient that was not to some extent based on evidence, which might be scientific or financial in nature.

The digital reformation, which is a part of the overall transformation process, is currently underway. What it does is changing behavioral patterns and intellectual approaches. It is bringing light to the setup


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of innovative processes in healthcare systems and empowerment to those who wish to learn more about their medical conditions. It does this by using its digital tools of transparency to find access to large bodies of knowledge and generate insights that were previously the right of the privileged few who possessed what is now called health literacy, and from there is slowly evolving into what is rightly called ‘citizen science’.

Disruption, to revisit this word again, has seemingly always started out with ideas and people seizing power over a realm they were previously secluded from. We know this as a reformatory process; the revolution has been procrastinated and delayed until new structures will have gained momentum. What has happened in times of reformation seems to have always been the alliance of technology, curiosity and necessity. In healthcare, we see all three of them equally expressed. As necessity arises from medical conditions, it is a constant trigger for people with acute but even more so with chronic conditions to tap into other sources of information and be ahead of the curve for their own good - further ahead often than their own physician who is tied down by fiscal, financial and federal constraints. Add common mistrust in expert systems and political bodies to the equation and there is more potential yet for technology and services entwined to satisfy needs.

For all physical beings, being body owners by nature, the sheer number of people interested in the achievements and infrastructure of modern medicine and what it can do is caused by innate human curiosity. This again leaves us with technology: it is always a means to reach a goal, but has never been the goal itself, for at the start of a new technology, there are no words for this matter, there is marveling only and an invention that will eventually become an innovation, let alone a disruption. So how should there be anything else but necessity and curiosity? Disruptions, on the other hand, if there are any, are not planned, predicted or pushed, they just happen. In fact, often their future potential is not revealed to their own creators who function as disruptors of their own ideas. It takes time and visionaries to lead these technology advancements to full market success. This is another reason why we are all better off talking about a digital transformation rather than a disruption. There might be a disruptive technology, but its potential needs to be envisioned by business explorers and product pioneers alike. And again, these processes are rather transformative in pace but also in result.

We want to provide an overview of what we see happening in other markets outside of healthcare and try to bridge the gaps between here and there, now and then, them and us by connecting the dots of what transformative processes are setting in motion. All of this is imprinted on the narrow and yet so vast area of healthcare, medicine and life-sciences. This whitepaper embarks on a journey together with the reader and we shall sail several very different seas listening to expert guides in their respective fields who each have their own style in reasoning and concluding. These different voices should get the reader oriented with the most current standards and aspects of digital transformation in general and in healthcare in particular. It points the way into one or more of our possible futures. It is, however, the reader’s responsibility to connect the dots with lines that lead to transformation into our next reasonable future. For the future always is what we allow ourselves to make of it.


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Chapter 1:Digital Health in General with a Special

Focus on th e Medical Affairs Perspective (Descriptive)


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An outsider’s view of the stakeholders of most European healthcare systems, and especially the German one, will most likely be led to the conclusion that digital transformation takes place at a very slow pace, if at all. This contrasts with what is happening within the organizations of healthcare providers and administrators. Since the turn of the millennium, digitalization has successfully been integrated at considerable volume: internal production processes at the payer level, especially in terms of accounting, customer care and patient steering have meanwhile to the greatest possible extent been digitalized. Fundamental to this success has been the well-established legal framework in individual countries. It defines explicitly the financial pathways for invoicing and payments on behalf of digital machinery. Furthermore, it is legally binding for all parties involved in service delivery and charging.

On the other hand, what is happening in terms of external affairs between the stakeholders is very heterogeneous. We see diversity in country-to-country comparisons as well as payer-to-payer and provider-to-provider relations. The reasons are discrepancies between payers and the lack of uniform legal frameworks. The latter ones are not yet well established, as opposed to digital invoicing but they need to become the pillars in setting up consistent digital relationships in the future.

Data security regulations have come up a number of times now as the one impediment for a quicker expansion of digital transformation. They are often considered to be too strict and then again there are age-specific pockets that harbor a deep mistrust of electronic communication using medical and personal data beyond the already existing and seasoned system of digital invoicing.Even more, in many cases the cost-benefit ratio of new digital offerings is unclear. At a payer level, this

leads to adherence to old behavioral patterns – most of which are pre-digital in nature. This approach is reinforced by systemic risk aversion and often rigid regulatory obedience at the public payer level. Even if this behavior might not be considered desirable from a transformative standpoint, it is very well understandable from the legal mandate of public bodies: their job is not to push innovations, let alone to develop them.

The basis for any activity in the field of digital health is electronic communication in a medical content. This ranges from classic and straightforward telemedicine applications, the acquisition of second opinions in dedicated second opinion portals, all the way to exploiting wearable data under the umbrella of “connected health”. In the not so distant future we will also see more and more surfacing around predictive analytics.We wanted to start you off in this whitepaper by showing some comprehensive charts and tables. A lot of thought has gone into the graphs, for they are trying to depict the actual situation in a very comprehensive way.

1.1Overview of the Current

State of Digital Health with Focus on the Diverse Groups

Affected (Patient, Payer, Physician, Pharma, Politics

etc.)


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1.1 Overview of the Current State of Digital Health

Figure 1: Electronic medical communication is the core function of digital health. The figure provides an overview of the current state in the German health care system. The deeper the color, the farther the implementation has been achieved at present. For details please refer to table 1a and the electronically provided comprehensive appendix. (contact [email protected] for details)

Fig. 1: Current State of Digital Health - Focus: Electronic Medical Communication


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1.1 Overview of the Current State of Digital Health

For the pharmaceutical industry, several new opportunities have opened. The industry can now directly approach patients and prescribers. They might do that with integrative “beyond the pill” solutions in mind or by using digital and social media as platforms for social feedback and health information. This can be helpful in terms of the provision of neutral medical aspects as well as marketing tools. As a matter of principle, in almost all digital communication relationships there is space for pharma contribution. From a payer side this might also be quite interesting in terms of strategic aspects.

Fig. 2: Pharmaceutical industry: Relevant general categories of digital health applications. The deeper the color, the farther the implementation has been achieved in the German public health caresystem. For further details refer to table 1 (appendix).


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1.2Current Streams of

Engagement and OfficialInterest

1.2.1 Health Literacy and Patient-Centricity

Health Literacy: it is a reformation rather than a revolution but in any event it is a revelation . Overall literacy, and especially health literacy, is one of the major achievements of modern societies which have made it a duty for children to acquire a least a certain degree of knowledge in understanding written texts. However, it is not only the writing that needs to be understood, it is also the thoughts (and often advice) in medical texts, contained within the alphanumerical codes that our brains are trained to decipher to make sense of the world, for better or worse.

Thus, according to the WHO, health literacy is defined as “the cognitive and social skills which determine the motivation and ability of individuals to gain access to, understand, and use information in ways that promote and maintain good health”1. It can be also seen as a “constellation of skills, including the ability to perform basic reading and numerical tasks required to function in the healthcare environment2”, as the American Medical Association puts it. However, it is academically put. Health literacy, at its core, means “the degree to which individuals have the capacity to obtain, process and understand basic health information and services needed to make appropriate health decisions”3.

1 Nutbeam, Don. (1998) Health Promotion Glossary of the World Health Organization.2 American Medical Association Ad Hoc Committee. (1999). Health Literacy for the Council on Scientific Affairs.3 Nielsen-Bohlman, Panzer, and Kindig. (2004) Health Literacy: A Prescription to End Confusion. Retrieved from https://www.nap.edu/read/10883/chapter/14 Berkman ND, et al. Health Literacy Interventions and Outcomes: An Updated Systematic Review. (2011) 5 Eichler, K., Wieser, S. & Brügger, U. Int J Public Health (2009) 54: 313. doi:10.1007/s00038-009-0058-26 US Department of Health and Human Services. Retrieved from https://health.gov/communication/literacy/quickguide/quickguide.pdf 7 American Medical Association Ad Hoc Committee. (1999). Health Literacy for the Council on Scientific Affairs.

Poor health literacy is likely to be associated with unfavorable health outcomes and a limited use of preventive care4. This also means that healthcare costs are, on average, higher in its absence5. It is estimated that up to one half of the US population has limited health literacy standards5, and it is probably not much different in the European countries. These mechanisms are very well understood; therefore; health education materials are being simplified in order to improve patient-to-provider communication and thus overall health literacy, which is considered to lead to more efficacious spending in healthcare6.

However, it is not only the absence of skills and abilities that render an individual incapable of comprehending complex healthcare-related content, or to understanding the professional language of physicians and care providers. Active neglect and turning a blind eye to the obvious also seems to be a part of the challenge. So it is not only a question of socioeconomic status, but also of the will to break habits, to change, and to innovate on a personal and systemic level. In this it does not come as a surprise that health literacy is believed to be a stronger predictor of health outcomes than social and economic status, education, gender, and age7.

It seems though that we are now addressing a well-known phenomenon more and more under the novelty


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1.2 Current Streams of Engagement and Official Interest

aspect of contemporary technological achievements. In this, we are guided by the thought that technology has already fixed a number of issues concerning longevity, so why should health-literacy (via health understanding and modern technology use) not be one of them? The root of this thought lies in the introduction of ubiquitous computing, with potentially 80% of adults carrying a supercomputer in their pocket by 2020. We may also see technological advances in connectivity affect health literacy habits. Technology and medicine seem to have been a matching pair in the West dating back as far as Hippocrates in ancient Greece.

Six out of ten respondents have used the internet to search for health-related information within the last year. At the top of the ranks are searches for general health information, nutritional information, and facts on lifestyle choices. In second place are queries for information on specific injuries, diseases, and illness conditions; as well as side effects of medications. It is no surprise that at the current stage, the early adopters in their twenties and thirties still lead the numbers, with the silver surfers catching up steadily8.

There is a clear trend that is owed to what I would refer to as democratization in healthcare - knowing that this might not be the best term to describe what is happening when patients engage with physicians and actively decide on their therapy. It might as well be called enlightenment in healthcare, alluding to Immanuel Kant’s famous words (quoted as such in 1784 in his text “What is Enlightenment?”): “Laziness and cowardice are the reasons why such a large part of mankind gladly remain minors all their lives […]. They are the reasons why it is so easy for others to set themselves up as guardians. It is so comfortable to be a minor. If I have […] a physician who prescribes my diet […] - then I have no need to exert myself.” And Kant continues: “I have no need to think, if only I can pay; others will take care of that disagreeable business for me.” This goes along with the “self-imposed nonage which does not lie in the lack of understanding but in indecision and lack of courage to use one’s own mind without another’s guidance.”

8 Flash Eurobarometer 404 “European citizens’ digital health literacy” (2014) Retrieved From http://ec.europa.eu/public_opinion/flash/fl_404_en.pdf

As we can see happening in paternalistic medical approaches, these “others” are guardians who have “kindly taken supervision upon themselves to see to it that the overwhelming majority of mankind […] should consider the step to maturity not only hard, but as extremely dangerous.” This also makes the guardians’ lives easier and more predictable, because they can fully leverage any effects deriving from this nonage to their advantage by exploiting value claims that are lacking in medical evidence in the context of proposition-induced demands. This means that health literacy, its level of development, and its acceptance, becomes a deeply ethical question within several groups: politicians, payers, physicians, patients, and industrial suppliers.

The latest driver of “democratization in healthcare” or “medical enlightenment”, to pick up this phrase one more time, is the so-called digital transformation in healthcare. We have seen a number of other fields affected, probably even disrupted, by the introduction of modern technology, especially information technology systems and the resulting change in customer empowerment and business models. In many markets this has led to the disappearance of the middle man when customers actively engage with providers and vendors. What is possible in the eBay commerce markets or the direct insurance markets might not yet be feasible in healthcare; but we see an increase in companies who are trying just that - actively ignoring regulations that have been around and untouched for decades.

One of them is the patient-physician relationship, which is considered to be the physical basis for therapeutic success. Lichtenberg, a German poet of the 18th century, once claimed that it was the physician’s duty to entertain the patient up until to the moment that nature had cured the disease. However, doctors have lost their entertainment acumen since then, probably because they are too much in love with professional technology themselves, and thus have ceased to be the only source prospective patients turn to in their quest for understanding the cause of their current condition. Often times, the first resource people consult has become an online search engine,


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followed by specific and dedicated websites including blogs and forums8. We also understand that it is a very private thing to search for medical information, so the vast majority who used the internet to look for health-related information did so for themselves8.

The European Commission’s report on European Citizens’ digital health literacy states that over three quarters of all respondents agree that the internet is a good tool for improving their knowledge of health-related topics. Almost nine out of ten people who looked for health information online say they were satisfied with the information they found. The biggest downsides are reliability of textual content, its commercial orientation, and its lack of detail8. These are the major pitfalls of data acquisition from unreliable sources, and it is a pivotal illustration of the accuracy of what is often heard - that data has become the new oil. Just as in the refining process and fractional distillation, compounds are intellectually separated and can be used in different ways according to their compounding quality, which translates into medical reliability and accurateness. This will be the catalyst for business models that facilitate the search for sound healthcare-related information.

Especially because healthcare so often deals with uncertain decision-making based on a number of influential factors from various sources, it will become key in a connected society to declare the origin and quality of data and information available to the lay population wanting to grasp their medical conditions. In the classic patient-physician interaction, the principal agency theory (framed by professional board exams on display at the doctor’s sideboard) made sure healthcare was provided by a reliable and well-tested source under the conditions of trustworthiness and efficacy. Nowadays, things are not so easy. Not only do the borders between healthcare and self-care become blurry, but some prosumer electronic companies find themselves in a steady process of change towards understanding the patient’s needs and detecting the underlying medical conditions at an early stage. What will be called disease interception in a couple years’ time started from humble beginnings in the trenches of historic epidemiology fights against cholera, which helped us understand the value of prevention. Prevention

cannot work without an engaged individual, both on the healthcare provider’s side and on that of the recipient.

This is where health literacy comes full circle. It is thus not about selling more medical interventions, it is about selling the right ones to the right individual at the right time and place. In times of information overload, trustworthiness issues, and the declining reliability of things that were taken for granted before, business models of the future that engage in the patient-centric arena need to be able to offer real value; not just for public reimbursement but also to convince a consumer of health goods of the value of a specific product or procedure. They must separate the wheat from the chaff by using the tools of digital transformation, such as self-learning algorithms that utilize a knowledge database linked to individual patient data. This will be a key asset in guiding patients through a maze of medical information in their search for more opinions and more security. The numbers of the EU report show that almost four out of ten people do not trust information from the internet when making health-related decisions. But then again, we already have indicators of the effect the internet has on well-being: people who have a poor health status use the internet less for health-related queries than healthy people8.

There are currently a number of physicians and healthcare professionals engaging in the field of patient enlightenment or patient empowerment through their efforts to increase health literacy. They are either curating their own webpages or creating medical apps where they provide links to trusted sources; or they have started to take action and fill the knowledge gaps with health literacy tools that they have produced themselves. The privately run webpage www.orthopaedie-fuer-patienten.de (orthopedics for patients) is an example for a health care provider taking action and making specific information accessible to patients in a way they are able to grasp and comprehend. Interestingly enough, payers were not too enthusiastic about Dr. Klein’s actions, so he turned his own conviction into a 3 kilo book project. Together with the initiative innovate.healthcare (http://innovate.healthcare), a healthcare hackathon event run by the HealthCare Futurists, this book will now also be made accessible to the digital patient.


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Health literacy is a product of simplification and communication. It is the core discipline in becoming patient-centric rather than disease-centric. Consider that it is healthcare that we talk about, not disease-care. It also means getting the patient involved in the design and setup of healthcare, which is of course as cumbersome as a new traveler coming into a cozy train compartment. A tool of health literacy needs to be a process of co-creation in healthcare, to understand the patient’s values and wishes as to how the product works.

It is the so-called P4 Medicine that will have a major influence on how we practice healthcare: predictive, preventive, personalized and participatory medicine, which quantify individual wellness and take the mystery out of disease9. Individual data clouds fueled by sensors we wear outside, on and inside ourselves will, to some extent, be able to predict future health statuses. It will also give us clues as to where prevention

9 Hood, Leroy and Price, Nathan D. (2014) Science Translational Medicine10 CMS; US Census Bureau, Bain & Company

makes sense, given our genetic setup, and we will learn to delay the progress of diseases or avoid future pathologies. On a personal level, we need the acknowledgment that genetically we are all different from one another (always confined to the n=1 conundrum)9. The participatory aspect points to the education of patients and their exchange of experience for example in social networks, which could become key in behavioral pattern change, because the interaction with a different peer group leads to a reframing on an individual basis. Health literacy then becomes the driving force for personal change in the digital healthcare age.

On a governmental level, however, there are different forces at work, such as rising costs that force the growth of a demographic weave of more engaged healthcare consumers. It is expected that online health literacy programs and mobile health per se will decrease the direct costs of healthcare in the US by 28% in 2020 compared to today10.

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In those healthcare systems where out-of-pocket payments do not constitute a large percentage of gross income, governments will likely push for more health literacy as a sustainable means of reducing costs. One of these cost-containment programs could be introducing prosumer tools to run diagnostic tests in a non-hospital setting, in order to deliver fast and accurate care at home. We will most likely see this happen in underserved and rural areas. Telehealth, mobile Health and overall eHealth trends point to the direction of more patient engagement11. This is needed to comply with the political, financial and humanitarian challenges ahead. The concept of digital scorecards comprising blood pressure/heart rate, body mass index, cholesterol levels, immunizations, appropriate preventive measures, and self-reported status could become another tool in the domain of health literacy12. Thus, more and more responsibility is given to the individual who is able and willing to follow up.

In the future, the commission has made it clear that activities aiming at increasing citizens’ digital health literacy will be supported13. This means that patients will be put in the driver’s seat. We will see the development of new indicators of how to assess the actual value of eHealth services in cooperation with users. This also ties into the concept of user-driven research and innovation in the area of eHealth. In the future, patient engagement need not remain political jargon.

Combining the world of health workers who also need to develop their digital skills (important stakeholders in the digital transformation of healthcare) with the reality that patients need to reliably use the eHealth assets delivered to them, we hope to find ourselves in a world of wider acceptance of eHealth technologies. For doctors, this translates into more meaningful time with their patients and fewer unnecessary appointments, thanks to the use of ePrescriptions, medication plans and tele-monitoring, just to name a few examples.

11 Wong, Genius. (2016). The Foundation For Healthcare Democratization. Health IT Outcomes.12 IOM (Institute of Medicine). 2013. Health literacy: Improving health, health systems, and health policy around the world: Workshop summary. Washington, DC: The National Academies Press.13 Quaglio, Gianluca , et al. (2016) Accelerating the health literacy agenda in Europe. Health Promotion International14 European Commission Memo: eHealth Action Plan 2012-2020: Frequently Asked Questions (2012) Retrieved from http://europa.eu/rapid/press-re-lease_MEMO-12-959_en.htm

Similarly, patients will be empowered to spend less time, effort and money on unnecessary GP and hospital visits. It is said that 80% of visits to the GP in the UK are from patients requesting repeat medication14.

Contrary to what one might think, given all these insights and the obvious coherences, health literacy is still in its infancy in Europe13, and has its limitations in terms of personal dismay. Even professionals fall short when facing fatal conditions themselves. This indicates that it is not a question of education level, willingness to break old habits, or unwillingness to recognize health hazards; but that in every individual, questions of value and trust prevail in how states of disease and well-being are perceived, dealt with, and complied with. It drills down again to the level of trust expressed towards healthcare providers, media, and other information carriers. Health literacy makes the patient a partner. It assigns more responsibility to the individual, but does not absolve the health professional of the responsibility to still act as a patient’s advocate, thus respecting that the patient has decided not to remain a minor in the Kantian sense as stated above.

The same accounts for technological solutions that support opinion-forming in both the healthcare professional and the patient. Technology can be a means of support, but it will most likely not be the key to questions of noncompliance, ignorance, or intentionally hazardous life styles. By and large, in a society that makes healthcare more and more a public affair (because of the way it is funded through tax money or contributions), and with data generation sources such as wearables permeating our daily lives, we must not forget that individuals are still free to exercise their right to ignorance and to disregard the facts provided to them. Event though we have asserted above that it is preferable for the citizen to become a “citoyen”, an educated participant in all things pertaining to the preservation of the health status, we should think


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about using technology not only to check whether something has been accomplished, but also to support the completion of goals. Health literacy and its ethical implications do not mean having the right to sacrifice self-responsibility on the altar of public surveillance, even when our assessment might differ with the individual’s choices with regards to staying healthy.

Technological advances in the history of mankind have provided us with a number of tools that have changed the way we live and how we perceive our world. When Gutenberg invented book printing, and when Martin Luther translated the bible from Latin (the professional language of the clergy at that time) into the language of the people, the foundations for what we now call the Reformation were laid. People drew their own conclusions about questions that had been at the heart of a profession, and they did that by exercising their right to enlightenment. It is this enlightenment, also called democratization, that then leads to revolutions, be they political or technological in nature. It is also the core of innovation: to marvel at the extraordinary within the ordinary and to put common things into uncommon contexts.

Today we see a similar thing happening: the highly-regulated healthcare systems that operate on certifications for medicines, machines, and medical practitioners are infiltrated and sometimes inundated by companies and entrepreneurial individuals. These entities make use of the digital area’s printing plate - the internet - and, like modern-day reformers, initiate digital transformation by trying to bring literacy to healthcare, questioning information asymmetry, and jeopardizing the dearly held status quo of those in the system and thus in charge. Given the fact that our ancestors already fought this battle over eternal life, it is interesting to see how massive professional resistance is in an area that only, and by all means professionally, deals with disease and with sustaining life, which is undeniably one thing amongst all: finite.

1.2.2 Co-Creation in Healthcare

Co-creation in the area of fast-moving consumer goods has become standard in certain product areas. It has been widely understood and accepted that customer

engagement can be increased by soliciting opinions on already existing products and those currently being developed, or by giving customers the chance to contribute to innovation and business development.

Healthcare poses a number of further challenges to the concept of co-creation and customer engagement which are legal and logistical in nature. Research and development have so far been quite remote from actual patient engagement. Rather, development seems oriented toward clinical demand, potential for reimbursement, and individual portfolio fit per company. Instead, in most cases we find product creation being driven by companies.

With the internet and the waves of digitalization pre- and post-internet, we have seen the possibilities of patient engagement change from individual members of patient organizations and official patient representatives being explicitly asked to contribute in advisory boards or political meetings, to patients taking on their fate and organizing themselves. This in turn has jeopardized compound marketing, because certain internet portals have become powerful opinion leaders, and internet services now serve as CRO support to recruit patients for their studies in considerably less time than would ever have been possible before.

This pull in co-creating healthcare has also already been seen in personal genetic services such as 23&me and others. While there is still discussion about whether patients ought to have access to their sensitive genomic data on Alzheimer’s disease and others without the guidance of a professional physician, patients already use these platforms to band together in pushing pharmaceutical companies and research labs to investigate remedies for ultra-orphan diseases. The advantage they clearly bring to the table is the fact that these individuals, besides having a profound understanding of their condition - usually unparalleled by any physician - also constitute the study population. So the expensive process of finding apt patients for studies is approaching zero.

Novelty technologies will enable us to rethink how we perceive and thus practice healthcare. Rethinking in this context means challenging common practices and putting them to the test. We anticipate that the way we take in


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medicines will be subject to these kind of challenges. This includes the form, color, size, coating etc. of orally administered agents; and we foresee this becoming a main domain of mass customization in healthcare, to the tune of 80% individualized polypill medication.

The will to co-create healthcare on an individual level is also a prerequisite not only for patient empowerment but also for successful and sustainable disease prevention. While current efforts of prevention primarily geared towards a holistic healthy living approach, individual factors such as genetic setup (eg. FOXO 47 Gene for carbohydrate), epigenetic interactions, and personal preferences are widely neglected. It is hoped that predictive analytics will provide a more granular approach towards individual risk factors and thus a more sustainable and co-created healthcare.

1.2.3 Predictive Analytics

We care about the future. Especially our future. And despite knowing it won’t end well in the end, we want to make sure it doesn’t end well too soon.

There are things we are not good at predicting. If you are about to die in a freak accident, you can at least take comfort in the fact that you wouldn’t have seen it coming. The same reasoning applies to terrorist attacks. There’s simply not enough data to predict these rare events.

We are much better at predicting whether you are about to click on an ad, or the likelihood that you will pay back your debt, simply because we have a lot of data about those events and it’s therefore easier to build accurate models of your future; but what about developing diabetes or having a heart attack? We know the exact probability of an average person running into those issues, but your individual risk is probably far from average. So maybe seeing your physician is a good idea, and on close examination they would be able to assess your risks more accurately. That’s what we do, and it works.

15 Quote by W. Edwards Deming: “In God we trust; all others bring data.”.” 2012. 13 Sep. 2016 http://www.goodreads.com/quotes/34849-in-god-we-trust-all-others-bring-data16 Wigner, Eugene P. “The unreasonable effectiveness of mathematics in the natural sciences. Richard courant lecture in mathematical sciences delivered at New York University, May 11, 1959.” Communications on pure and applied mathematics 13.1 (1960): 1-14.17 Halevy, Alon, Peter Norvig, and Fernando Pereira. “The unreasonable effectiveness of data.” IEEE Intelligent Systems 24.2 (2009): 8-12.

But is it the best we can do? Your physician is well trained and knows a lot of things about you. But I doubt they know about your browser history (unlike the ad network that predicted you would click on that mortgage ad). They don’t even know about highly predictive factors in your genes. And even if we would show them your genetic information on a DVD, would they be able to make use of it?

Not really. Nobody could. The genome of a single person is more data than any human could read in a lifetime, let alone make sense of it. If we want to extract information from this data, we’ll have to let the machines take a look. And not only at one individual genome. At all genomes.

Not long ago, we had no idea about germs and viruses. They were already there, but we couldn’t see them until we had good enough microscopes. Nowadays we collect all kinds of data and won’t be able to see much if we don’t look at it with the right instruments.

In God we trust, all others bring data.15

While mathematics has proved to be surprisingly useful in the field of physics as described in the paper “The Unreasonable Effectiveness of Mathematics in the Natural Sciences” by Eugene Wigner16, its application has been less successful in the fields of medicine or social sciences. There’s no elegant mathematical formula to predict whether someone will develop colon cancer next year; but being unable to formulate elegant equations describing the health status of humans sufficiently does not mean we can’t do anything. Maybe we have to accept the complexity of human beings and their environment as a given and resort to the next best option we have: looking at data.17

With enough data, some things become pretty easy which otherwise would be really hard - spell checking, for example. The traditional method of spell-checking was to look up each word in a dictionary. When a word


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was not found in the dictionary, we would assume it was misspelled and search the dictionary for the word that was most likely intended instead. This search was done via complex heuristics like soundex and metaphone to find words which sound similar but are spelled differently. But if you have enough data you won’t have to deal with complex heuristics. Let’s assume you have a lot of textual data; then you can build a dictionary automatically. You’ll generate a list of edits for each word you want to check and filter out words that are not in your dictionary. After that, you calculate the probability for each generated word and take the one with the highest probability. Now it’s possible to do that with less than two pages of computer code18.

18 “How to Write a Spelling Corrector - Peter Norvig.” 2010. 15 Sep. 2016 <http://norvig.com/spell-correct.html>19 Banko, Michele, and Eric Brill. “Scaling to very very large corpora for natural language disambiguation.” Proceedings of the 39th annual meeting on association for computational linguistics 6 Jul. 2001: 26-33.

There are two ways to extract more knowledge from data:

1. Build better algorithms to get more insight from the data you have

2. Gather more data

In 2001 Michele Banko and Eric Brill published a paper comparing different learning algorithms in different data sizes19. They showed that it’s not possible to predict the relative performance of algorithms when you increase the order of magnitude of your dataset. So one algorithm might seem to be weak on a small dataset, but does much better on a big dataset compared to other algorithms and vice versa. This makes sense, because any sufficiently complex model should reflect the complexity of the data it was trained on, not the complexity of the algorithm that was used to train it.


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But what does it mean if we accept the premise that we have to use complex models based on huge amounts of data to predict future events? In the past one of our best guidelines in science was the use of Ockham’s Razor. It’s the principle that entities must not be multiplied beyond necessity or that simplicity is a guide to truth. If we are given a set of observations and have to come up with a theory which would have predicted them, we tend to choose the simplest one.

But if we apply Ockham’s Razor as a method of choosing which model is better at explaining data we try to learn from, it seems that it doesn’t work so well20. Maybe it’s a really good idea to use Ockham’s Razor in a world where the only way to teach a natural law is to write it down on a blackboard or in a book. And maybe the reason for this idea being good lies not so much in the world being governed by simple rules, but that simple rules are the only ones you can write down and read again as a human being. Model simplicity is probably a really good inductive bias in a world where the lack of information technology is the bottleneck.

For a world where access and transfer of information is no longer scarce, other inductive biases might be more appropriate. In other words: we don’t have to be able to understand a model to be able to make use of it. This is good, because the number of useful models will increase without this constraint. But it will also be strange, because will no longer be able to explain why our predictions work.

20 “Ockham’s Razor is Dull - Apperceptual.” 2012. 15 Sep. 2016 <http://blog.apperceptual.com/ockham-s-razor-is-dull>


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1.3Data Privacy, Data Security,

Data Ownership

Neither European nor German law recognize “one” data law that covers all aspects of data as such. Therefore, data is neither singularly protected by data privacy law nor by any other existing legal provision. Depending on the quality of the respective data or its relation to an individual, the approaches to legal data protection vary substantially.

The question of whether data rights already exist and/or should be introduced is subject to intense academic discussions in Germany and across Europe (for a detailed survey see Osborne Clarke’s “Legal study on Ownership and Access to data”, A Study for the European Commission DG Communications Network, Content &Technology). The majority of scholars tend toward the conclusion that a property right to data does not exist. Moreover, there seems to be a consensus that a right to data should currently not be established due to the unpredictable effects such a right may cause.

German scholars categorize rights in two groups: absolute rights (i.e., erga omnes rights) and relative rights. Absolute rights apply with respect to any third party. Such absolute rights grant the entitled person an exclusive authority with regard to a certain legal position (e.g., an item or a patent). Relative rights only grant legal claims towards particular individuals, e.g. contractual obligations which only apply vis-à-vis the contract partner. In fact, the discussion about data ownership in Germany is a dispute about whether there should be an absolute right in data (not necessarily ownership). Approaches to establish data ownership are as various as

21 BVerfG, judgment dated 15 December 12.1983 – 1 BvR 209/8 – Volkszählungsurteil = NJW 1984, 41922 BGHZ 143, 307, 309; 109, 97, 100 f.; 102, 135, 144; BGH, judgments dated 4 March 1997 – X ZR 141/95 – MDR 1997, 913; 14. July 1993 – VIII ZR 147/92 – NJW 1993, 2436, 2437 f.; 7 March 1990 – VIII ZR 56/89 – NJW 1990, 3011; 6 June 1984 – VIII ZR 83/83 – ZIP 1984, 962, 963; decision dated 2 May 1985 – I ZB 8/84 – NJW-RR 1986, 219

there are provisions in German law regarding or relating to the protection of data. They range from criminal law, copyright law, competition law, general civil law, tort law and data privacy law to telecommunication law.

1.3.1 Data Ownership in German Case Law

There is currently no judgment of the Federal Constitutional Court (Bundesverfassungsgericht - “BVerfG”) that addresses ownership in data as such. However, the BVerfG stated in 1983 that an individual does not have absolute and unlimited rights in data. The data about a person instead represents an image of social reality, which cannot be allocated exclusively to the data-generating person21.

1.3.2 Federal Supreme Court of Germany (Bundesgerichtshof)

The Federal Supreme Court (Bundesgerichtshof - “BGH”) has issued various judgements concerning rights in data, but it has not yet acknowledged ownership in data as such.

Traditionally, ownership in German civil law depends on a physical object. In contrast, data is not physical as such and no longer depends on a physical carrier. The BGH has adhered to this fundamental principle of German civil law in its judgements so far. To constitute a physical object (a “thing”) in accordance with Sec. 90 BGB as a prerequisite for ownership rights, it is decisive for data to be stored on a data carrier22.


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1.3 Data Privacy, Data Security, Data Ownership

The BGH has dealt with further aspects of data - taking its commercial value into account - by acknowledging that a data subject can have commercial interests in its own personal data (in this case, a photo of the actress Marlene Dietrich) which might even include a licensing right23. Further, in two different judgements in 1999 and 2006, the BGH recognized that the use of customer data by a business can constitute the violation of trade and business secrets24.

The higher regional courts (Oberlandesgericht – “OLG”) in Germany have supplemented the rulings of the BGH by focusing on additional aspects of data.

In 1995, the OLG Karlsruhe made a landmark decision on the destruction of data, stating that the deletion of data stored on a data carrier may violate the ownership in the data carrier pursuant to Sec. 823 para. 1 BGB25. Thus the decision extended the protection of ownership rights in regard to the data carrier onto the data itself.

A recent judgement by the OLG Naumburg addressed issues regarding the legal authority to read and change data collected in a radar control system26. The judgement examined whether the producer of electronics or the owner may use the data generated by such systems with the help of Sec 202a StGB. According to the OLG Naumburg, the data access should belong to the person generating the data.

The review of the available case law shows that the establishment of a veritable and dogmatically reliable concept of an erga omnes right ultimately fails because the provisions put forward to support such a right cover only certain aspects of data, are limited to certain situations or addressees, or may not be transferred to the specific dynamics of data.

23 BGH, judgment dated 1.December 1999 – I ZR 49/97 - Marlene Dietrich = GRUR 2000, 70924 BGH, judgment dated 14. January 1999 – I ZR 2/97; judgment dated 27.April 2006 – I ZR 126/03.25 OLG Karlsruhe, judgment dated 7 November 1995 - 3 U 15/95 - Haftung für Zerstörung von Computerdaten = NJW 1996, 20026 OLG Naumburg, judgment dated 27 August 2014 – 6 U 3/14 = CR 2015, 8327 Grosskopf, IPRB 2011, 25928 Hilgendorf, JuS 1996, 509 (511); Hoeren, MMR 2013, 48629 (e.g., Dorner, CR 2014, 617; Zech.CR 2014, 138 (142); Schefzig (co-author of this study), K&R 2015, Beihefter zu Heft 9, 3 (6); Kraus, TB DSRI 2015, 537; Grützmacher, CR 2016, 485

1.3.3 Academic Discussion on Data Ownership

There are three main positions among German legal academics: (i) erga omnes rights in data already exist, (ii) erga omnes rights in data do not exist but should be created, and (iii) erga omnes rights in data do not exist and there currently is no need for additional laws.

Approaches to deriving data ownership from already existing principles or provisions in German law are various. They range from granting ownership in the traditional sense to intending to circumvent the necessity of a physical object by classifying data as the fruits (“product”) of a thing27. In the end, these approaches are not convincing, as the German principles of ownership require a corporeal quality.

Other legal scholars argue that the German law already acknowledges a right in data because such a right would be a prerequisite for the protection of data under criminal law28. Since Sec 203a ff. German Criminal Code (Strafgesetzbuch – “StGB”) and Sec 303a StGB protect data, it commonly understood that the legal asset which is protected by these laws is the authority to utilize the data. In conclusion, this authority to utilize data should be seen as an erga omnes right pursuant to Sec 823 para 1 German Civil Code (Bürgerliches Gesetzbuch – “BGB”).

However, the majority of legal scholars argue that German law currently does not acknowledge a right in data as such29.

Some of these scholars support the establishment of an erga omnes right to set incentives for the data economy and to create legal certainty (Zech, CR 2015, 137 (144 et al.). Others regard existing contractual solutions as


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sufficient to protect data effectively30. Therefore, these academics argue that it has not yet been shown that there is indeed an economic necessity to create a right in data. Furthermore, the artificial limitation of data might negatively affect innovation because especially large data applications depend on large amounts of data.

Data privacy law regards data as a threat, not as an asset. Essentially, data privacy law is a personality right31, designed to protect the individual from any infringement of their right to privacy resulting from the collection, processing, use and transfer of personal data, cf. Sec 1 Para 1 Federal Data Protection Act (Bundesdatenschutzgesetz – “BDSG”). As a consequence, data privacy law does not protect data as such, but the information contained therein relating in various degrees to an individual.

Data protection laws protect the individual’s right of informational self-determination. Therefore, the data itself should be protected by the general right of personality as well. Other scholars simply state that the extensive rights of data subjects regarding their personal data implicitly establish a general right of the data subjects to commercially exploit their data. This theory would lead to an erga omnes right of subjects in their data because the general right of personality is recognized as an erga omnes right. If third parties used personal data without the necessary justification, the data subject would have a claim for damages and basically have similar rights as if it owned such data. But the general right of personality as such cannot be transferred (or at least, only to a limited extent). Therefore, the data subjects could only trade their data to a limited extent.

The majority of legal scholars argue that there is no general right of data subjects in their data. Even though data protection laws grant the data subjects rather extensive rights, data protection law would only be a regulatory instrument of the public law, which is supposed to regulate the interaction of data subjects and data controllers, but should not create private, commercially exploitable rights. This finding seems to be supported

30 Dorner, CR 2014, 617; Schefzig, DSRITB 2015, 551; Grützmacher, CR 2016, 48531 Hoeren, in: Grützmacher, Recht der Daten und Datenbanken im Unternehmen, 1st edition 2014, § 23 par. 432 Dorner, CR 2014, 617, 62133 Dorner, CR 2014, 617, 622

by the “census judgement” (Volkszählungsurteil) of the BVerfG in which the court stated “information, also information on people, is a picture of social reality which cannot be allocated exclusively to the data subject”.

Data privacy law defines the responsible body as the Controller and grants the individual extensive rights towards this Controller, including information, erasure, and correction rights. Hence, the BDSG confers on the data subject a position similar to actual ownership. But this position is limited to the specific individual, is directed against the data Controller, and is restricted to personal data. Data as such is not exclusively related to individuals. Technical data stripped of or initially compiled without any connection to identifiable individuals does not fall within the scope of data privacy law to begin with

Particularly in connection with large amounts of data, Sec 4 para 2 sentences 1 Copyright Act (Gesetz über Urheberrecht und verwandte Schutzrechte – “UrhG”) comes into focus. Databases structuring data systematically or methodically qualify as personal intellectual property and are therefore protected by copyright law. A high level of creativity (“Schöpfungshöhe”) is required. In the case of mere data analysis this requirement is not met32. Further, Sec. 87a to 87e UrhG which govern the protection and use of databases might be applicable. The creator of the database is exclusively entitled to reproduce, distribute or publicly display the database. In contrast to the protection of databases - pursuant to Sec 4 para 2 Sentence 1 UrhG - the protection does not originate from a certain level of creativity, but rather from the economic effort necessary to compile, verify and arrange the data33.

Both concepts grant protection to the database as a whole but do not create ownership in the data itself.

The patient’s right to inspect their medical records originates in their right of self-determination and the patient’s personal dignity, as those records affect


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them directly in their privacy34. In addition, this right of inspection is now legally standardized in the Medical Association’s professional code of conduct, in Sec 630g BGB, Sec 810 BGB. First and foremost, this right aims to grant the patient a right to inspect his medical records. This includes the right to obtain a transcript of the patient’s medical file. Sec 630g para 2 BGB. Sec 630g para 2 also compromises the right to receive an electronic copy of the file but only if and so far as the file is compiled electronically35. The inspection right is a specific form of the patient’s right of information. Therefore the information has to be readable and uncoded. However, the right of inspection does not establish any right of ownership, nor does it limit the physician’s right to process the data lawfully, e.g. to safeguard their own justified interests.

1.3.4 Data Privacy in General

Any collection, processing, and use of personal data is subject to the ban of permit reservation pursuant to Sec 4 para 1 BDSG. Therefore, it is only admissible in cases of legal justification or the data subject’s consent. Personal data means any information concerning the personal or material circumstances of an identified or identifiable individual, Sec 3 para 1 BDSG. Health data will often qualify as a special category of personal data pursuant to Sec 3 para 9, referring explicitly to an individual’s state of health; therefore the collection, processing, and use of this data is even further restricted and limited in Sec 28 para 6 to para 9 BDSG. In accordance with these provisions, processing of special categories of data is only admissible in certain situations, e.g. if necessary for medical treatment or diagnostics by groups bound to confidentiality, such as physicians. Further processing is admissible if necessary to protect vital interests of the data subject in case the data subject is unable to provide consent, if the data is made public by the data subject, or in case the data is necessary in relation to legal claims, or for scientific research in observance of a strict principle of proportionality. Outside these limited purposes the controller has to revert to the data subject’s consent. This

34 BVerfG decision dated 16. September 1998 – 1 BvR 1130–98 – NJW 1999, 177735 Weidenkaff, in: Palandt, 75. Auflage 2016, § 630g par 436 cf. Sec 1 Para 1 BDSG37 European Court of Justice, Breyer v Germany – Case C-582/14

will be the case with most mobile health applications and even with medical products specializing in data analysis.

Only data processing that does not include personal data as such, e.g. mere technical data or data stripped of any personal reference (anonymous data) is excluded from these strict prerequisites, since it is not subject to data privacy laws. As a result, the eligibility criterion for the protection of data under the BDSG correlates with the direct or indirect personal reference to a specific individual36.

The question of whether data is personal data is subject to a heated debate, mainly because the answer to that question determines whether data privacy law is applicable (for a general overview see Brink/Eckardt, ZD 2015, 205; Bergt, ZD 2015, 365). This is essentially a question regarding the requirements necessary to establish a connection between an individual and the respective data. In other words: when can a person be identified by means of the available data? Most data privacy authorities have adopted an absolute approach: data qualifies as personal data if anyone might hypothetically identify the individual in question. Consequently almost any data qualifies as personal data. Contrary to this absolute approach is the relative approach presented by most practitioners and moderate scholars: decisive for the qualification as personal is the question of whether the controller can realistically identify the data subject with reasonable effort. Following that approach, the controller determines whether the data in question is personal or not. Neither the Regulation 95/46/EC nor the recently adopted General Data Protection Directive offer an unambiguous answer. The currently pending case before the European Court of Justice addresses this particular question and might bring some clarification37.


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1.3.5 Right to Data Portability

The General Data Protection Regulation (“GDPR”) introduces the right to data portability, which has no current equivalent in European privacy law. Art. 20 of the GDPR enables the data subject to receive a copy of their personal data currently residing with the controller in a structured, commonly used and machine-readable format, as well as the right to have their data transferred directly from one controller to another, Art. 20 para 1 GDPR. This right only applies in limited circumstances, e.g. in case the data was obtained directly from the data subject based on consent or on a contractual basis and the processing was carried out by automated means. Initially expected to pave the way for an equivalent to data ownership, the right to data portability is still not suitable to establish such an absolute right. However, it is true that the data subject gains additional disposition rights in regard to their data38.

At the same time, there are substantial limitations to the right to data portability which separate this right from the concept of ownership. First of all, Art. 20 grants only the right to receive a copy of the respective data and explicitly excludes the data subject’s sole disposition over the respective data by detaching the right to data portability from the right to erasure, Art. 20 para 3 sentence 1 GDPR. The controller might still use the data as long as there is a corresponding legal justification. Furthermore, this right refers only to data available at a specific time and, most importantly, solely to the data subject’s own data. As most of the data today relates not to one individual alone, the amount of data the data subject might transfer is severely restricted by the rights of other data subjects39.

1.3.6 Data Privacy and Data Protection

Data controllers are subject to various data protection requirements. As a central provision for data protection, Sec 9 BDSG and its annex require the implementation

38 Jülicher/Röttgen/v. Schönfeld, ZD 2016, 358, 36139 Kamalah, in: Plath, BDSG, 2nd edition, 2016, Art. 20 DSGVO par. 540 Ernestus, in: Simits, BDSG, 8. Auflage 2014, § 9 par. 2741 Ortner/Daubenbüchel, in: Medizinprodukte 4.0, NJW 2016, 2918, 291242 Störing, in: Von Apps und Atommeilern, c’t 2015, 154

of adequate mandatory technical and organizational measures. This includes physical and electronic access control to data processing systems and to the data itself. The legislator does not define what he considers to be adequate measures. However, it can be derived from Sec 9 and the general principles of the BDSG that the requirements of these measures correlate with the sensitivity of the respective data40. As health data often qualifies as a special category of data pursuant to Sec 3 para 8 BDSG, this affects the level of data protection.

In July 2015 the new IT-Security Act (IT-Sicherheitsgesetz – “IT-SiG”) came into effect. Operators of critical infrastructure are now obliged to cooperate more closely with the German Federal Office for Information Security (Bundesamt für Sicherheit und Informationstechnik – “BSI”). The IT-SiG addresses foremost nuclear plant operators, gas and electricity providers, and telecommunications network operators. Nevertheless, critical infrastructures might also originate in the health sector as Sec 2 Para 10 No 1 BSIG explicitly mentions the health sector as a critical infrastructure. Which parts and players of the health sector are affected by the new requirements is yet unclear. Specifics are subject to a legislative decree expected for 201741. But even today, many data controllers or providers will be subject to the effects of the IT-SiG, since - almost unnoticed by the public - the changes also address providers of commercial telemedia services such as online shops, search engines, webmail services and websites42. The requirements of the IT-Sig were implemented by inserting the new § 13 para 7 into the German Telemedia Act (Telemediengesetz – “TMG”). Providers have to ensure the safety of their systems by means of technical and organisational measures, as far as is technically possible and economically reasonable. This reinforces technical and organisational compliance obligations. Infringements of these compliance obligations are now subject to severe fine proceedings and may also trigger competitive warnings by fellow competitors.


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1.4Dedicated Section on

Health Apps and Tracking Devices

and their Regulation

1.4.1 Market Penetration of Health Apps: An Overview

Nowadays, digital technologies shape the market environment of almost every industry. Huge players as well as young entrepreneurs are developing novel approaches to facing every kind of daily challenge and unsatisfied customer need. Healthcare, as one of the most attractive markets, is no exception. Digital solutions developed for patient treatment, diagnosis, disease management, communication needs, and patient data exchange, along with other numerous applications, are labeled as eHealth or digital health solutions.

One of the fastest growing segments of eHealth is the mobile health market, which includes all services and applications that may be carried out using mobile devices such as smartphones, phablets, tablets or wearables. According to official data, the worldwide mobile health revenue is expected to total as much as 23 billion U.S. dollars in 2017, up from 4.5 billion U.S. dollars in 201343. From 2013 to 2020, the compounded annual growth rate (CAGR) of the global mobile health market is projected to reach 36%44. In comparison, the worldwide market for IoT (Internet of Things) solutions will experience an annual growth of 20% from 2013 to 202045.

Health apps represent an opportunity to make mobile health available to patients, healthcare providers, and stakeholders. Among others, they might be used to identify a patient’s disease in its early stages, improve

43 Statista: Forecast of the worldwide mobile health revenue since 2013. Available at: http://www.statista.com/statistics/218843/forecast-of-the-world-wide-mobile-health-revenue-since-2013/ (last accessed 05/09/2016)44 Statista: Forecast CAGR of worldwide digital health market by segment. Available at: http://www.statista.com/statistics/387875/forecast-cagr-of-worldwide-digital-health-market-by-segment/ (last accessed 05/09/2016)45 IoT: Worldwide regional forecast 2014 – 2020. Available at: https://www.business.att.com/content/article/IoT-worldwide_regional_2014-2020-fore-cast.pdf (last accessed 05/09/2016)

or manage its treatment, increase prevention, encourage patients to adopt a healthier lifestyle, and make them more informed and aware of their own health. There are various applications for health apps. While some of them may be used for wellness purposes, others may be applied for clinical or medical use. The differentiation between wellness and medical apps plays an important role due to the fact that there are different regulations associated with each. Health apps labeled as medical must undergo a certification processes by the supplier, which includes a demonstration of clinical efficacy and economic feasibility before reaching the final target population.

With a wide range of contents and user interaction, wellness apps are developed to improve or support a healthy lifestyle. They might track for example the mobility of their users and provide recommendations such as daily activity targets or nutritional advice.

In contrast, medical apps are applied to prevent or diagnose diseases. Furthermore, they support the treatment and therapy of patients, for example by measuring a patient’s vital signs and subsequently sending push alerts to physicians automatically in case of severe results. For the measurement of vital signs, the sensors of smartphones or wearables may be used. Furthermore, additional devices such as electrodes for ECG measurements or blood glucometers may be connected to enhance the range of applications for health apps. Ongoing technological developments increase the opportunities for healthcare with every passing day.


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1.4 Dedicated Section on Health Apps and Tracking Devices and their Regulation

Consequently, the countless functions of both wellness and medical apps have led to new health apps being registered in the online stores of Apple, Android or Microsoft every day. Their developers aim to either attract a huge number of patients in the second healthcare market, which includes only privately purchased products or services, or to convince health insurance companies and other stakeholders to promote their medical apps for standard care, by promising a frequent use of the application by healthcare practitioners or patients. These target consumers represent attractive revenue for developers. To enter standard care - and therefore the first healthcare market - app developers promise huge medical as well as economic benefits for patients, healthcare providers, and payers. Nevertheless, they often lack valid evidence, and as a consequence, the diffusion of medical apps into the first

46 CHARISMHA-Study - Chancen und Risiken von Gesundheits-Apps. Available at: http://www.bmg.bund.de/fileadmin/dateien/Downloads/A/App-Studie/CHARISMHA_gesamt_V.01.3-20160424.pdf (last accessed 06/09/2016) 47 IMS health study: Patient options expand as mobile healthcare apps address wellness and chronic disease treatment needs. Available at: http://www.imshealth.com/en/about-us/news/ims-health-study:-patient-options-expand-as-mobile-healthcare-apps-address-wellness-and-chronic-disease-treatment-needs (last accessed 06/09/2016)48 CHARISMHA-Study - Chancen und Risiken von Gesundheits-Apps. Available at: http://www.bmg.bund.de/fileadmin/dateien/Downloads/A/App-Studie/CHARISMHA_gesamt_V.01.3-20160424.pdf (last accessed 06/09/2016) 49 Taking in account that there are approximately 2.2 million different apps available

healthcare market remains low, at least in Germany.

A study published by the European Commission in 2014 estimates that by 2017, around 1.7 billion people will use health apps. According to several sources, there are currently more than 160.000 apps available that are tagged as health apps. Within app marketplaces (e.g. Apple’s App Store, Android’s Play Store) they are usually available in the categories “Health and Fitness” or “Medical”46,47. The category can be randomly chosen by the developer, however. The number of available health apps may be reduced by excluding those that are published in several app marketplaces at the same time, which reduces the total number down to about 100.000 downloadable health apps48. Consequently, approximately every 22nd app is categorized as a health app49. However, there are

Figure 3: Differentiation of eHealth solutions (own illustration)


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only 8.500 health apps offering content in German50.

A common segmentation of health apps relates to their individual purpose in healthcare. Therefore, the following categories are established: monitoring, diagnosis, treatment, health practitioner support, wellness, administration and prevention. According to official data, in 2017 the market share for those segments will be the one shown in Figure 451.

Nevertheless, the distinction between wellness and medical health apps depends highly on the intended

50 Gesundheits-App, Medizin-App, Medizinprodukt? Klassifizierung nach Gesundheitszielen & Nutzerzielgruppen. Available at: http://www.healthon.de/sites/default/files/uploads/files/wp-content/uploads/2016/03/1603_Gesundheits_Medizin_Apps_Medizinprodukte_Healthon.jpg (last accessed 06/09/2016)51 Statista: Global mobile health market share forecast by service category. Available at: http://www.statista.com/statistics/219262/global-mo-bile-health-market-share-forecast-by-service-category/ (last accessed 06/09/2016)

user group, the app’s provided functions, and its offered features. Regarding the segmentation displayed above, health apps that belong to the monitoring segment can be both wellness and medical apps.

The following two examples illustrate a possible use of a wellness and a medical app:

!

Worldwide mobile health market shares in 2017

0.010.01

0.03

0.05

0.1

0.15

0.65

Monitoring DiagnosisTreatment Health Prac>>oner SupportWellness Administra>onPreven>on

Figure 4: 2017 Mobile health market shares worldwide by segment depending on expected revenues


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1. Wellness app: Monitoring a user’s activity for fitness reasons.

2. Medical app: Tracking a patient’s pulse, providing information about the patient’s risk of developing a chronic heart disease, and if required, displaying recommendations for subsequent courses of actions to treat the condition.

In the case of the second example, such an app needs to be certified as medical product (especially if it is remotely connected to other medical products such as a blood glucose meter). In addition, there are several risk levels of medical products in Germany, ranging from risk level I (low risk) to level III (high risk). Medical apps are mainly allocated to risk level I. Nevertheless, those apps that allow an active diagnosis or therapy for a certain disease, as well as apps used for birth control, are associated with risk level II52. Higher risk levels are also associated with more strict certification requirements. For medical products of risk level I, the developer proves the conformity of the product by providing product documentation and risk assessment. All medical products that are classified into higher risk levels are assessed more closely and certified by an independent authority.

While for accessing the US market a certification by the FDA is required, products sold in the European Economic Area require a CE marking53.

1.4.2 Description of Health App Segments

Prevention apps are applied in order to support primary, secondary or tertiary prevention. While primary prevention is used to minimize the risk of developing a certain disease, secondary and tertiary prevention apps aim to stop or decelerate the further progression of sickness. In the context of primary prevention, health apps should support a healthy lifestyle by providing information about healthy nutrition, mobility, or stress management, for example. Consequently, primary prevention apps are very likely to be categorized as wellness apps. Apps that are used in the context of secondary or tertiary prevention allow their users to

52 Bundesinstitut für Arzneimittel und Medizinprodukte: Orientierungshilfe Medical Apps. Available at: http://www.bfarm.de/DE/Medizinprodukte/Abgrenzung/medical_apps/_node.html (last accessed 13/09/2016)53 FDA: US Food and Drug Administration / CE: Conformité Européenne

document parameters such as blood glucose levels in a type of diary, which is then used to provide information about further activities or to synchronize the information with a physician in order to adjust treatment.

In Germany, prevention apps may represent a cheap opportunity to reduce future costs for patient treatment. Therefore, they pose a great alternative for special tariffs or programs provided by health insurance companies in order to increase a patient’s awareness concerning risk factors related to certain diseases. Nevertheless, there has to be proven medical evidence as well as ease of use for both health practitioners and patients in order for the app to be accepted within the market.

By contrast, health apps may be used by health practitioners or patients to diagnose a patient’s disease. There are several way diagnostic apps can support their users. While some of them employ medical dictionaries displayed on a handheld device, providing all necessary symptoms for and possible differentiations between diseases, others use sensors built into smartphones or other mobile devices to measure vital parameters, and can even signal the risk of manifesting a certain disease. This last case especially requires medical evidence and certification as a medical product. Diagnostic apps are often used to detect heart disease or to measure the risk of diabetes and psychological diseases. Another prominent use case is the diagnosis of skin diseases. In contrast to the measurement of vital parameters, in this case patients exchange photographs through digital platforms in order to get an anonymous diagnosis.

At first glance, diagnostic apps offer great potential, especially for health insurance companies, to detect a patient’s sickness at an early stage and therefore reduce the higher costs associated with the progression of a disease. Nevertheless, the application of diagnostic apps might increase the usage of healthcare resources by patients, leading not to the desired results of early detection and reduced costs, but to false-positive results (as measured by the diagnostic app and followed


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by unnecessary and costly medical consultations).

Monitoring apps exist in a huge variety. Like diagnostic apps, they often use different sensors built into mobile devices. Furthermore, they may analyze the signals sent from an implanted medical device such as a pacemaker. Monitoring apps often utilize complex algorithms to analyze the captured signals and, if required, generate alerts in case of severe results. Those alerts may be directly signaled to the patient or to healthcare practitioners who would have to respond promptly if needed, and set the course of treatment. Pacemakers, blood glucometers and insulin pumps are frequently used medical products that are linked to monitoring apps. Needless to say, those apps must meet strict certification requirements.

By contrast, the sensors embedded in smartphones or attachable wearables are often used for wellness purposes. A major application field is fitness, in which live data is used to track the performance of a user and to prepare detailed analyses about sport activities. In addition, monitoring apps provide information that helps the user to improve their performance in sport activities.

Chronic diseases represent an important field where treatment apps offer many benefits for patients, physicians, and payers. They empower patients to manage their diseases more effectively. A prominent application is the management of drug ingestion - for example, in chronic, multimorbid patients who have to take many different drugs. For this purpose, the app coordinates and schedules the proper intake of the medication at the right time, providing patients more security in regards their treatment.

Finally, health apps may also support the daily business of healthcare practitioners as well as healthcare administration. They can do so for example by offering information about the need for reimbursement of treatment services and medications, or by offering a platform to rate physicians; to make appointments, or to enable data exchange between healthcare practitioners.

Consequently, there are various case scenarios and

54 Gemeinsamer Bundesausschuss (G-BA): The G-BA is federal joint committee that represents all German health insurance companies as well as health-care practitioners.

segments for health apps. In many cases they fit not only one segment but offer features that support various necessities. For the existing and upcoming technological developments in this type of app, there is still a lack of clear and understandable guidelines for developers. For example, they should know in which segment to best market their app, and which further certifications are necessary to publish and promote it to patients, healthcare practitioners and payers.

1.4.3 Diffusion of Health Apps in Germany

Although there are about 100.000 health apps available in the relevant app marketplaces, only a small number of suppliers have established a large customer base. A conduit to accessing a vast number of potential customers is health insurance companies. Health insurance companies may act as distribution partners of health app suppliers for various reasons.

First, their business model and purpose offer direct access to a huge number of healthcare practitioners and patients, who may both be users of health apps. Consequently, health insurance companies may push emerging health apps into the market by using their communication channels. Second, health insurance companies may enlarge the legal framework for reimbursement of healthcare services. Typically, healthcare practitioners may invoice only those healthcare services that are included in a Germany-wide health service catalog, which is defined by the “Gemeinsamer Bundesausschuss” (G-BA)54. The extension of this reimbursement catalog is quite difficult. The G-BA adds only those services and treatments that have been proven to be clinically and economically effective. Obviously, due to financial constraints, healthcare practitioners only offer treatments to their patients that can be invoiced afterwards.

Since patients’ price sensitivity for healthcare services is high, free use for patients - and therefore reimbursement by health insurance companies - might be one of the most crucial criteria for establishing a huge customer base. Further studies comparing customer needs and related price sensitivity seems useful. Nevertheless, health


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insurance companies may set up contracts that enlarge the range of treatments that can be offered to patients and define additional reimbursement opportunities. Therefore, those contracts are effective instruments for introducing new innovative e-Health solutions such as health apps to the first healthcare market.

Finally, health insurance companies have additional but rather small budgets for offering new services and products to their insured customers. Unfortunately, those budgets are often exhausted by already existing services. Furthermore, they underlie very specific and unique use cases.

Consequently, health insurance companies represent an essential point of entry into the first healthcare market in Germany, which is associated with a large customer base and frequent revenues. Nevertheless, in order to act as distribution partners, health insurance companies need to be convinced of the business model of each respective health app. Therefore, the following requirements are crucial:

1. Clear settings and associated certifications 2. Evidence of clinical efficacy and economic

feasibility 3. Data security compliance

1.4.4 Clear Settings and Associated Certifications

Contracts to extend the service catalog of healthcare services and treatments are underlain by special legal constraints concerning the service that may be provided as well as the parties that can engage in such contracts. In order to initiate such a contract, health insurance companies would expect a clear presentation showing in which setting a potential health app would be used. That is why health app suppliers should include intended patient journeys, associated healthcare practitioners, and the applications of the health app. Does the patient use the health app itself or does a healthcare practitioner apply it for the treatment of patients? Will the health app function as a substitute for an existing service? Is it used within a hospital? What risks does it pose for patients or healthcare practitioners? Based on the answers to those

questions, the health app may be categorized into a health app segment (wellness app, medical app) and risk class (none, I, IIa, IIb, III) with associated certification requirements, as referenced earlier in this chapter. Those requirements must be fulfilled in order to be part of a new contract set up by a health insurance company.

1.4.5 Evidence of Clinical Efficacy and Economic Feasibility

Health app suppliers need to prove the clinical efficacy and economic feasibility of their products. In order to provide medical evidence, a comprehensive systematic review of literature should present all epidemiological studies, both experimental and observational, preformed to their health technology. While medical evidence is often provided by health app suppliers, those suppliers often fail to present business cases to health insurance companies. Although economic feasibility does not have a direct influence on a patient’s health, it is certainly as important for health insurance companies as medical feasibility. Therefore, economic evaluations that are focused on the perspective of a health insurance company are necessary. Usually, the intended outcome of such evaluations is either the demonstration of cost reduction compared to standard care, or the extent (units) of incremental efficacy that justifies a higher price. Correct economic evaluations must contain long-term analyses concerning a patient’s demand for healthcare services (e.g. inpatient & outpatient treatment, drugs) and other associated costs that would need to be reimbursed by health insurance companies. Furthermore, the potential for false-positive or false-negative results, and the app maker’s plan for dealing with such results, should be considered. Therefore, a full data analysis must be performed.

Consequently, in order to enter the first German healthcare market, a health app provider needs to state either that its product offers a cost reduction with better or least the same health outcomes compared to standard care, or that the incremental efficacy justifies the higher price of the product (for these cases some limitations apply).


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1.4.6 Data Security Compliance

Health apps may collect a huge amount and variety of data about their users. Although such data offer a huge potential for improvements in healthcare, they also pose risks for misuse. For example, app users might be concerned that their data is being transferred to third parties. For this reason, data security is one of the key requirements for attracting customers and avoiding legal or ethical consequences. Legal frameworks for data security arise from Data Protection Directive of the European Union (Directive 95/46/EC). In Germany, further legislation is in effect (BDSG, SGB X, TMG, TKG). Nevertheless, the legal framework depends heavily on the origin of the health app supplier. Consequently, further legislation may take precedence55. In order to avoid misuse of patient data, relevant measures include the anonymization of information as well as a sophisticated authorization for the use of a health app56. Customers may identify trusted health apps based on the existence of a data privacy statement, the access rights of an app, and whether it is up to date. Additional confidence may be assured with quality labels and certifications55.

1.4.7 Conclusion

There are about 100.000 different health apps available in app marketplaces. Only few of them are used by a large customer base. Health apps may be categorized as wellness or medical apps depending on their intended purpose in healthcare. They might support prevention, diagnosis or treatment of diseases, healthy lifestyle choices, or the administrative needs of healthcare practitioners. Especially those apps intended for diagnostics or those that are linked to medical products are categorized as medical apps, which require certain certifications as medical products.

Special contracts with health insurance companies represent an opportunity to acquire a large customer base and increased revenues. Therefore, health app suppliers

55 CHARISMHA-Study - Chancen und Risiken von Gesundheits-Apps. Available at: http://www.bmg.bund.de/fileadmin/dateien/Down-loads/A/App-Studie/CHARISMHA_gesamt_V.01.3-20160424.pdf (last accessed 06/09/2016) 56 Greenpaper on mobile health (“mHealth”) of the European Commission (2014). Available at: https://ec.europa.eu/digital-single-market/en/news/green-paper-mobile-health-mhealth (last accessed 13/10/2016)

need to demonstrate a clear understanding of the setting as well as related legal frameworks which are relevant for their apps. Furthermore, they should provide valid evidence for an app’s medical and economic evidence as well as their accordance with data security requirements.

Health apps offer a great potential to make healthcare more effective and efficient. Nevertheless, at least in Germany, most of the currently available health apps do not seem to offer sufficient value to decrease the price sensitivity of patients. Furthermore, clearly understandable guidelines about reimbursement possibilities and associated legal requirements as well as more medical and economic evidence could support and hasten the diffusion of health apps into the first healthcare market in Germany.


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1.5The Digitally Embedded

Patient: How Does the Patient of the Future Behave in Comparison to

the Current Patient?

1.5.1 The Changing Roles of Doctors and Patients

Traditionally, the patient role has rather been passive57 and the physician-patient relationship was predominantly paternalistic. It has been generally accepted that physicians have the knowledge and medical skills to decide what treatment is best for the patient58. Lay people and patients have had sparse access to sources of health information outside of physician-and/or pharmacist patient-consultations. Moreover, the possibilities of interacting with other patients by sharing personal experiences and treatment options has been limited to local patient support groups59.

In the past decades a growing demand for a more substantial involvement of patients in their own health care has evolved on behalf of the population, health care providers, and politicians. The former imbalance of power and information due to clinicians´ wealth of knowledge and the social gap between professionals and their patients58 is decreasing. Furthermore, physicians and pharmacists are no longer the prime counterpart for health-related issues60. These developments support the change from physician- to patient-centricity in medical care. To foster patient engagement and

57 Lupton, D. (1997). Consumerism, reflexivity and the medical encounter. Social Science & Medicine. Retrieved from http://www.sciencedirect.com/science/article/pii/S027795369600353X58 Brody, D. S. (1980). The patient’s role in clinical decision-making. Annals of Internal Medicine. http://doi.org/10.1059/0003-4819-93-5-71859 Lupton, D. (2013). The digitally engaged patient: Self-monitoring and self-care in the digital health era. Social Theory & Health, 11(3), 256–270. http://doi.org/10.1057/sth.2013.1060 Kulzer, B. (2015). Arzt-Patienten-Beziehung: Im digitalen Zeitalter grundlegend verändert. Dtsch Arztebl International, 112(43), [20]. JOUR. Retrieved from http://www.aerzteblatt.de/int/article.asp?id=17272261 Weinhold, I., Gastaldi, L., & Häckl, D. (2015). Challenges and Opportunities in Health Care Management. Challenges and Opportunities in Health Care Management, 307–318. http://doi.org/10.1007/978-3-319-12178-962 Fischer, S., & Soyez, K. (2015). Trick or Treat: Assessing Health 2.0 and Its Prospects for Patients, Providers and Society. In Challenges and Opportu-nities in Health Care Management (pp. 197–208). Cham: Springer International Publishing. http://doi.org/10.1007/978-3-319-12178-9_16

accomplish the sharing of decision making and responsibility, it is necessary to provide patients with adequate health literacy, support, and guidance, which is particularly enabled by the use of digital solutions61.

1.5.2 Digital Levers to Engage Patients in Health Care Processes

Newly developed information and communication technologies, along with mobile digital devices, have gained an increasing influence in all areas of life62. This progressive digitalization of everyday life including implementation into the healthcare system leads to an essential change in patients´ roles and the physician-patient relationship61.

The active engagement of patients in different health care processes is a core attribute of this patient-centricity. Innovative digital services mainly target:

• patient deliberation and support prior to medical encounters and/or during treatment

• increasing patients’ responsibility in managing their health, symptoms, treatment, and personal health data and/or


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1.5 The Digitally Embedded Patient

• facilitating patients’ access to and interactions within the health care system61

Apart from their intrinsic purposes, digital solutions support different health-care related activities, i.e.: knowledge management, data management, and organization and time management61. The following sub-paragraphs provide a short summary of different solutions and respective popular examples.

1.5.3 Digital Patient Deliberation and Support

The internet in particular offers enormous medical, healthcare, and treatment information, which is easily accessible for everyone at any time and provides great possibilities for patients to gain health-related knowledge62. In a ‘traditional’ way, digital content is offered uni-directionally by many stakeholders in the health system. The majority of these services are related to health education, and an increase in patients’ health literacy, i.e. the ‘skills in understanding and applying information about health issues’63. Health care providers as well as health insurers and federal institutions actively disseminate information and advice on healthy living and disease prevention via different web channels, e.g., their homepages or digital newsletters.

At the same time, patients actively search for and acquire relevant information via Web technologies such as search engines, social media, online support groups etc., becoming more empowered and confident in actively handling their health64. Prior to the medical encounter, many patients inform themselves about symptoms, potential diagnosis, and treatment options65, supported by online health information providers and services such as WebMDSymptomCheckers. Decision aid services such as Option Grids (see www.optiongrid.

63 Ishikawa, H., & Kiuchi, T. (2010). Health literacy and health communication. BioPsychoSocial Medicine, 4, 18. http://doi.org/10.1186/1751-0759-4-1864 Lo, B., & Parham, L. (2010). The Impact of Web 2 . 0 on the Relationship.65 Kivits, J. (2006). Informed patients and the internet: a mediated context for consultations with health professionals. Journal of Health Psychology, 11(2), 269–82. http://doi.org/10.1177/135910530606118666 Emmert, M., Sander, U., & Pisch, F. (2013). Eight questions about physician-rating websites: a systematic review. Journal of Medical Internet Re-search, 15(2), e24. http://doi.org/10.2196/jmir.236067 Van De Belt, T. H., Engelen, L. J. L. P. G., Berben, S. A. A., & Schoonhoven, L. (2010). Definition of health 2.0 and medicine 2.0: A systematic review. Journal of Medical Internet Research, 12(2), 1–14. http://doi.org/10.2196/jmir.1350

org) summarize and explain treatment alternatives for patients with preference-sensitive conditions such as breast or prostate cancer and support them in revealing and weighing their preferences. To find and choose a health care provider, many people seek information and advice by visiting review and rating sites. Illustratively, researchers estimated that in the US about 16% of aprox. 700,000 physicians had been rated by patients via RateMD (see www.ratemds.com) by 201066.

It is common among many of these more topical sources of health-related information that users or patients actively contribute to the production of information and content. So-called Web 2.0 technologies facilitate participation, social networking and interaction within and between different stakeholders such as healthcare consumers, patients, health care providers, researchers etc.67 Patients exchange their knowledge as well as their experience of symptoms, diagnoses, treatment, side effects, and health care providers in disease-specific or general social networks and online communities, forums and blogs – either mediated by medical professionals, in health chats, or among peers. This exchange among patients in particular often offers an access to alternative therapy options and can influence patients’ preferences in terms of treatment. The social networking platform PatientsLikeMe (see www.patientslikeme.com) is a successful example of this development.

1.5.4 Digital Solutions to Increase Patients’ Self-Responsibility in Managing their Health, Symptoms, Treatment, and Health Data

In recent years, a huge variety of digital services for monitoring and managing health data has emerged, allowing patients more control over their own health. The range of application areas seems to be almost


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unlimited, covering the employment of mobile devices and wearables or implanted monitoring sensors61. Patients are actively involved in managing their health by monitoring bodily functions and activities, recording medical data using apps and wearable devices. Most medical applications are developed and implemented for chronic conditions such as cardiac diseases, asthma, diabetes, and hypertension68. Depending on application and indication, the collected data is either interpreted by patients themselves or jointly evaluated in the medical encounter. The data is transferred to health care professionals (either automatically or by the patient) who receive a notification in case critical values are exceeded. Examples are numerous, ranging from fitness apps and diet trackers for practically everybody, to disease specific-applications such as digital diaries or prescription reminders - see Silva, Rodrigues, de la Torre Diez, Lopez-Coronado, & Saleem, 201569 for a list of the top applications in 2015.

An associated pillar of digitally-supported self-management covers the field of health data management. While the ‘traditional’ electronic health record is primarily used by health care providers in order to digitally process and save patient data, more topical solutions such as web-based personal health records, for example Microsoft HealthVault, can be used by patients themselves to access, store, manage, retrieve and exchange health data with their doctors and other relevant stakeholders within health care systems62, 70.

1.5.5 Digital Solutions to Facilitate Patients’ Interactions with the Healthcare System

Especially in larger health organizations like hospitals,

68 Wootton, R. (2012). Twenty years of telemedicine in chronic disease management--an evidence synthesis. Journal of Telemedicine and Telecare, 18(4), 211–20. http://doi.org/10.1258/jtt.2012.12021969 Silva, B. M. C., Rodrigues, J. J. P. C., de la Torre D??ez, I., L??pez-Coronado, M., & Saleem, K. (2015). Mobile-health: A review of current state in 2015. Journal of Biomedical Informatics, 56, 265–272. http://doi.org/10.1016/j.jbi.2015.06.00370 Steinbrook, R. (2008). Personally controlled online health data - The next big thing in medical care? New England Journal of Medicine, 358(16), 1653–1656. http://doi.org/10.1056/NEJMp080173671 Gastaldi, L., & Corso, M. (2012). Smart Healthcare Digitalization: Using ICT to Effectively Balance Exploration and Exploitation Within Hospitals. International Journal of Engineering Business Management, 1. http://doi.org/10.5772/5164372 for example, solutions for the dematerialization of clinical documents73 For example, EMRs or logistic process management solutions74 According to the company’s website information

progressive strategies focus on digital solutions that support interactions within the healthcare system, mainly targeting the efficient use of health care providers’ resources71. In many organizations, the digitalization process started with preliminary investments in digital infrastructures72, which laid the foundation for a more comprehensive digital internal integration73. Just recently, hospitals began to plan or implement digital services for external integration71, i.e. with direct applications for patients and options to interact. Digital service delivery, online booking systems for hospital visits, online payment for health care services, SMS reminders, or solutions providing information on clinical exams and on waiting times will increasingly support patients in their care coordination and increase efficiency and effectiveness of their interactions within the health care system71. For example, ZocDoc is a popular digital service that helps to find health care providers within zip code areas and make an appointment online (see www.zocdoc.com).

ICT-supported treatment and counseling services are important facilitators of access to specialized health care, which often falls short outside major population centers. By now, remote medical consultation is available in various fields, for example in mental health or cardiac care. Routine visits or counseling support can be made by videoconference with health professionals or via messaging (e.g. using email or online chats)71. Online services such as DrEd (see www.dred.com) offer online access to qualified health care providers and prescriptions for selected treatments for almost 2 million74 patients in the UK, Ireland, Germany, Austria, and Switzerland.


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1.5.6 The Current Digital Patient – Usage and Usage Barriers to Innovative Services

For years, digitalization in health care has focused mainly on processes and less on patient needs. This understanding, however, is supposed to be the base on which digital products and services should be built75. The need for and ability to process health-related information differs depending on sociodemographic characteristics, health status76, and the urgency of a health problem. Moreover, patients’ desire to engage in health care processes and to use supportive digital technology is moderated by personal and demographic characteristics: a large European study found that the overall use of digital health services is more frequent among the younger and higher educated populations, in students and the employed, in urban areas, and among people who are in bad health or have chronic conditions77, 78.

In general, the majority of patients, i.e. 75% as reported in the McKinsey Digital Patient Survey 201479, already used digital health services – mainly websites, online portals, or email interactions. Smartphone apps were used only occasionally by about one third of the respondents, and social media hardly played a role in the German and UK samples75. Italian surveys found similar trends – collaborative solutions such as health chats or social media are only valued by the younger age groups80. Considering the services for health and data management and interaction, a different picture emerges: considerably more than half of the European respondents have never bought medicine or vitamins online, participated in health-related social networks or support groups, or disclosed medical information on websites. More than 75 % have never participated in online consultations,

75 Biesdorf, S., & Niedermann, F. (2014). Healthcare’s digital future. Retrieved from http://www.mckinsey.com/industries/healthcare-systems-and-ser-vices/our-insights/healthcares-digital-future76 Sørensen, K., Pelikan, J. M., Röthlin, F., Ganahl, K., Slonska, Z., Doyle, G., … Helmut Brand. (2015). Health literacy in Europe: Comparative results of the European health literacy survey (HLS-EU). European Journal of Public Health, 25(6), 1053–1058. http://doi.org/10.1093/eurpub/ckv04377 Citizens of 14 European countries Citizens who have used the Internet in the last three months, stratified by country, gender and age groups, n=14.00078 Lupiañez, F., Maghiros, I., & Abadie, F. (2013). Citizens and ICT for Health in 14 European Countries: Results from an Online Panel. In European Commission / Joint Research Centre: Scientific and Policy Reports (pp. 1–166). http://doi.org/10.2791/8406279 The survey was conducted in Germany, the UK and Singapore, sample size was >1000 respondents of different age groups, genders, and incomes; and levels of digital familiarity80 Observatories ICT in Health. (2013). “Digital” Doesn’t Have to Be Left Only on the Agenda. School of Management of Politecnico di Milano, www.osservatori.net, in Italian.

never uploaded medical results to online repositories, never used health or wellness apps and never transferred vital signs or any medical data anywhere78. The main barriers to digital health service utilization are privacy and security for about 50% of respondents in the European study. People further pointed to concerns with respect to reliability and trust into the services. About one third mentioned shortcomings in terms of liability, health literacy, access to services, and digital skills. Older people, women, and those with lower education are especially sensitive to the usage barriers78.

It is, however, not accurate to conclude that people in general and older generations in particular do not want to use digital services – it seems to be type of service, not the channel, that matters most for them75. Almost 40% of the respondents in the European study used digital services to search for health- or disease-related information75. Among the digital services presented in the previous paragraph, those that facilitate access and interaction within the system are perceived as most relevant to patients in general80. Quite in line with these results, gains in efficiency thanks to eliminating processes or receiving services online and increased awareness of online services were found to be the top drivers of digital service adoption75.

In the course of increasing access to digital technologies, there is a shift of responsibility towards patients regarding their health. On the one hand, patients’ extended information-seeking can lead to a better understanding of the diagnosis, the therapy recommendations, or the mode of action of drugs, and can eventually help patients to accept more responsibility for their illness and become actively engaged in their health management60. On the


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other hand, being left on their own to choose and assess the content of web-based health information, patients have difficulties in differentiating between information that is based on scientific evidence, and incomplete, false or even manipulating contents60. This can lead to misinterpretations and insecurity81. Furthermore, patients already suffering from health anxiety are at risk of intensifying their affliction by searching for symptoms of unlikely diseases which they mistakenly feel60.

From the health professional’s perspective, the medical encounter with the well-informed patient is an ambivalent one. Admittedly, engaged and empowered patients are valued, particularly if they suffer from complex symptoms, but the medical encounter sometimes becomes more exhausting for physicians81. Misinformed patients may require additional time due to longer discussions, for example. Some physicians even feel like “being examined” and take personal offense from interactions with suspicious patients, while others regard pre-informed patients as a positive challenge81. Similar results were reported in a survey of 491 family and consulting physicians; about half of the participants considered that medical encounters with pre-informed patients were aggravating, mainly due to an increased time requirement82.

Patients feel highly vulnerable when they need health care57 and during times of illness. Being hospitalized or treated is associated with feelings of fear and lack of privacy or lack of control83. Under many circumstances, people who are incapable of acting as sovereign patients will hardly be able to question medical authorities, or may not wish to do so. Agreeing to self-surveillance and self-care can be overwhelming, and some patients do not want to be faced constantly with their disease57 or turn their homes

81 Baumgart, J. (2010). Ärzte und informierte Patienten: Ambivalentes Verhältnis. Dtsch Arztebl International, 107(51–52), A-2554-A-2556. JOUR. Retrieved from http://www.aerzteblatt.de/int/article.asp?id=7986282 Gerlof, H. (2014). Sand im Praxisgetriebe durch das Internet. CardioVasc, 14(2), 34–35. http://doi.org/10.1007/s15027-014-0353-683 Berry, L. L., Bendapudi, N., & Berry, L. L. (2007). A Fertile Field for Service Research. http://doi.org/10.1177/109467050730668284 Oudshoorn, N. (2011). Telecare Technologies and the Transformation of Healthcare. London: Palgrave Macmillan UK. http://doi.org/10.1057/978023034896785 Hortensius, J., Kars, M. C., Wierenga, W. S., Kleefstra, N., Bilo, H. J., van der Bijl, J. J., … Rutten, G. (2012). Perspectives of patients with type 1 or insulin-treated type 2 diabetes on self-monitoring of blood glucose: a qualitative study. BMC Public Health, 12(1), 167. http://doi.org/10.1186/1471-2458-12-167

into ‘clinics’84. Reactions to self-monitoring one’s vital functions or clinical parameters can be ambivalent: diabetes Type 2 as well as COPS patients for example reported feeling more secure and encouraged if their clinical parameters were acceptable, but felt ashamed, anxious, helpless and frustrated if they were not85.

Understanding what patients want from digital healthcare as well as their fears and concerns is a prerequisite to the successful diffusion of digital services. Healthcare organizations and digital service providers are advised to implement and add new services step by step and in accordance with their customers’ real needs: in order to keep their attention, do not overburden patients, and ensure that they become gradually accustomed to the digitalization.


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1.6 Future Developments

in Digital Health

Digital Health nowadays is still subject to a number of misunderstandings. To physicians, digitalization might mean sending patients reports via email rather than using a fax machine. To sick funds and health insurers, it might mean quicker reimbursements or data mining, and to the industry it might mean including wearable-generated data in phase III or phase IV studies, hoping to provide the right levels of evidence needed to sustain the authorities’ reimbursement requirements. The truth is that digitalization is none of this and all of it.

Digital health will live up to its full potential when it brings all the different stakeholders and their individually-suited solutions together into one grid of data exchange, which we commonly refer to as the ‘Internet of Healthy Things’ (IOHT). With legislators being ever clearer on the rights of patients to exercise ownership over their own data as described in this chapter, and predictive analytics stepping down from hype status to usable technology, we will see the rise of business models that combine these sets. This means we will be looking at Healthcare data brokers and data sharing cooperations that will act as trust centers or intermediaries on the sale of data to research organizations or industry with the data owner’s full consent and a price attached.

1.6.1 Outlook on the Pharmaceutical Industry with an Emphasis on Generics and Patent-Protected Products

To the pharmaceutical industry, digitalization is friend and foe alike. Common business models are being put in jeopardy through the employment of data-based solutions such as prescription overrulings according to the contract status of the individual patient. Physicians are currently already guided by software as to what medication they should be prescribing

to what patient. This changes sales models because it reduces the importance of the prescriber when decisions are taken centrally rather than at the doctor’s desk.

Pharmaceutical companies are also looking for technical support and digital solutions to integrate parts of the IOHT in their phase III and IV studies. The challenge is to achieve evidence-based medicine status with these technical devices in order to be able to integrate the data harvested there into randomized control trials or studies that bear significance for regulatory and reimbursement decisions.

The generic drug industry (Gx) in particular is experiencing more and more pressure, with prices declining and competition on the rise. These medications have become commodities and new KPIs for measuring success not only need to come in but need to bridge the way to an entirely new business model. This will consist of technological availability, patient access, and patient commodity, which poses the challenge to these companies to invest into new technology in markets that do not necessarily warrant economic risk-taking. We are looking at a person-centric therapeutic approach rather than maintaining the fragmented provision of medicines by individual companies, dispensed by the physician and paid for by sick funds or insurance companies.

Digitalization in healthcare opens the door to new business models that focus on the provision of care to communities or individuals with a technological follow-up that will monitor health-related behavior of individuals and generate longitudinal data on how medicines actually work. In the case of polypharmacy, this data will also include how medicines interact with one another.

But it would be short sighted for package sales not


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1.6 Future Developments in Digital Health

to adopt new technologies in old and financially unattractive markets. First of all, this technology and overall strategic approach can be used on other compounds, but also other markets. Second, new markets can be developed, taking into account that old players also need to move. The current ruling of the European Court that fixes pharmacy prices for medication is just the beginning of achieving full market transparency. This will bring retail pharmacists to the negotiating table with sick funds. We will also see the rise of mail-order pharmacies and, for some products, a ferocious competition in price. Losers will be the wholesalers who will need to give their margin to the pharmacists. This might result in the withdrawal of wholesalers from the market and might force the digitally illiterate or emergency prescription patients to look for other options. Mail order pharmacies with wholesaler licenses will be the winner in this model, but none of them will be able to do without digital solutions for logistical and patient administration.

In order to fully suit patients, the Gx industry will need adopt a patient-centric approach even more than the Rx industry. Since major Gx manufacturers have a plethora of molecules on their shelves, they will easily be able to supply 80% of a chronic patient’s medication. It is the proverbial “last mile” that needs to be on these suppliers’ minds; literally - the space between the blister and the patient’s mouth. Smart and easy-to-handle pill dispensing devices that do not require repackaging or reblistering (considered value chain obstacles) are in short supply. This is because pharmaceutical companies usually consider one of the patient’s conditions a priority for which they have a fix; but the truth is that most chronic patients have diverse co-morbidities that need ailing as well. So again we see inverse Taylorism, where the agent needs to

86 Ackerman, Spencer, and MacAskill, Ewen. “Privacy experts fear Donald Trump running global surveillance network“, The Guardian, 11 November 2016, https://www.theguardian.com/world/2016/nov/11/trump-surveillance-network-nsa-privacy87 Mastroianni, Brian. “Pokemon Go is catching personal data from your smartphone”, CbsNews.com, 11 July 2016, http://www.cbsnews.com/news/pokemon-go-is-catching-personal-data-from-your-smartphone/88 Martin, Taylor. “How to stop WhatsApp from sharing your data with Facebook”, CNet, 26 August 2016, https://www.cnet.com/how-to/how-to-stop-whatsapp-from-sharing-your-information-with-facebook/89 Solon, Olivia. “Google’s ad tracking is as creepy as Facebook’s. Here’s how to disable it”, The Guardian, 21 October 2016, https://www.theguardian.com/technology/2016/oct/21/how-to-disable-google-ad-tracking-gmail-youtube-browser-history

adapt to the producer and not vice versa. This will be a sea change that the industry needs to undergo, from package provider to full care provider. We will see this happen in the pharmacies of Europe in the next couple of years: those pharmacies will not survive unless they make personal special care for patients their strategic center and legacy as the main KPI that distinguishes them from cheaper mail-order pharmacies. Those who will not or who have not the size to negotiate with sick funds over individual deals for patients will have a hard time in a market that is mainly about saving money.

1.6.2 The Issue of Data Ownership: What Kind of New Business Models we will See in the Future? What are the Legal Issues?

One of the first questions to tackle when constructing a data system is the central issue of who owns the data itself. This question has recently become much more important as the collection and aggregation of data about individuals has increased drastically86.

As recently as 2016, companies have been in the news for collecting data that they probably shouldn’t87, aggregating data that was previously unconnected88 and for using data to make individuals more transparent89. All of these issues have only become possible because it has become feasible to actually collect and collate these data about individuals, while the legal framework that should govern collection and use of personal data has remained unchanged.

For medical data, that framework is pretty clear and uncompromising: every person has the right to decide who can collect, use and even know of the existence of


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data about them90. Naturally, there are situations where consent is assumed, but even these specific exceptions are strictly regulated91. As such, from a legal standpoint, it is clear that data can only be collected with explicit and specific consent by the respective person92.

With these provisions, Europe and specifically Germany have one of the strongest individual protection frameworks in the world. In fact, it has become sort of a trope already to mock German data protectionism93. While this makes some business models harder or impossible, it is a good thing, both for individuals and businesses. It is good for individuals because they retain sovereignty over their own data, which is an important personal right. It is, however, also good for business because all data carries an inherent risk with it, a fact that is often overlooked by collecting companies. All data is, sometimes quite literally, also a liability, especially so in case of loss, misuse, or breaches. While this may sound like science fiction, already identity thieves are stealing real property using data collected from multiple sources94. It is unclear which parties are liable in each scenario of data loss or breach. Loss and misuse of data can also carry severe risks in regard to public relations, as the image of a company that is careless with users’ personal data can be damaged seriously.

In any case, protection of data against unauthorized access on a technical level is paramount.

However, voices from the industry are now calling for changes in data protection laws, even going so far as to ponder whether data protection should

90 https://www.gesetze-im-internet.de/sgb_1/__35.html91 https://www.gesetze-im-internet.de/sgb_10/__67.html92 Note that this does not only apply to medical data but to all personally identifiable data [REF: datenschutzgesetz]. There are specific laws for medical data because of their sensitive nature as well as the long-standing practice of actually keeping records about patients.93 Hucal, Sarah. “Germany’s Cryptic Debate on Data and Privacy”, US News, 5 April 2016, http://www.usnews.com/news/best-countries/arti-cles/2016-04-05/germanys-cryptic-debate-on-data-and-privacy 94 Remo, Jessica. “Trio stole $1M in identity theft, mortgage fraud scheme, AG says”, nj.com, 1 September 2016. http://www.nj.com/union/index.ssf/2016/09/trio_stole_1m_in_identity_theft_mortgage_fraud_sch.html95 Etgeton, Stefan. “Wem gehören die Daten? Digitale Patientensouveränität im Stadium der Morgenröte”, Der Digitale Patient (blog), 27 July 2016. http://blog.der-digitale-patient.de/digitale-patientensouveraenitaet/96 “Wem gehören die Patientendaten? – Eine Auseinandersetzung mit den Thesen von Dr. Stefan Etgeton von der Bertelsmann Stiftung”, dieDaten-schützer Rhein Main, 31 July 2016. https://ddrm.de/wem-gehoeren-die-patientendaten/97 Schneider, Rainer. “Rabatte für Gesundheitsdaten: Was die deutschen Krankenversicherer planen”, ZDNet.com, 18 December 2014, http://www.zdnet.de/88214397/gesundheitsdaten-per-fitness-tracker-die-deutschen-krankenversicherer-planen/

be abolished completely95, even if at this stage only for pseudonymous data. Naturally, there is a heated and ongoing discussion about this96.

So, at this point the situation is both clear and unclear. From a legal perspective, all data about a person belongs to that person; copies must be made available to the owner and any use can be reviewed and vetoed by that person. What is unclear is how the legal situation will change in the future and how individuals will use their rights to control their own data.

Still, there are plenty of business models that are viable under these very restrictive laws, or even because of them. One obvious business angle could be to assist the user in managing their own data and acting as a trusted informant to the user. In a wider sense, this is the strategic angle for companies like Facebook, which help the user in managing their personal data in exchange for access to that data and become a “gatekeeper” to the user’s’ data.

Users can also usually be persuaded to give up their rights voluntarily in exchange for other benefits, such as tracking and aggregation or entertainment. Fitness trackers, which will be discussed in-depth in a later chapter, are a prime example of the benefit for users (being able to review and track their own fitness progress) apparently outweighing the risk of external surveillance and data collection. This data, while not directly medical, is at least medically relevant; in fact, German insurance companies have begun offering to incorporate fitness data into their insurance plans, offering rebates to active people97. At the same time,


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fitness and outdoor activity data has been successfully introduced in mapping applications, allowing more accurate and widespread mapping of walkways than would be possible with traditional methods98. This pattern is actually indicative of the business models that are made possible by collecting data, even if the data might only be tangential to the actual business. In fact, it has been shown time and again that often at the point in time when data is collected, it is unknown for which business use the data can be adopted.

As such, it is hard to point to specific business models that will become available with more data. On the one hand, big data, data collection, and data analysis will continue to make current business models more effective and efficient. On the other hand, once data is present, connections can be drawn that allow novel business models and structures; however, these depend highly on the attributes and quality of the data.

1.6.3 The Issue of Data Security and Data Safety : where are Data Being Stored and Secured? An IT Point of View

As we have indicated in previous chapters, data, once collected, is not just valuable but also a liability. Stories of data breaches and loss abound, including discussions of the reverberations for both the compromised businesses and the affected individuals. One thing is clear, however: loss of data due to breaches or negligence is going to be very costly99. Not only is stored data susceptible to hacking attempts and “inside connections”, but also, quite banally, to simple loss of the devices100. It is estimated that over 13 million people were affected by identity theft in 2015 in the United

98 Purdy, Kevin. “What Google is getting out of Ingress”, IT World, 18 April 2014. http://www.itworld.com/article/2698381/mobile/what-google-is-getting-out-of-ingress.html99 Watson, Willis Towers. “Cyber Claims Landscape: Companies Face Increasing Data Breach Liability”, Willis Towers Watson Wire (blog), 23 July 2015. http://blog.willis.com/2015/07/cyber-claims-landscape-companies-face-increasing-data-breach-liability/100 Nancy zz_Ferris. “NIH researcher loses laptop with data on 2,500 patients”, Healthcare IT News, 28 March 2004. http://www.healthcareitnews.com/news/nih-researcher-loses-laptop-data-2500-patients101 “Identity Theft and Cybercrime”, Insurance Information Institute, http://www.iii.org/fact-statistic/identity-theft-and-cybercrime102 “Identity Theft Resource Center Breach Report Hits Near Record High in 2015”, Identity Theft Resource Center. http://www.idtheftcenter.org/ITRC-Surveys-Studies/2015databreaches.html103 “Data at rest” means data that is not currently loaded into a program for analysis, such as while stored on a laptop computer, in cold storage, or in backups.104 “Specifications Overview”, FIDO Alliance, https://fidoalliance.org/specifications/overview/

States alone, at a total cost of over $15 billion101,102.

It is thus of outstanding importance to secure data against unauthorized access and loss. The approach most widely used is called defense in depth. It is a layering approach in which redundant levels of security are built around a central system, in this case the stored data. It ensures that there is no single point of failure. And even in the case of a security failure in a single layer, an attacker does usually not gain access to stored data right away, but instead has to surpass the other layers as well.

The first and most obvious step is to encrypt all data with strong keys and passwords while it is at rest103. There are full-disk encryption options for all operating systems today, and there is absolutely no reason why they shouldn’t be used on all computers in an organization handling data, including all employee’s computers. Since this is basic IT security nowadays it’s barely worth mentioning; however, many companies still do not publish such guidelines nor adhere to them. Requiring encryption on all devices minimizes risk from the simple loss of a device.

The second obvious point of protection against unauthorized access is to bolster user security with two-factor authentication. This minimizes the risks posed by weak passwords and password reuse by adding a second factor, usually a hardware token or a software on the user’s smartphone that is very hard to attack with traditional password-cracking methods. There are commercial off-the-shelf solutions available at very low cost, so there is very little reason to forgo the use of two-factor authentication104. But these are just the basic IT security


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mechanisms that any company should follow.

Regarding data security, in the specific context of patient and medical data, the first step that must be taken when storing data is to anonymize the data. This means that any association between stored data and an actual person should go through a high-security context and be stored in a physically different place or be encrypted with a different key, and only be combined at the last possible moment. This is to minimize the risk of exposure in the actual case of a breach, but also to minimize the chances of inadvertent disclosure during regular work routines. After all, if a data set cannot be readily tied to a person then a data breach is much less serious. Naturally, this has been standard operating procedure in scientific data collection for a long time now. Stronger anonymization is necessary when publishing data, such that no single person can be identified; however, it is very hard to completely secure a data set against de-anonymization, as examples have shown in the past, especially when it is possible to combine different data sources105.

Finally, moving unencrypted data away from central locations is becoming more common and adds further layers of protection to each individual data row, as well as a data set in total. This is because in the case of distributed encryption, for example on the user’s own device or on an authenticated “edge server”, the data is only readable in a specific context and for a short time, minimizing exposure risks. Such a situation is known as “zero-knowledge”; that is, the physical storage system of the data does not know what it actually stores. Data is only decrypted when it is used, and in a specific usage context. These contexts then also allow fine-grained control over what is allowed and what isn’t with each particular data row, simply by denying an access key to non-authorized contexts. Since in this case there is also no central registry of such keys, there is no single point of attack or failure, adding much greater resilience to such a system.

Unfortunately, while a few commercial operators already use this technology, it is not already available as an off-the-shelf solution. Most prominently, Apple implements

105 Schneier, Bruce. “Why ‘Anonymous’ Data Sometimes Isn’t”, Wired Magazine, 13 December 2007. http://archive.wired.com/politics/security/com-mentary/securitymatters/2007/12/securitymatters_1213

a zero-knowledge system for the data stored on iOS devices, where the encryption key never leaves the device and all operations are performed only at the user’s request.

However, the security of an implementation has to be evaluated on a case-by-case basis, with respect to the needs of a project. In many cases, security that goes as deep as zero-knowledge systems will not be possible, or will only be possible with restrictions of the service.


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Chapter 2:Special focus on Big Data

Potential Assessment and Exploitation in

Healthcare


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2.1Current State-of-the-Art and

Application Examples

All models are wrong, but some are useful. - George Box106 2.1.1 Introduction

For a long time, artificial intelligence research was not taken seriously, because it had failed to produce results that were expected in the 50’s and 60’s. It made bold statements about what it would be able to do, but delivered next to nothing. What followed was a time period called the “AI winter” where funding of research projects was discontinued and the whole field went from being mainstream and interesting to exotic and suspicious.

Ironically, the methods used in the eighties were strikingly similar to the ones which are enormously successful nowadays. Geoffrey Hinton, who is a pioneer in the field of artificial neural networks and co-published the first paper on the back propagation algorithm for training multilayer perception networks, answered the question of why they didn’t work back then (1986):

We all drew the wrong conclusions about why it failed. The real reasons were:

1. Our labeled datasets were thousands of times too small

2. Our computers were a million times too slow 3. We initialized the weights in a stupid way 4. We used the wrong type of nonlinearity In the mid nineties, interest started to rise again, with the first success stories in the field of data mining and with new models like support vector machines. It became fashionable to call the field machine learning, because on the one hand the term artificial intelligence was burnt

106 “All models are wrong - Wikipedia, the free encyclopedia.” 2014. 13 Sep. 2016 https://en.wikipedia.org/wiki/All_models_are_wrong107 “Speech Is 3x Faster than Typing for English and ...” - Stanford HCI Group. 2016. 15 Sep. 2016 http://hci.stanford.edu/research/speech/

out and on the other hand the goals had to be set much lower to avoid getting trapped by inflated expectations again. The goal switched from “we construct human-like robots” to “how about software which gets better with experience”. In the good old days of artificial intelligence optimism, it wasn’t quite clear that software had to have the ability to learn at all. If you can deduct all the things you need to know from a few principles using first order logic, then you don’t need to learn. Unfortunately it turned out that we live in a universe where you can’t.

This rise of machine learning continued far into the 00’s. Then something else happened: the internet became more and more widespread. While surfing the internet, people generated lots of data that wasn’t available before. For example, in the late nineties the problem of speech recognition seemed to be nearly solved. I remember being told that we would all talk to our computers, because in three to five years the recognition error would be low enough to replace the standard keyboard interface for text editing. This didn’t happen. But today speaking into your phone really is faster than typing107 (interestingly most people are not yet aware of this). What happened? It turned out that in order to be useful the error rate has to be lower than a threshold, 2% for example, and while it was relatively easy to bring down this error rate from 10% to 5%, which was done in the nineties with machine learning models like hidden markov and gaussian mixture models, it was exponentially harder to bring the error rate down from 5% to 2%. And to be able to do so you would need a lot of training data which was not available in the nineties. But today there really are a lot of people talking into their phones, and we have plenty of training data.


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2.1 Current State-of-the-Art and Application Examples

This change in the availability of data has pushed many problems from the state of “promising but not useful yet” to “it works”. Having lots of data has another advantage : if you only have a small amount of data, you often need to extract handcrafted features to get machine learning to work. In fact most of the work you have to do in the fields of image or speech recognition is feature engineering. This has changed with more data because now it’s possible to do representation learning on that data, which means learning the feature extraction from the data itself. This new possibility together with new models which could make use of interactions between those features termed deep learning, and is a huge success.

With deep learning we have already seen significant improvements of the state of the art in image and object recognition, speech recognition and natural language processing. Even things like learning to play video games from video signal or beating humans in Go have only been possible by using deep reinforcement learning. Encouraged by those achievements, some people are courageous enough to speak of artificial intelligence again.

2.1.2 Big Data

Big data is often sold as a silver bullet. It’s not. For example, if someone tries to sell you a big data system, because it will overcome the data silos in your company, they are playing a game. There have been countless attempts to solve social problems with technology, but did this ever work out well? For the technology salesman it probably did. For the others, probably not.

The job of marketing is sometimes to find new and fancy words for old concepts, and since the term “big data” is really hot, all kind of stuff is now called big data. But it’s not all marketing fluff; there really are new use cases where the data you have is too large or complex for traditional data processing applications, and then you have to do something new. But how do you know when you have arrived at that point? In the following sections I want to elaborate a little bit on this question.

108 “The Reinhart-Rogoff error – or how not to Excel at economics.” 2013. 15 Sep. 2016 http://theconversation.com/the-reinhart-rogoff-error-or-how-not-to-excel-at-economics-13646109 “Large Scale Non-Linear Learning (Pygotham 2015) - SSSSLIDE.” 2015. 13 Sep. 2016 http://sssslide.com/speakerdeck.com/amueller/large-scale-non-linear-learning-pygotham-2015

Different people have different views on what big data means. Some people call all data which is too big to fit in an excel sheet big data. From a software engineering perspective it was never a good idea to implement serious logic in a spreadsheet, simply because you can’t apply the best practices that we’ve developed over the last decades in the software engineering field. You can’t write unit tests for your Excel code, for example. It’s very hard to do proper version control, too, and therefore to work as a team on a spreadsheet. Nevertheless, people have used Excel for all kinds of strange things and got away with it far too often. Sometimes it hasn’t ended well108.

For us software engineers it’s therefore kind of a late gratification to see a new trend in finance: many banks are replacing Excel with ipython notebooks. The New York Python user group has about 10,000 members and they organize weekly events with hundreds of attendees. And although it’s certainly easier to process bigger data in ipython notebooks than in Excel, this wouldn’t be the greatest advantage they’ll get from this change. The biggest advantage will be that despite its being a little bit unglamorous, they will have access to the abundance of boring but working libraries and will be able to fully exercise their programming craft in a professional manner.

Using a proper database to store data or applying time-tested best practices for software development is nothing new. This is traditional data processing, but of course, if you don’t do it yet you should start doing it now. And you’re absolutely allowed to call traditional boring data processing big data if it helps you to get it funded.

Scenario 1: Your Data Fits in RAM on a Single Machine

256GB ought to be enough for anybody (for machine learning) - Andreas Mueller, scikit-learn core-dev109

This is the sweet spot you are looking for. Computers can have lots of RAM these days and if you are working with biggish data, they should. Running a machine learning system is not that different from running a database.


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Both are easy to scale up but hard to scale out. Once I attended a database performance class held by Kristian Köhntopp (working as a principal consultant for mysql at that time) in which he told us half jokingly the three secret rules of database performance optimization:

1. Ok, you’ve got performance issues - let’s do the obvious first thing to do and add more RAM to the database server.

2. Well, things are getting harder, but maybe it’s possible to overcome even this by adding some more RAM to the database server.

3. This is tough, you’ve tried everything and it didn’t work and you’re running out of options, but there’s a last thing you could try before giving up: add more RAM to the database server.

If you don’t want to run a database but rather a machine learning system, the same advice still holds true. If your data doesn’t fit in RAM on a single machine, hard drive access will inevitably become your performance bottleneck, or even worse, network latency if you distribute the data over multiple machines. With SSDs things are getting better, but the fundamental problem remains the same. It’s often possible to stay below the “fits in RAM” boundary via subsampling. You should only move beyond it if you really know your problem well and that it would be beneficial to do so.

Scenario 2: Your Data Does not fit in RAM but on Disk in a Single Machine

There’s a special class of “out of core” or online learning algorithms that don’t need to hold the complete training set in RAM. They work by incrementally looking at a part of the data and train just on that part. Being able to do fast retraining if there’s new training data without having to batch retrain on the complete training set is another benefit of those algorithms.

One of the most widely used software packages for fast online learning is vowpal wabbit110. If you have data where a linear model works well - textual data for

110 “Vowpal Wabbit (Fast Learning) - Machine Learning (Theory).” 2007. 15 Sep. 2016 http://hunch.net/~vw/111 Rowstron, Antony et al. “Nobody ever got fired for using Hadoop on a cluster.” Proceedings of the 1st International Workshop on Hot Topics in Cloud Data Processing 10 Apr. 2012: 2.

example - you should definitely give vowpal wabbit a try before distributing your data across a cluster of machines.

Scenario 3: Your Data Doesn’t fit on a Single Machine

“Nobody ever got fired for using Hadoop on a cluster”111

In my opinion, this is the point where it starts to makes sense to speak of big data, because now you’ll have to substantially change the way you work with data.

Usually you move the data you want to process from the place it’s stored to another place where you actually process it. For example - you might do a sql query on a database and analyze the returned data in Excel. If the data you want to analyze becomes big enough, you can’t do that anymore, because it won’t fit on your computer. And maybe it’s not an sql database server you query for the data, but a storage system where the data is distributed over multiple continents and you can’t move the data, because you neither have the bandwidth nor a central place with enough storage capacity to do so.

A possible solution for this problem is to invert the usual workflow and move your algorithms to the data. Google has developed the most popular paradigm in this domain called Map/Reduce in 2004. There were similar concepts in the high performance computing community before, but nothing applicable to a cluster of commodity machines. The most popular method of scaling out data processing today is to use apache spark. You’ll write your code in Python, R (the two dominating languages in the data science field today), Scala, or Java and it’ll get automatically distributed and executed on your data. But this sounds easier than it actually is. Changing the way data is processed is a difficult thing to do. For a lot of algorithms there exist no efficient distributed versions. You’ll have to rewrite your code in a way that makes it distributable and the constraints you’ll face will be very different from the ones you optimized for on a single machine. There’s a lot less infrastructure you can build upon, so you’ll have to pay premium for


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commercial vendors or have to roll your own. It’s easy to predict that this will be expensive and really hard to do.

Different Kinds of Data

Big Data is a vague term, used loosely, if often, these days. But put simply, the catchall phrase means three things. First, it is a bundle of

technologies. Second, it is a potential revolution in measurement. And third, it is a point of view, or philosophy, about how decisions will be -

and perhaps should be - made in the future. -- Steve Lohr, The New York Times 112

Data that is too big to process it on a single machine is not the only case where traditional data processing breaks down. One of the largest publicly available datasets is image-net113. This dataset consists of about 15 million hand labeled images. The subset that is used for the Large Scale Visual Recognition Challenge 2016114 has 1000 categories and a size of about 140Gb, so it’s not really necessary to distribute it over a cluster of machines. But you have to use special hardware to build the deep network models which are now state of the art in image recognition. Usually that would be a normal computer packed with GPU extension cards. And yes, you can bring down the training time even more if you distribute your training run over more than one of those machines. But your standard off-the-shelf scale-out hadoop-cluster, which is typically sold as “big data system”, will be not of much use for this kind of problem.

The Square Kilometre Array telescope115 is predicted to generate one exabyte of data per day116. This is a thousand petabytes, which are a thousand terabytes each. You’d need a nuclear power plant to have enough power to process this kind of data in a traditional way. Therefore you probably don’t want to store all the data, but filter out all the noise before you store the signal. The detectors of the big particle accelerators do the same and have a stack of hardware ASICs that try to reduce

112 “Sizing Up Big Data, Broadening Beyond the Internet - The New York ...” 2013. 13 Sep. 2016 http://bits.blogs.nytimes.com/2013/06/19/siz-ing-up-big-data-broadening-beyond-the-internet/113 “ImageNet.” 2007. 15 Sep. 2016 http://image-net.org/114 “Large Scale Visual Recognition Challenge 2016 - ImageNet.” 2016. 15 Sep. 2016 http://image-net.org/challenges/LSVRC/2016/115 “The Square Kilometre Array SKA Home.” 2011. 15 Sep. 2016 https://www.skatelescope.org/116 “The Informatics Institute at the University of Amsterdam invites ...” 2014. 15 Sep. 2016 https://plus.google.com/106192624796075357023/posts/BBfCqMfjxWd117 “Statistical Learning with Big Data - Stanford University.” 2015. 13 Sep. 2016 http://web.stanford.edu/~hastie/TALKS/SLBD_new.pdf

the data stream those detectors create. Processing this kind of data is quite different from most other use cases.

It’s also very important to know whether your big data is tall (billions of rows/observations) or wide (billions of columns/predictors). Click Logs are an example of tall data where you have just a few predictors per click, but a lot of clicks in total. Since most of the ads which are shown to users are not clicked on but the interesting observations happen when users click on an ad, it didn’t hurt the performance to only take every 15th ‘non-clicked’ event. So subsampling was a very effective strategy in this case117. Wide data like brain activity scans or genome data usually consists of only a few observations, brains scanned, genomes sequenced; but contains a lot of data per observation. Wide data requires a completely different approach (dimension reduction, strong regularization, lasso)117

Conclusion There are valid cases for non-traditional data processing, but there’s no silver bullet and therefore no “one size fits all” big data system. Handling data correctly depends heavily on the use case, and the hardware requirements are very different for different use cases.


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2.2Deep Learning

Deep Learning is much easier to define. It’s about learning representations of data with multiple levels of abstraction118. This is usually done by training a neural network with multiple layers of neurons using the backpropagation algorithm.

Traditionally, much of the effort in building machine learning systems went into finding the right representation of the data. This process is called feature engineering, and transforming the raw data into the right representation is called feature extraction. For example, this is a (greatly simplified) process to transform a text into a feature vector that can be used later as a training example in a dataset: 1. Split the text into words 2. Assign a unique number to each word and

interpret this number as the dimension of this word

3. Initialize a vector containing all possible words or dimensions with all entries set to 0

4. For each word, set the value of the previously initialized vector at the respective dimension to 1

118 LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep learning.” Nature 521.7553 (2015): 436-444119 “Extract Data Conference | SlideShare.” 2015. 13 Sep. 2016 http://www.slideshare.net/ExtractConf

Now you have a vector representing the text. If you like fancy names you can call this representation a “unigram bag of words model with binary weights”. It works surprisingly well, because much of the meaning of a text is defined by the words it consists of. For other kinds of data, feature engineering is much more difficult. How do you split up an image into words?

Deep learning systems aim to learn the right representation from the raw data itself in an unsupervised manner. It gets even better: previously, one had to use the same representation for all kinds of images, because you couldn’t invent a new clever feature extraction mechanism for each new dataset. But with deep learning, the representation is automatically tailored to the dataset it was learned on. And the more data you have, the better the representation gets.

This chart by Andrew Ng sums it up neatly119:


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2.2 Deep Learning

Deep Learning works best in domains where it’s difficult to come up with good feature extraction methods like image or speech recognition. However, if a particular neural net architecture works great in one domain, like convolutional neural nets for image recognition, that doesn’t mean that this architecture would work as well in a different domain without modification. For sequence-based data like text, other architectures like LSTMs or RNNs seem to work better. And even though learned representations like word2vec or GloVe play an important role in natural language processing, those feature learning systems don’t use deep learning, but a instead a very shallow neural net.

There is another drawback to deep learning. Deep learning models sometimes have hundreds of millions of parameters that have to be fitted. Though it’s now possible to make use of more data than before, deep learning won’t work if the amount of data is too small.

Surprisingly, Georg Dahl, who worked on deep learning models for speech recognition in the same group as Geoffrey Hinton, tried to use deep learning in the Merck Molecular Activity Challenge120 especially because the data was so small. He was interested in whether it would work despite not having enough data. It worked and his team won the challenge121.

How to Start

It seems that every approach has its advantages and disadvantages. You could buy a hadoop cluster from your friendly big data salesman, but later it turns out that you could have done all the predictive analytics you need on a single laptop. Or that a GPU cluster would have been better. As long as you don’t know your use-case and the kind of data you’ll have to deal with, you will know neither what hardware to buy nor what kind of models you should develop. But how to start then?

120 “Merck Molecular Activity Challenge | Kaggle.” 2012. 15 Sep. 2016 https://www.kaggle.com/c/MerckActivity121 Dahl, George E, Navdeep Jaitly, and Ruslan Salakhutdinov. “Multi-task neural networks for QSAR predictions.” arXiv preprint arXiv:1406.1231 (2014).122 “Exploratory data analysis.” 2014. 13 Sep. 2016 https://www.stat.berkeley.edu/~brill/Papers/EDA11.doc

Exploratory Data Analysis

Exploratory data analysis” is an attitude, a state of flexibility, a willingness to look for those things that we believe are not there, as well

as those we believe to be there - John Tukey122

This is usually the first step. You take a look at the data you have and try to find some interesting properties. Visualization will probably play an important role and the “exploratory” aspect means that the problem you are trying to solve might change as you go. Usually you won’t look at complete datasets, but only at representative samples, to avoid running into the engineering difficulties caused by data size.

Eventually you’ll find some promising patterns in your data and proceed to the next step.

Predictive Analysis Start simple. First try to find out how hard the problem actually is. What simple means depends on the structure of your inputs and desired outputs. For example, if you find out that logistic regression (an off-the-shelf classification algorithm) does not work because there are too many nonlinearities in your data, you could try to employ nonlinear models or get better at feature engineering. But at first it’s important to know if your problem requires creativity.

Scaling

If your approach then needs to be scaled, the advice is to rent before you buy. This is especially true in an area that is known to have undergone major leaps in technology such as the availability of storage space and the speed of CPUs. Renting contracts also often times entail maintenance for servers and software that make sure you always have the current security patches installed and server uptime is within acceptable range.


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2.2 Deep Learning

Data Annotation / Cleaning

The importance of good data annotation and cleaning tools is often overlooked. In practice it’s often preferable to have good editorial frontends over having a slightly better performing model, because it is relatively easy to improve the performance of your system by adding annotated data, but difficult to improve it by building a better model. It’s also difficult to handle changes in your data over time by adapting the model. Simply adding new annotations to the change data is much easier. It’s also easier to find more people to operate your editorial tools than to find new data scientists to work on the models.

Data scientists spend up to 80% of their time cleaning up messy data, usually because there are no editorial tools and no people to operate them. In the end, somebody has to do it. If you only have the budget to hire two people, the second one after the data scientist should be someone good at building frontends.

Performance Measures You need to be able to assess how well your methods are working. Ideally, the performance measure you choose is aligned with some business value. From a technical perspective, let’s consider the simplest possible example: binary classification.

Binary Classification We understand this type of problem thanks to an annoyance we are all too familiar with: spam. In the beginning of the 00’s, the spam problem became significant enough that someone brilliant began to think about a solution: Paul Graham123. Graham suggested using a very simple machine learning model called naive Bayes to extract just one bit of information from every received email. Is this Spam? True or false? Despite the simplicity, this model worked well enough to be an effective measure against spam.

But how are we able to measure how well it works? Maybe we can measure its accuracy and claim “our

123 A Plan for Spam - Paul Graham.” 2005. 15 Sep. 2016 http://www.paulgraham.com/spam.html

spam filter recognizes spam with an accuracy of 90%!”. We pat ourselves on the back and consider the job done - until someone comes along claiming that his spam filter has an accuracy of 95%. And it’s a lot faster, too, because all it does is return always True, regardless of whether the mail considered is really spam or not. Surprisingly, he would be right, because more than 95% of all mail is spam. But since an obviously useless classifier which always returns True has a better score, our performance measure seems to be useless, too. In other cases accuracy might be useful, but in cases with imbalanced class counts it doesn’t do well.

So, measuring the quality of a model really depends on your data and your objective. For the spam filter problem, precision and recall might be more useful. Precision is the number of true positives divided by the sum of true positives and false positives. Recall is the number of true positives divided by the sum of true positives and false negatives. We are interested in all mail which are not spam. In practice the output of our classifier is not a binary true or false - it would instead be a score or a probability. Then we can establish a threshold and say that we consider all mail having a score greater than this threshold as not spam. From there, we can calculate the values of precision and recall for each threshold. If we sampled enough of these recall/precision pairs, they would form a line - a so-called precision-recall curve. This curve is a much better indicator of the performance of our classifier.


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2.2 Deep Learning

Cross Validation Being able to calculate appropriate performance measures for data where we know how the output of our model should look is all fine and well, but what we are really interested in is how our model will perform on data we haven’t seen before. The reason we do predictive analytics is precisely because we are interested in predicting things we don’t know yet. The good news is that most of the time we are able to find a good approximation of how well our model will perform on unknown data by using the data we already have. We just have to replace unknown with unseen.

Let’s assume we have a dataset consisting of emails which were manually annotated to be spam or not spam. We could then split this data set randomly into three parts. First we train our model on two of those three parts and calculate the value of our performance measure on the last, unseen part. Then we train on the second and the last part and test on the first part. On the last run, we train on the first and last part and test on the second part. Now we have three performance measures that we can combine by simply averaging them. This gives us a single performance measure depending on the classification result of each email in the dataset, but classified by a model which has never seen the emails it was tested on before.

In practice, it’s not often that easy to find a valid cross-validation strategy, and trying to find one is probably one of the first things you want to do. If the observations in your data set have a timestamp and you are interested in predicting future observations, then you can’t simply split the data randomly into train and test sets. It’s often more helpful to select a holdout set which has the same properties as the unknown data you are interested in - the last two weeks of a time series data set for example - and then compare your cross validation score against the score you get on your holdout set. If changes in your cross validation score lead to similar changes in the holdout score, you know your cross validation strategy is probably valid. If your holdout scores go down while your cross validation

124 https://www.kaggle.com/

scores go up or vice versa, you are probably overfitting.

Machine Learning Competitions

If you are interested in the current state of the art, take a look at Kaggle124. It’s a startup that hosts machine learning competitions for its customers. Running a competition on Kaggle with your data is a great way to see if it’s possible to improve above your baseline. In almost all competitions, significant improvements over the current state of the art were achieved.

Text Categorization Example from the Ecommerce Industry (use case 1)

In the sections about binary classification and performance evaluation, I used spam filtering as an example. Let’s proceed to a more complex example.

Let’s say you run an online shop selling summer houses. You can’t sell hundreds of different types of summer houses, because the storage costs would kill you, so you are stuck with just a handful of different summer houses to sell. How do you go about marketing them? Well, nowadays you might just bid on Google Adwords like anybody else. At first this seems great, bidding on “summer house” provides you with a lot of traffic from Google. But after some time you notice that the users you bought from Google are not buying as many summer houses per thousand visits as your usual customers. You take a look into your weblogs and notice that most of the users from Google look at the selection of your summer houses and then go away. Well, maybe they weren’t satisfied with the choices they saw on your site. They searched for “summer house” and then had to take one of the five similar looking summer houses on your site. Ok, maybe in the end it’s not a good idea to bid on the keyword “summer house” if you can’t offer a wide range of varieties that cover most of the requirements of customers searching for “summer house”.

But you still want to be able to market your products, right? This is exactly where shopping aggregator


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2.2 Deep Learning

sites come into play. Price comparison engines are a prominent example of this kind of website. They aggregate all the product feeds from different shops and import them into their own taxonomy of products. Price comparison sites often list millions of products on their site and probably thousands of summer houses. If those aggregators manage to correctly identify all summer houses in the product feeds and have a category landing page listing all the summer houses, they can effectively bid on generic keywords like “summer house” because the probability of showing a suitable summer house after a user clicks on the ad is much higher.

But by having millions of products and thousands of categories, the integration of a new shop with a few thousand products becomes difficult. The aggregator site could pay people to annotate all the products in this new feed with the right category, but this would be expensive manual labor. And what if the shop later adds changes to the feed? Then this manual work has to be repeated.

It’s clear that you have to automate the integration of new or changed products into the taxonomy. The first idea we had was to build a mapping between the taxonomy the shops used and our taxonomy, and then use this mapping to categorize the products in the product feed of the shop. But this didn’t work. The problem was that this mapping was brittle, and small mistakes could lead to many misclassified products which had direct financial consequences. Additionally, this mapping meant our own taxonomy hard to change, and the people in marketing who wanted to introduce a new wading pool category in summer had no idea how to change the mapping rules since they weren’t programmers. In short, solving this problem with a set of fixed rules or code was not possible.

What follows is our solution: at first we built editorial frontends in order to annotate products with categories from our taxonomy. You could do a fulltext search on all products in our databases or pick products by the category they were given by their shop or by the shop itself. You could formulate queries like “all products from shop X having the shop-category Y and having a price between $30 and $100”. This is quite similar to the interface customers see when they search for a product on a price comparison site. Having got a result

for your query, you could do things like “add category ‘summer houses’ from our taxonomy to all products in this result set”. This seems to be quite similar to the rule base approach we discussed before, but it’s actually very different. There’s no rule stored saying “if a product is in shop X and has shop-category Y and has a price in the following range, add it to category Z in our taxonomy”. It’s just a bunch of products tagged with the information that they belong to category Z in our taxonomy.

If you collected few hundred or thousand products for your category Z, you could press a button in the editorial frontend and it would build a model for category Z with the new training data you created. This works exactly as if you had been creating a spam filter - the only difference is that it doesn’t filter spam, it filters category Z. Having a model that knows the difference between products of the category Z and all other products, you now could hit another button in the front end that says “classify all the products in our database as being elements of category Z or not”. Then you could choose those products where the model was unsure and classify them manually, or refine further and annotate more products as belonging into category Z.

After some iterations, a technical editor would check the precision-recall curve for category Z and decide if it looked good enough to generate a threshold for the model score, at which point products which scored above this threshold would be automatically classified as belonging to category Z. If the curve didn’t look good enough, the editor would have to gather more training data. After a little bit of practice an editor should be able to train a few categories per day to be handled automatically. The number of generated training samples was about 20-30K per day per editor.

We now had a system where people without coding skills could add, merge or change categories and ensure the percentage of false positives per category would stay below a tolerable threshold (5% for example). If a new shop had to be integrated, nothing had to be done; it just worked automatically. If an old shop changed its product feed, the changes in category assignment would also be done automatically. Despite this whole system being based on simple binary classification, it could handle 60 million products in over 2.5K categories. We had about


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2.2 Deep Learning

11 million products labeled, and one product could have an arbitrary number of categories it belonged to.

To classify a product, we needed to calculate one score per category and model - i.e. 2500 scores. That seems to be a lot, but since the classification operation is just a scalar product, this is just 2500 scalar products which is not that much on a modern CPU. If you used the categorization service by hand and typed in a few words to describe something you were seeing at that moment, then the system would return a list of probable categories which all seemed to be a good fit. It really felt like a kind of magic. But it wasn’t magic - if you learned from 11 million products, you would have seen almost every word a few times and would know its probability of belonging to a category.

These factors were critical for the success of the project:

• Good editorial frontends led to more training data being incorporated faster, which was better than complicated models

• Fast model training time - it’s much easier to collect training data if you can try out the knowledge you’ve generated in an interactive manner. On adding or removing some products from a category we did an immediate retrain/reclassify cycle to show the effects of the last editorial action. This was possible because we were able to use online learning. Batch retraining time with all products from a category was about a few minutes.

• We did some tricks to speed up training time (memory mapping the feature matrix)

• It’s not a problem to use Python if you use it for the parts where speed doesn’t matter and implement the hot inner loops in C

• We had our own mapreduce framework to create the feature matrix

One possible application in the field of healthcare is to extract information from textual data - for example, a binary classification based on doctors’ notes showing whether a patient has had a heart attack.

Clustering of Products for Price Comparison (Use Case 2)

Any price comparison site’s main purpose is to show its users the best price for the product they are interested in. Now you might think this is a trivial task: just group your database of products by EAN/UPC or any other unique identifier, and you are done. Well, that’s easier said than done. The use of identifiers in your product feed is not free of charge. Therefore, shops which are competing hard on price can’t afford to use them. For our database the percentage of products for which we had unique identifiers was about 30%.

If the problem is finding an unknown unique identifier for a product, maybe we can solve this in the same way as the categorization problem. We group the products for which we have unique identifiers by them and train a model for each group of products with the same identifier. Then we classify a product for which the identifier is unknown with each of the identifier models and find out which identifier this product has.

This sounds great, but it won’t work. We are not talking about thousands of categories anymore. There are over a million different unique identifiers in our database. So, we would have to build a million models. This would be impractical, but even worse, we don’t have that much training data for each identifier - just a few examples per EAN. That’s far too few to train any kind of model on, and those groups of products are not as stable as categories. New products appear and old ones disappear into oblivion all the time.

So what can we do if we have no annotated training data from which we could learn? We could do clustering or “unsupervised learning”, as it is often called. Conceptually, clustering seems to be simple. All you need to have are some observations you want to cluster and a distance function to measure how close each pair of observations is. In practice, clustering is far from being simple, and making effective use of unlabeled data is one of the big unsolved problems in the field of data science.


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2.2 Deep Learning

What happens if we apply clustering to our problem of finding clusters of products that belong to the same identifier? The parameters we can choose are the distance function and the clustering algorithm. Let’s take tfidf-cosine similarity as the distance function. This is the same function used by full-text search engines to determine the similarity between a query given by a user and the documents in the result set. So what we are doing is taking each product as a query and looking at the distance from all other products as we would see it in a full-text search engine. Then we cluster the set of products using k-means, which is one of the most popular clustering methods due to its speed and correctness. If you try this method on the set of all products you’ll see that indeed the products would be clustered in groups which are somewhat similar, but those clusters would have no semantic meaning. If you use k-means, you have to set the number of clusters you want beforehand. If you choose a low number, you get clusters that look like broad categories of products, whereas if you choose a high number of clusters you get a lot of smaller clusters which are really similar textually, but are often products from the same shop, because

the name and description texts of the products of one shop are often generated by some kind of template. Neither result is we are looking for. The problem with clustering is that there are a lot of possible clusters of products which are completely valid, but in which we are not interested. We need a highly accurate method of finding products that belong to the same identifier, because comparing prices of products having different identifiers (for example, an iPhone and an iPhone screen protector), does not make sense. A website user looking for the best price for an iPhone would feel tricked, and the shop offering the screen protector would have to pay for a lot of clicks to his product, despite selling nothing, because those users were looking for a different product.

It seems that we have to incorporate our knowledge of what a cluster of products belonging to the same identifier should look like into the distance function. It would be great to have a clustering method where we don’t have to know the number of clusters in advance, because we don’t know how many different products exist.


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2.2 Deep Learning

Combining Distance Functions

We know that different products tend to have different prices. Sure, there is some variance, but you wouldn’t find an online shop selling an iPhone for half of the list price, for example. Therefore we could try to make use of this knowledge by looking at the distribution of price distances for products which belong together and the distribution of price distances between products which don’t belong together. We can visualize those distributions using a simple histogram.

In this graph the green histogram shows the distribution of relative distances between products which belong together and the blue histogram the same distribution of relative prices for products that don’t belong together. Those distributions are clearly different, which is good, because we want to know if we can use them to distinguish between related and unrelated products. For example, we can see that if we have a pair of products with a relative price distance of greater than 0.9, it is highly improbable that those products belong together.

In my experience, it is always useful to look at histograms of this kind if you want to estimate how hard it would be to distinguish between classes of items. In dealing with supervised learning problems I would usually look at histograms of all the features where this is possible. When you see distributions that are similar, you know this feature won’t be useful. If you see shifted distributions like in this case, you know that this feature contains some information about the thing you want to predict.


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2.2 Deep Learning

Let’s take an example of another distance function that seems to be useful: TfIdf-cosine distance. This distance is calculated between the text of the products. At first, the text is transformed into a vector representation. For each unique word in the text there’s a dimension in the vector. If we wanted to keep things really simple we could just use boolean values indicating whether a word occurs in a text or not. The problem with this representation is that some words are clearly more important than others. So we use a weighting scheme called tf-idf which has been proven really useful in the field of information retrieval. Then, we normalize the vectors representing our product texts to unit length and calculate the scalar product between all of them. This scalar product represents the cosine of the angle between the two text vectors and is near one for small values of this angle and near zero for big angles. We then transform this cosine similarity to a distance by subtracting it from 1, so that products which are close to each other in vector space have a small distance and products which are different have a larger distance.

The distributions of cosine distances are clearly different between products that belong to each other and the products which do not. It is also interesting that the difference in this distribution looks different from the difference in the price distance distributions. We could expect to be able to extract a different kind of information from this distance. We now could think of a lot of useful distance functions that reveal information about whether a pair of products belongs together. But for our clustering algorithm we need a single measure of distance. So we have to combine those different distance functions into a single distance. The simplest possible thing we could do is to concat all distance function results into a vector of distances for each product pair and then compute its overall length. The higher the combined distance, the lower the probability that a pair of products belongs together. Indeed this method alone does not work too badly.


53

However, we can do much better. For all products with an identifier, we know all the pairs that belong together and all pairs that do not. We could use this information as training data to train a binary classifier to distinguish between matches and non-matches given a vector of distance functions. This model should be able to learn a lot of information about whether two products belong together or not just by looking at the vector of distances.

Final Clustering

Now that we have a good measure of the likelihood of two products belonging together, learned from examples of products which do belong together, we still have to apply a clustering algorithm to the distance graph. To get a better idea of what we have to do, consider this 3d rendering of the data structure we try to separate into clusters. Products belonging to the same identifier were assigned the same color.

125 Stein, Benno, and S Meyerzu Eißen. “Document categorization with MajorClust.” Proc. 12th Workshop on Information Technology and Systems Dec. 2002.126 Christen, Peter, Tim Churches, and Markus Hegland. “Febrl–a parallel open source data linkage system.” Pacific-Asia Conference on Knowledge Discovery and Data Mining 26 May. 2004: 638-647.

In practice, all nodes would have the same color and we have to decide which product belongs to which cluster only by looking at the graph structure. Here’s another 2d visualization:

Applying k-means clustering with an estimated number of clusters leads to an ok but not great result. We evaluated different clustering strategies, and the one we found worked best for our problem was MajorClust125. With proper parameterization, our system was able to reach a precision of over 97% at reasonable recall.

Things that were critical for the success of the project:

• Good editorial frontends • Clever tricks to reduce the number of product

pairs the system had to look at - as the set of potential matches is O(n^2) for n=60000000, this number can become very large

Possible Applications in the Domain of Healthcare

• Record linkage to determine which textual descriptions of a person belong to the same actual person126

• Discover whether a person fits into a cluster of conditions, but isn’t labelled as such yet (ie. AI DOCTOR)


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2.3Description

and Assessment ofTools Used to Work on Huge Data Sets

Everything gets more complex when the size of data sets grows. Things that seem easy or normal for small data sets become complex and difficult when data size grows. For example, the quickest way to “upload” or “download” large data sets to different cloud providers is simply to ship hard drives127 directly to the data center. The simple act of moving data from one place to another becomes a logistical task when the data is large enough. Naturally, there are tools and programs to help manage large or huge data sets128, so in the following we will list some of them and discuss the data size and purpose they are intended for.

127 Bright, Peter. “Need to get a bunch of data onto Windows Azure? FedEx your hard drives”, Ars Technica, 5 November 2013 http://arstechnica.com/information-technology/2013/11/windows-azure-cloud-services-now-accepting-data-uploads-by-fedex/128 It is hard to define what “large” or “huge” data sets mean, and care has to be taken when working with a specific application to determine wheth-er the size needs are fulfilled by it. Also, the actual sizes of data sets are changing rapidly. What counted as a “huge” data set some years ago may now be viewed as small. While the largest accepted definitions of “big data” start between five and ten terabytes, already in 2012 Facebook announced that their graph data alone was measured in Petabytes, while their total storage was in the “high hundreds of petabytes”http://www-conf.slac.stanford.edu/xldb2012/talks/xldb2012_wed_1105_DhrubaBorthakur.pdf

While in the past, large-scale computing was often run on specialized hardware, all solutions we present here run on commodity computers that can easily be bought or rented anywhere, bringing down cost massively. The typical first choice for data processing is Microsoft Excel. Almost by definition, data processed in Excel is not big data. The maximum size of data that can be processed in Microsoft Excel is determined by the workstation it is run on, but can usually not be larger than 1 GB. Analysis is limited, and there are also well-known flaws in Excel’s data processing that can

Data size Processing options Problems and risks

0 - 1 GB Microsoft Excel, standard desktop tools Known for inaccurate results at large sizes, limited by desktop machine capabilities

0 - 1 TB Custom scripting, for example using Python or R Choice of data storage must be chosen, analysis speed limited by single machine, might be exploratory and as such investment needs to be checked

0 - 100 TB SQL database Might not offer specific query capabilities, then combination with custom scripts

10 TB - 1000 TB

NoSQL databases Many different options with different offerings must be considered, also custom scripting is usually necessary, distributed systems offer different cost/speed trade-offs

10 TB and up Apache Hadoop or other Map/Reduce system Distributed system must be administered, usually a big investment, only necessary for large data sizes but often mis-used for smaller data sets

An overview of data size categories and the options for handling storage and processing in each of them.


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2.3 Description and Assessment of Tools Used to Work On Huge Data Sets

lead to flawed results129, so using Excel for large-scale or important data analysis is strongly discouraged.

Custom scripts for data analysis are best for data of the next step up in size and accessibility. These scripts are usually written in a scripting language like Python or R, intended to be run on single machines. These are useful in situations where storage is not likely to be an issue, namely for data sets less than 1 TB in size. Some storage mechanism has to be provided, so either raw files, CSV files, JSON data sets or some other storage backend must be present. The major advantage of a custom script is its flexibility, since anything that can be programmed can be tried out. However, since speed of analysis is then dependent on the operations, custom scripting is often used in the initial, exploratory phases of a project to determine the final needs of data analysis tooling. Investment in such an exploratory phase is rarely wasted, since the exact analysis mechanisms are usually not known beforehand to a fine-enough degree, so that precise determination of needs in advance can prevent costly corrections later on. Additionally, once exploration is finished, the custom scripts can be adapted into other analytical tools or re-worked to fit larger data sets.

To meet the storage needs of larger data sets, and also to offer somewhat specialized query engines, the next step up is a regular SQL database engine like MySQL, PostgreSQL, Microsoft SQL Server, or Oracle SQL. Due to prohibitive licensing costs at larger scales, open source SQL systems are usually preferred. Even though most installations of such systems are used to handle data sizes in the megabytes, these systems scale surprisingly well even into large terabyte sizes. As an example, both Google and Facebook have used MySQL installations well into the 1000’s of terabytes, albeit quite customized ones130, 131

129 Hesel, Dennis. “Is Microsoft Excel an Adequate Statistics Package?”, Practical Stats. http://www.practicalstats.com/xlsstats/excelstats.html130 Maitland, Jo. “Google moves AdWords off MySQL to F1”, 30 May 2012, GigaOm. https://gigaom.com/2012/05/30/google-moves-adwords-off-mysql-to-f1/ 131 Borthakur, Dhruba. “Petabyte Scale Data at Facebook”, September 2012. http://www-conf.slac.stanford.edu/xldb2012/talks/xldb2012_wed_1105_DhrubaBorthakur.pdf132 https://www.mongodb.com/133 http://cassandra.apache.org/134 http://redis.io/135 https://www.elastic.co/136 http://hadoop.apache.org/

(note: both have now reached scales where custom systems have been implemented). Since SQL systems offer both storage and processing tools, using such a common off-the-shelf database can be an excellent choice for sizes up to 100 TB. When crossing the threshold of 10 TB, now-common NoSQL-databases start to become meaningful alternatives. These databases do not rely on the complicated SQL standard, but instead store documents in a key-value format or in custom formats. The advantage is that querying these databases is very fast because there is very little query logic involved. The downside is that processing is not usually available. As such, these databases can be used as a storage backend that feeds into a query processor or analysis engine, such as one produced by custom scripting as outlined above. There are a variety of common open source NoSQL systems available, offering vertical and horizontal scaling, essentially without size limits. The most widely-used of these databases are MongoDB132, Apache Cassandra133, Redis134, and Elasticsearch135. Each NoSQL database has its own advantages and disadvantages, which must be evaluated before using any such database. Some come with integrated query capabilities to different extents, others simply offer storage.

Finally, at the largest sizes, there exist specialized storage/analysis systems built specifically for big data that are able to handle sizes well into the hundreds of terabytes without problems. The most well-known system is Apache Hadoop136, which consists of a distributed file system called HDFS (which can be used as a NoSQL database) and an attached Map/Reduce system. The Map/Reduce paradigm is a method of treating very large datasets in a structured way on a large number of machines and was first published by Google. Most big data systems


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will eventually use a Map/Reduce mechanism, and as such this is the current gold standard for big data systems over the critical sizes mentioned above.

While there are several proprietary systems offered by vendors such as Oracle or SAP, these are usually quite cost-prohibitive and are often built on top of Apache Hadoop besides.

Since any big data project necessarily operates on large data sizes, the necessary computing infrastructure always plays a part in implementing such a system. The usual vendors each have their own offering of such systems that include automatic scaling and pay-per-use. Examples are Google’s BigTable137 or Cloud Dataflow138, Amazon’s DynamoDB139 and Redshift140, and Microsoft’s DocumentDB141 and Table142 storage. However, all of these solutions rely on cloud offerings, which are also hosted outside of the European Union and are thus usually not legally usable for sensitive data.

In conclusion, there is a large selection of storage and analytics engines for all sizes and needs, both in cloud solutions and on-premises. The smallest solutions can be implemented quickly and used for exploration and specification, while the gold standard is represented by a cluster running Map/Reduce programs on Apache Hadoop. Many solutions are open source and can be used without cost, or reinforced by commercial support offerings, and all solutions can be run on widely available commodity hardware.

State of Technology Development With the Goal of Understanding how Big Data in Healthcare can be Exploited in a Pharma Setting

137 https://cloud.google.com/bigtable/138 https://cloud.google.com/dataflow/139 https://aws.amazon.com/dynamodb/140 https://aws.amazon.com/redshift/141 https://azure.microsoft.com/en-us/services/documentdb/142 https://azure.microsoft.com/en-us/services/storage/tables/143 Kopf, Dan. “The Guinness Brewer Who Revolutionized Statistics”, Priceonomics, 11 December 2015. https://priceonomics.com/the-guinness-brew-er-who-revolutionized-statistics/

On a very general level, big data techniques can be grouped into a handful of categories, of which these three appear most interesting: analytics, clustering, and feature detection. Each of these categories has different properties and different application scenarios.

The first category is analytics, and is probably the most traditional statistical technique. In general it means determining some quality of a data set according to a specific standard. In fact, the field of statistics itself was started by a brewing company interested in analyzing their products and finding the factors that led to a stable product quality143. Naturally, analytics are used by almost every company nowadays, especially so in healthcare and pharma settings. Usually, well-known database systems are used to store moderate amounts of data for statistical analysis regarding specific questions and hypotheses. However, once the size of these data sets crosses into the unwieldy (by traditional standards), the emphasis shifts from pure hypothesis testing into hypothesis generation and exploration. However, this is still led by users and a rather more manual way of dealing with big data.

Naturally, analytics can help the pharmaceutical industry in the same ways it can help other industries - namely, by streamlining processes. However, it can also be used to evaluate other kinds of data and can be useful in interpreting many different kinds of measurements.

The second category is clustering. Here, varied, multi-dimensional data are treated by specialized algorithms to create a clustering, or labeling, of each individual data point. Instead of finding qualities of a data set, groups of data points are found that somehow belong together, ie. a subset or cluster is built into the data set. This is sometimes called “labeling” because each group can also be called a label, so putting one data point into a group is the same as attaching a specific label to it. Also, once


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the items are grouped it is usually easy to attach labels to the groups, for example to differentiate species of plant specimens144. One of the major breakthroughs in big data was the design of automatic clustering algorithms, such as support vector machines. There exist several well-understood clustering algorithms now that need very little human input and can be used to extensively label data quickly and accurately. However, guidance and interpretation of results is still needed in many cases.

It is hard to predict how such clustering methods can be used specifically in pharma and healthcare, simply because there are many cases where a grouping of data points can be useful. Do patients with a specific outcome constitute a cluster? Are there unknown clusters of symptoms or side effects reported? Many different cases and questions can be approached with clustering algorithms, so an exhaustive list is impossible to generate.

The third and most recent category in big data can be called “feature extraction”. This is a technique where varied, unlabeled and unstructured data is associated with specific features that are not obvious from the data itself. Most recent breakthroughs come from this field, specifically in image and sound classification. One example is detection of dog breed from simple images145. While this task might be simple for human viewers, it was traditionally very hard for computers to answer these questions with any accuracy, especially without “overfitting” to other factors of training data such as the average lightness of an image146. However, recent advances in this field have yielded robust results even on very unstructured

144 “Iris Flower Data Set.” Wikipedia, Wikipedia.org, np. https://en.wikipedia.org/wiki/Iris_flower_data_set145 https://www.what-dog.net/146 “Machine learning and unintended consequences”, LessWrong.com, 23 September 2011. http://lesswrong.com/lw/7qz/machine_learning_and_unin-tended_consequences/147 Hill, Kashmir. “How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did”, Forbes, 16 February 2012. http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/#3351b6a734c6148 “Big data and analytics in the automotive industry; Automotive analytics thought piece”. Deloitte. 2015. https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/manufacturing/deloitte-uk-automotive-analytics.pdf149 Noyes, Katherine. “What’s on trend this season for the fashion industry? Big data”, Fortune.com, 22 September 2014. http://fortune.com/2014/09/22/fashion-industry-big-data-analytics/150 Dede, Chris; and Ho, Andrew. “Big Data Analysis in Higher Education: Promises and Pitfalls”, Educause Review, 22 August 2016. http://er.edu-cause.edu/articles/2016/8/big-data-analysis-in-higher-education-promises-and-pitfalls151 Lohr, Steve. “Google Flu Trends: The Limits of Big Data”, Bits, 28 March 2014. http://bits.blogs.nytimes.com/2014/03/28/google-flu-trends-the-limits-of-big-data/?_r=0

data, such as user-generated images or continuous measurement data. Instead of relying on labeling and directed training phases, these new algorithms instead rely on sheer volume of data for their efficacy. Since this field is quite young and advances are frequent, it is hard to estimate the impact it will have on data science as a whole, as well as the use cases it will cover. However, already problems have been solved that seemed intractable only a few years ago. As such, it seems plausible that feature extraction might be useful in medical and pharmaceutical settings as well, given that enough source data is available.

Even though the most recent and most surprising use cases for big data have been in fields as diverse as retail147, automotive148, fashion149, and education150, there have been several forays into the usage of big data in a healthcare and pharma setting.

The most famous example of this is naturally the Google Flu Trends debacle, in which Google offered to mine their vast database of search queries and find search terms that correlate to outbreaks of flu in certain areas. This was then taken as an indication that it should be possible to predict outbreaks of flu with these search terms, even though it later turned out that this was not possible151. In fact, projections based on 3-week-old case data as collected by the CDC yielded better projection accuracy than did Google’s data mining. Furthermore, it looks as though Google’s selected search terms correlated much better to the media’s presentation of flu trends than to actual cases. As such, the fanfare created by the publication of Google Flu


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Trends might very well have impacted its own accuracy.

It is important to keep in mind that big data and data mining very rarely turn up causal links. Rather, previously undiscovered correlations and clusters are found in existing data sets. There is tremendous value in finding such correlations since they point the way towards further, causal investigation. Similarly, clusters or patterns in data are enormously useful, especially in a healthcare context; what else are illnesses but clusters of symptoms, and how better to find new ones or more clearly specify known ones than to look at these clusters and their patterns?

There have been other uses of big data specific to health care, although not as widely publicized as Google Flu Trends.

We find that each of the three categories outlined above - analytics, clustering, and feature extraction - show great promise to be used productively in pharma and healthcare settings, where many different use cases are possible.

Learn from Practical Examples that Have Been Used Successfully in Healthcare, Medicine, Life Sciences, and Outside the Healthcare Industry

Judging by the plethora of applications that harness the power of big data across industries, one cannot deny that big data analytics has already evolved from a high-potential to a high-impact technology. A machine learning algorithm outperformed general practitioners in predicting depression in individuals, solely by analyzing the composition of their pictures on Instagram152. Rapid progress in deep learning has enabled cognitive systems like Google’s ‘DeepMind’ to become the world’s best in the game of Go153 - and the gap to human level performance is closing in other domains154. Clearly, big-data applications are

152 Reese, Andrew G; and Danforth, Christopher M. “Instagram photos reveal predictive markers of depression”, https://arxiv.org/ftp/arxiv/pa-pers/1608/1608.03282.pdf153 http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html154 https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf155 http://www.ibm.com/analytics/us/en/case-studies.html156 https://netzpolitik.org/wp-upload/LKA_NRW_Predictive_Policing.pdf157 http://www.faz.net/aktuell/gesellschaft/kriminalitaet/software-programm-precobs-berechnet-ort-von-einbruechen-13966153-p2.html158 http://articles.chicagotribune.com/2013-08-21/news/ct-met-heat-list-20130821_1_chicago-police-commander-andrew-papachristos-heat-list

not exclusive to specific industries. Take for example the long list of projects that are making use of IBM’s big-data analytics capabilities. IBM presents use cases in the banking, government, education, insurance, and healthcare industries, in business areas ranging from finance to risk management, to name just a few155. Despite the diversity of applications, common threads can be detected. Whether it is about understanding the patterns of prospective criminals or predicting readmission risks of post-surgery patients, big-data analytics often yields actionable information about potential scenarios in the future. This section gives an overview of exemplary big data analytics projects that are making an impact in the real world today.

Modern law enforcement practices have been strongly shaped by advances in information technology and scientific research. The increasing stream of behavioral data from numerous sources is now opening up new possibilities in this area. A data-driven approach called ‘predictive policing’ allows law enforcement agencies to focus their efforts on at-risk locations and suspects who are most likely to be involved in upcoming crimes. The underlying process enabling this practice involves pattern detection in big data databases to gain a better understanding of criminal activity, ultimately allowing for a more effective deployment of police officers. Such predictive policing systems are no futuristic scenario: 70-90% of US police departments surveyed in 2013 were using or intended to use predictive methods by 2016156, and Germany and Switzerland have implemented a predictive system called “Precobs” in multiple police departments157. A few years ago, the city of Chicago, using data-driven law enforcement measures, generated and publicly released a ‘heat list’ of 400 people who were deemed to be most prone to violence158. In 2016, more than 70% of people shot and 80% arrested in connection with shootings by


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mid-year were on this now 1,400 person list, according to the Chicago police as cited in the NY Times159. On the business side, Microsoft (Cortana Analytics)160, Hitachi (Predictive Crime Analytics)161 and IBM162 all offer crime-management solutions and present successful use-cases thereof, such as cutting violent gun crime by 46% over a 7-year period in Durham.

US Startups like ‘BLOCKpeek’ seek to carry this technology into the private-consumer market. BLOCKpeek is working on a mobile app that is powered by predictive analytics that warns users of nearby potential hazards like demonstrations, shootings or severe weather163.

Further illustrating the role of big data in this context, a large-scale study by A. J. Rosellini et. al. aimed at the development of an actuarial model to predict future violent crimes among US Army soldiers. A machine learning algorithm was trained on data relating to 975,057 soldiers who served during a six-year period. The developed algorithm found certain key predictors of future crimes, such as a disadvantaged social/socioeconomic status, mental disorder treatment and prior crime. Of all major physical crimes (e.g. murder-manslaughter, kidnapping, robbery), 36.2% were committed by the group of soldiers with the highest predicted risk - 5% of the total population of male soldiers. These results suggest that such models could be used for predictive crime purposes, driving decisions about preventive measures such as routine-checkup, early interventions, or increased support164.

In healthcare, the emergence of EHRs, quantified self-

159 http://www.nytimes.com/2016/05/24/us/armed-with-data-chicago-police-try-to-predict-who-may-shoot-or-be-shot.html160 https://enterprise.microsoft.com/en-us/industries/government/fighting-crime-with-big-data-analytics/161 https://www.hds.com/en-us/pdf/case-study/hitachi-success-story-austin-police-department.pdf162 http://www-03.ibm.com/software/businesscasestudies/hk/en?synkey=E906175Y75689V95163 https://www.deutsche-startups.de/2015/10/14/blockpeek-moechte-die-welt-zu-einem-besseren-ort-machenwarte-auf-mail/164 Rossellini, AJ, et al. “Predicting non-familial major physical violent crime perpetration in the US Army from administrative data.” The National Center for Biotechnology Information, January 2016. http://www.ncbi.nlm.nih.gov/pubmed/26436603165 Cattell, Jamie, et al. “How big data can revolutionize pharmaceutical R&D”, April 2013, McKinsey & Company. http://www.mckinsey.com/indus-tries/pharmaceuticals-and-medical-products/our-insights/how-big-data-can-revolutionize-pharmaceutical-r-and-d166 Mirnezami, Reza, et al. “Preparing for Precision Medicine”, New England Journal of Medicine, 9 February 2012. http://www.nejm.org/doi/full/10.1056/NEJMp1114866#t=article

tracking, and other data-amassing trends are enabling a big data revolution. Furthermore, the average researcher who reads 250 to 300 articles annually cannot keep up with most of the novel scientific achievements in a world where scientific output doubles about every 9 years. Connections between many data sources might remain uncovered as a result, leaving behind a huge lost potential. Big data analytics tools present opportunities across all of these touchpoints - either creating new knowledge by detecting patterns in vast amounts of medical data, or gaining actionable insights from existing knowledge much faster than humanly possible. As the following examples suggest, big data analytics supercharges progress in Precision Medicine as well as in pharmaceutical R&D and offers exceptional potential for the optimization of diagnosis and improvement of prevention through predictive measures. This development is not only of interest to patients but also to businesses, since big-data strategies could generate additional revenues of up to US$100 billion annually in the US healthcare system alone165.

Precision Medicine promises to usher in the era of personalized medicine, where “diagnostic, prognostic, and therapeutic strategies [are] precisely tailored to each patient’s requirements”166. One must distinguish between therapies that are exclusively created for one individual patient and those that would fall in the category of ‘mass customization’. As a first step, the ‘trial and error’ approach can be abandoned in many cases in light of stratified medicine and companion diagnostics. For instance, targeted oncological therapies have already been developed and are in clinical use today, owing to a better understanding of cancers and their distinct


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genomic signature167. In certain types of lung cancers with specific mutations, for example, therapeutic selection based on genomics has become the standard of care. Cost-lowering next-generation sequencing and the analysis of this big data now promise to explore the molecular landscape of tumors further to ultimately optimize therapy strategies for individual patients168.

Another example of mass customization is a companion diagnostics test, “a pre-treatment test performed in order to determine whether or not a patient is likely to respond to a given therapy” 169. In Germany, more than forty drugs accompanied by such tests are currently on the market170. The ultimate treatment in personalized medicine, however, would go beyond selecting the most suitable therapy from a broad arsenal of available drugs. One example of such a therapy is individualized immune-therapy in cancer patients. Clinicians would produce medicine tailored to the individual patient by identifying the exact composition of peptides in the patient’s tumor-cells and training the immune system to fight these cells171.

In addition to better understanding cancer genomes, human genome sequencing on a massive scale is garnering increasing attention. With privately held genome-sequencing companies like Human Longevity Inc. and genotyping-focused 23andme entering the market, DNA databases are growing in size. Companies like Deep Genomics specialize on using deep learning to predict the consequences of genomic alteration on cell

167 “Targeted Cancer Therapies”. National Institute of Health, www.cancer.gov. April 2014. https://www.cancer.gov/about-cancer/treatment/types/targeted-therapies/targeted-therapies-fact-sheet168 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4397718/169 http://onlinelibrary.wiley.com/doi/10.1002/ddr.21029/abstract170 http://www.vfa.de/de/arzneimittel-forschung/datenbanken-zu-arzneimitteln/individualisierte-medizin.html171 https://www.jung-stiftung.de/de/presse-downloads/pressemeldungen/2016/personalisierte-immuntherapie-gegen-krebs172 “Deep Genomics launches, uniting deep learning and genome biology”. Kurzweilai.net, 22 July 2015. http://www.kurzweilai.net/deep-genom-ics-launches-uniting-deep-learning-and-genome-biology173 https://blog.enlightenbio.com/2016/02/15/at-agbt-2016-the-winners-are-long-reads-and-whole-solutions/174 23andme blog175 https://www.ncbi.nlm.nih.gov/pubmed/22472876176 www.nature.com/ng/journal/vaop/ncurrent/full/ng.3623.html177 https://www.nih.gov/precision-medicine-initiative-cohort-program178 http://www.japantimes.co.jp/news/2016/08/11/national/science-health/ibm-big-data-used-for-rapid-diagnosis-of-rare-leukemia-case-in-ja-pan/%252523.V7B-6JN97ow

mechanisms172. The academic community too is showing increasing interest in accessing assets like 23andme’s data pool of 1.2 million genotyped customers173. Studies that failed at finding links between genes and certain diseases might be more prone to success if the quantity of individuals studied was raised174: whereas a study incorporating 9.000 subjects did not yield any insights about genes causing depression175, a study involving data from over 300.000 23andme customers found 17 single nucleotide polymorphisms in 15 genetic loci associated with depression176.

Being aware of the importance of big data in the ‘omics’ field, governments are initiating large-scale projects. The US NIH “Precision Medicine Initiative Cohort” is planning a project involving a cohort of 1 million participants, with the goal of amplifying precision medicine’s successes in oncology as well as extending it to a broad range of diseases. Large amounts of diverse data sources will join molecular, genomic, cellular, clinical, behavioral, physiological, and environmental parameters to form an immensely diverse database177.

Cognitive systems like IBM’s Watson have attained levels of diagnostic performance that rival and sometimes even surpass human experts. In the case of a female leukemia patient in Japan, ‘Watson for Oncology’ refined the original diagnosis to a specific rare type of leukemia and suggested a different treatment178. Watson’s ability to design such evidence-based treatment regimens stems from the analysis of scientific literature, including


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“MSK curated literature and rationales, […] over 290 medical journals, over 200 textbooks, and 12 million pages of text”179. Mapping the knowledge gained hereby to a patient’s medical record allows Watson to provide ranked treatment options for individual patients in record time. Consequently, the identification of the specific mutated genes of diagnostic importance in the case of the Japanese patient took 10 minutes – a drastic improvement compared to the 2-week period required for human scientists to perform the same task.

179 http://www.ibm.com/watson/health/oncology/180 https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf181 http://www-03.ibm.com/press/us/en/pressrelease/50057.wss182 http://www.popularmechanics.com/science/health/a13391/ibm-skin-cancer-detection-system-memorial-sloan-kettering-17545836/

Taking into account that cognitive systems already reach accuracies of over 97% in facial recognition tasks180, the high potential of such systems to perform accurate medical image analysis does not seem farfetched. Indeed, IBM is unleashing this potential in the form of an algorithm that detects melanoma vs. 12 benign skin diseases. To date, the algorithm reaches accuracies of 83% to 91%181, depending on image quality, and is in use in the US hospital Memorial Sloan Kettering182. Two projects were


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announced in 2016 to further improve the accuracy of the aforementioned machine learning algorithm, aiming at mining more than one million images.

The technique by which cognitive computing systems like Watson successfully interpret vast amounts of structured and unstructured data and thereby generate diagnostically relevant insights is also highly relevant to the pharmaceutical research process. ‘IBM Watson Discovery Advisor for Life Sciences’ promises to accelerate the drug discovery process by rapidly interpreting data sources like scientific publications, patients, and genomics databases, and presenting information in a condensed form183. Watson is able to accomplish this feat using deep learning natural language processing, understanding not only the meaning of single words but also the relationship between them. This way, Watson optimizes the researchers’ ability to work with existing evidence by enabling them to quickly make out

183 http://www.ncbi.nlm.nih.gov/pubmed/27130797

the most important areas to focus on. For instance, if the relationship between a given disease and a certain gene is of interest, Watson processes all scientific publications available within hours and uses visualization techniques to create a holistic network map containing all connections mentioned in the studied data (see figure 4).

Not only does this procedure solve the impossible task of reading millions of articles and prevent human bias from contaminating the results of the search for information, but it also allows for connections across domains to be made. Whereas serendipity has previously been the reason for many medical breakthroughs in which seemingly dissimilar domains coincidentally revealed commonalities, the integration of data from disparate domains - across therapeutic areas, study types, and steps in the drug development process - can replace the serendipity-based approach with a systematic one. In the case of the cancer researcher, they would most probably


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limit their search to cancer literature. In contrast, Watson would draw on all information available, regardless of disease, journal or even species of study.

After the interpretation and visualization of the data, Watson continues mimicking the human decision-making process by engaging in the evaluation of the information. Machine learning and predictive analytics are used in this evaluation phase to generate new hypotheses about relationships for which no evidence is yet available. Two examples illustrate the practical significance and impact this procedure will have on the identification of new drug targets and the repurposing of existing drugs. In a 2013 retrospective study, Watson was used to detect high-potential cancer kinases that phosphorylate the P53 protein. Having learned from a training dataset, which included evidence of all kinases that had been observed to phosphorylate P53 through the year 2002, Watson identified 9 novel potential kinases most likely to have the desired effect. Indeed, 7 out of the 9 kinases predicted by Watson had been discovered and validated between 2002 and 2013 by human researchers.

The second example suggests that Watson’s usefulness is not limited to the accelerated identification of new potential drug targets, but also reaches into the area of drug repurposing. The bringing together of cross-domain data, as explained above, was the key in identifying potential compounds in the existing portfolio of a pharmaceutical company to treat malaria. For this purpose, Watson can draw on information from sources like preclinical study results, clinical trial data, ADE reports in drug safety databases and databases comprising all approved therapies and existing drugs. In the case of the malaria project, Watson first analyzed MEDLINE literature to identify drugs that suggested efficacy against the malaria parasite. Second, Watson filtered the portfolio of the pharmaceutical company for any

184 http://content.healthaffairs.org/content/33/7/1123.abstract185 http://www.ncbi.nlm.nih.gov/pubmed/22644078186 http://www.ncbi.nlm.nih.gov/pubmed/25099997187 http://www3.gehealthcare.com/en/insights/forward_thinking/forward_thinking/why_has_not_big_data_transformed_healthcare188 http://www.ncbi.nlm.nih.gov/pubmed/26363683189 http://jama.jamanetwork.com/article.aspx?articleid=1104511

compounds of similar structure. This 1-month endeavor left Watson with 15 drug candidates. In contrast, it took 10 research scientists 14 months to perform the same task, with similar results. Approximately half of the drugs identified by Watson matched the ones found by the scientists. Whether the other half turned out to be of value was not disclosed by the pharmaceutical company.

Deploying predictive health systems based on big data will yield substantial benefits even in the short-term, according to Bates et. al184. Adverse effects, high-cost patients, triage, and hospital readmissions are among the many use-cases where data analytics can deliver value to both patients and healthcare organizations. Simple predictive analytical tools for identifying high-risk patients and predicting patient readmission risks and death are already in clinical use internationally. However, the predictive power of industry-standard models like the LACE model varies, and proves to be ineffective in certain patient groups185 or diseases186. Predictive analytics solutions based on big data analytics involve higher costs, since one-size fits all approaches do not work and solutions must be tailored to specific institutions in order to be effective.

Every provider’s process and data are different, which is one of the reasons why big data predictive tools are hard to implement187. However, if institution-specific big data prediction models are applied, they outperform the LACE model, sometimes being twice or three times more effective188. Such insights can improve the quality of care effectively making use of resources to influence future outcomes. Furthermore, other studies show that most risk-prediction models - almost all of those tested being logistic regressions - perform poorly in predicting hospital readmission risk189 while more complicated models using big data, particularly deep learning, can substantially raise


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the predictive accuracy190. Intel with Cloudera191, GE-owned Caradigm192 and IBM193 are some of the companies currently in the market of predictive analytics solutions aimed at reducing hospital readmissions.

Working With Registry Data According to Policy Makers and Actual Examples.

Hand-written medical data is increasingly giving way to its digital counterpart194. The increasing adoption of electronic health records (EHR) and medical records (EMR) not only facilitates medical practices day-to-day, but is also tremendously relevant to the success of registry-based studies. Such studies can become more powerful, more feasible, and more easily conducted as a result of having greater amounts of healthcare data at their disposal.195 As indicated in the previous section, large amounts of data from EHRs can be used to support clinical decision-making in areas such as predicting readmission risks or empowering diagnoses. This section will explore the applicability of registry studies in medical research and point to potential regulatory hurdles.

Registries can be used for a wide variety of purposes, such as determining clinical effectiveness of treatments in real-world conditions (more in section ‘Future of EBM’); assessing the safety of pharmaceutical drugs; and measuring overall quality of care. With electronic medical data available in sources such as EMRs, EHRs, hospital records, and payer claims databases, registry studies can become a much more powerful tool. However, the adoption of such electronic medical

190 http://www.sciencedirect.com/science/article/pii/S1532046415000969191 http://www.intel.com/content/www/us/en/healthcare-it/solutions/documents/predictive-analytics-reduce-hospital-readmission-rates-white-paper.html192 https://www.caradigm.com/en-us/solutions-for-population-health/healthcare-analytics/ 193 https://www-01.ibm.com/software/sg/industry/healthcare/pdf/setonCaseStudy.pdf194 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4341817/195 Registries for Evaluating Patient Outcomes: A User’s Guide Volume 2, p. 59196 https://www.healthit.gov/sites/default/files/data-brief/2014HospitalAdoptionDataBrief.pdf197 http://content.healthaffairs.org/content/early/2014/08/05/hlthaff.2014.0445198 https://www.elga.gv.at/faq/wissenswertes-zu-elga/index.html199 http://digitalpresent.tagesspiegel.de/patientendaten-sollen-europaweit-vernetzt-werden200 http://www.ncbi.nlm.nih.gov/pubmed/27274072201 https://www.nih.gov/sites/default/files/research-training/initiatives/pmi/opportunities-challenges-electronic-health-records.pdf202 Registries for Evaluating Patient Outcomes: A User’s Guide Volume 1, p. 19

systems varies greatly by country. While the US adoption rates of EHRs in hospitals196 and physician offices197 has already surpassed the 75% mark, Austria’s stepwise introduction of its EHR system ‘ELGA’ was only initiated at the end of 2015198, and Germany’s goal is to have a working EHR system with information about medication, medical reports and self-measured data by 2018199. Despite these differences, large-scale registry studies are feasible today, as shown by a recent study that examined treatment pathways. In this study, data from EHRs and administrative claims databases from a total of 250 million patients was used.200

The feasibility of exploiting EHRs and registries for research purposes is strongly linked to compliance with legal standards. The studies mentioned in this section that used EHR data generally do not include a detailed description of the process by which regulations were met. The following paragraph gives a first look at the requirements for conducting registry studies by taking the US regulatory landscape as an example; there are different ways of getting permission to use registry data for research purposes201.

First, existing research cohorts could be merged to form a larger registry, requiring only an update in the terms of patients’ consent202. Second, clinical data could be acquired through organizational partnerships with community-based hospitals, clinics, pharmacies, health systems like Kaiser Permanente, and many more potential candidates. In the case of comparative effectiveness research with participating institutions


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located in multiple states, for example, one must comply with state- and institution-specific regulations and policies203. Third, patients themselves could become active agents through the right to access and share personal health information, called the ‘Blue Button’.

HIPAA compliance and research laws play an important role in using electronic medical data for research. Pharmaceutical companies that fall into the category of providers that “transmit any health information in electronic form in connection with a transaction” or “business associate[s] of another covered entity” can be classified as a covered entity subject to the Health Insurance Portability and Accountability Act (HIPAA) and the HITECH Act204. As such, they are subject to regulations like the Privacy Rule and the Security Rule, regulations establishing “national standards to protect individuals’ medical records and other personal health information” as well as “individuals’ electronic personal health information”, respectively205.

In addition to the Privacy Rule, the Common Rule, one of the U.S. Department of Health & Human Service (HHS) regulations, as “the uniform set of regulations on the ethical conduct of human subjects research” is an important regulation to consider206. Institutional Review Board approval is required when an institution is engaged in research with human subjects. The Common Rule “applies to all research involving human subjects conducted, supported or otherwise subject to regulation by any federal department or agency”. However, an institution is exempt from the Common Rule if (1) the information of interest is already publicly available or (2) the information is recorded in “such a manner that subjects cannot be identified”207.

The applicability of regulatory requirements for registry

203 http://www.ncbi.nlm.nih.gov/pubmed/23774516204 https://www.law.cornell.edu/cfr/text/45/160.103205 http://www.hhs.gov/hipaa/for-professionals/privacy/index.html206 Registries for Evaluating Patient Outcomes: A User’s Guide Volume 1, p. 19207 http://www.hhs.gov/ohrp/regulations-and-policy/regulations/45-cfr-46/index.html#46.101208 Registries for Evaluating Patient Outcomes: A User’s Guide Volume 1, pp. 19,171209 Registries for Evaluating Patient Outcomes: A User’s Guide Volume 2, p. 103210 Registries for Evaluating Patient Outcomes: A User’s Guide Volume 2, p. 106211 http://www.ncbi.nlm.nih.gov/pubmed/19132802

studies in particular can be evaluated using the official guide of the Agency of Healthcare Research and Quality (AHRQ) “Registries for Evaluating Patient Outcomes”. It outlines in great detail which aspects must be considered to successfully conduct registry studies for the purposes of public health activities, governmental health program oversight, quality improvement/assurance (I/A) and research. The applicability of the Privacy Rule and the Common Rule depends on many factors: the “purpose of a registry, the type of entity that creates or maintains the registry, the types of entities that contribute data to the registry, the extent to which registry data are individually identifiable […], the consent process, and the inclusion of genetic information”208. To conclude, the applicability of regulations must be investigated in detail for the specific registry study of interest and varies with regards to nation- and state-specific laws and regulatory standards.

Ensuring the safety of a drug is a core objective of the pharmaceutical R&D process, but the relatively low number of clinical trial participants relative to the much larger and more diverse populations who end up using the drug after approval poses clear limitations to guaranteeing such safety209. On the other hand, registries can include patients who are much different and suffer from more complex diseases or comorbidities than those of participants studied in clinical trials210. Postapproval pharmacovigilance is therefore an important topic for all healthcare stakeholders, yet adverse drug events (ADEs) suffer from high underreporting rates owing to the voluntary nature of reporting such incidences211. Analyzing data stored in EHRs can be used to remedy the shortcoming that systems relying on nonsystematic recognition of ADEs pose, as shown in various research projects: Large registry studies which involved multiple millions of subjects combined healthcare


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records from several EU countries and demonstrated the feasibility of detecting safety signals and ADE associations, if databases were sufficient in size212, 213.

In order to uphold the data holders’ control over their protected data and ensure patient privacy, the data from the different databases were aggregated. The subject of analysis in this case was structured data, which is much easier for data-analytics systems to process accurately. However, mining of unstructured data such as free-text clinical documents via natural language processing techniques214, 215 or simpler methods216 can also be used to detect ADEs and might reveal events otherwise undetected by analyzing only structured data. Data mining can thus be beneficial for rapid notifications of ADEs ahead of official alerts, as well as for hypothesis generation.

All in all, the combination of large amounts of both structured and unstructured data in EHRs has the potential to support pharmacovigilance research and pharmaceutical decision making. For a more specific example of ADEs detected using data mining methods, take the analysis of comedication for two of the most prescribed drugs worldwide. Data analysis of EMR data revealed a strong signal for glucose homeostasis affected by the combination of the cholesterol-lowering drug pravastatin and the antidepressant paroxetine (Paxil). The study found that the administration of both drugs together had a drastic effect in raising glucose levels217.

Insights from studies of this kind are already affecting the real world. Findings from the pharmacovigilance pilot project “Mini Sentinel” set up by the FDA are part of the pool of information sources the FDA draws on in weighing pharmacovigilance measures. For the blood pressure drug Olmesartan, a warning was added to the drug’s label after link to intestinal problems was

212 http://www.ncbi.nlm.nih.gov/pubmed/22315152213 http://www.ncbi.nlm.nih.gov/pubmed/21182150214 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2732239/215 http://onlinelibrary.wiley.com/doi/10.1038/clpt.2012.54/full216 http://onlinelibrary.wiley.com/doi/10.1038/clpt.2013.47/full217 https://www.nigms.nih.gov/news/meetings/documents/russ_altman_article.pdf218 http://www.fda.gov/drugs/drugsafety/ucm359477.htm219 http://www.nejm.org/doi/full/10.1056/NEJMp1302834

discovered218, and Mini-Sentinel played a role as well in refuting the hypothesized increased risk of bleeding events in the usage of Dabigatran219. The high potential of data-mining projects of this kind is evident, but it is important to keep limitations of the Mini-Sentinel in mind, such as its observational design-approach, the usage of claims data and the fact that it is just one of many sources in the case of the assessment of the two drugs mentioned above, next to the FDA Adverse Event Reporting System, published case series and information from the CMS Medicare database.


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2.4The Possible Futures

of Big Data in Healthcare

2.4.1 How Will Big Data-Driven Healthcare eventually be able to change Evidence-Based Medicine and the Way Studies are Conducted in the Future?

Science strives for a true understanding of nature and the rules by which it operates. Through the scientific method, the continuous process of making observations, generating hypotheses and testing predictions based on these hypotheses, scientific knowledge can be generated and thus hidden processes uncovered and understood. Fortunately for patients, modern medicine can be included in the list of practices which rest upon a foundation of scientifically tested hypotheses. From having been based on religious beliefs, subjective judgement, and individual experiences to being based on scientific evidence and the formal analysis thereof - medical reasoning and decision making has undergone an impressive transformation throughout the past centuries and decades to arrive at the point it is today.

The evidence-based medicine (EBM) approach ensures that decisions concerning treatment, diagnosis, prognosis and safety stand on a bedrock of “best available external clinical evidence” derived from experimental as well as observational studies.220 Randomized controlled trials (RCT) have been labeled the gold standard for producing and evaluating evidence relating to the effect of interventions because of their strong internal validity and the potential to control biases. While EBM is indeed the best approach modern medicine has to peel back the layers of medicine’s secrets, the best evidence available often does not represent the much desired scientific truth, and RCTs as EBM’s

220 Sackett 1996, http://www.bmj.com/content/312/7023/71221 Sackett 1996, http://www.bmj.com/content/312/7023/71

favorite methods are far from infallible. Shortcomings owing to the inherent nature of RCT study design as well as suboptimal policy and incentive systems leave much room for improvement. Now, with big data on the rise, EBM methods can be enriched in ways that promise to improve quality and efficiency of care. This section will shine light on ways big data might remedy some of the shortcomings of contemporary EBM.

EBM is equipped with a toolkit of methods and study designs aimed at generating clinical evidence. Depending on the type of clinical question, either experimental or non-experimental approaches are best suited to serve this purpose. The primary questions in EBM can be divided in questions regarding diagnosis, prognosis, harm, and treatment. 221

The examples in the previous sections have already touched on the applicability and usefulness of big data in the areas of diagnosis, prognosis, and harm. Cognitive systems can maximize the potential of the best diagnostic evidence available by evaluating all peer-reviewed medical evidence ever created, thereby contributing to a more effective practice of EBM. The machine learning algorithm that detected melanoma is a prime example of how big data is at the core of the development of cognitive computing-based diagnostic tests that are both fast and accurate. With regard to prognosis, predictive analytics systems using big data were shown to be superior to simpler methods in some cases. To add another example positioned at the intersection between diagnosis and prognosis, a 2016 study showed that machine learning models performed fairly well in diagnosing and predicting


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2.4 The Possible Futures of Big Data in Healthcare

risks for acute kidney injury.222 Concerning harm and safety, large registry-based pharmacovigilance studies proved to be effective and efficient in spotting ADEs. To complete the picture of big data’s influence on the primary areas of EBM, it is necessary to take a look at how questions of treatment can be addressed.

The value of the evidence generated in the respective area of interest is largely determined by the underlying design of the study in question. It is commonly recommended that experimental approaches, as described above, be used to assess evidence when it comes to questions of treatment in order to avoid false positive conclusions about efficacy.223 In addition to RCTs, other study designs are proposed as methods of EBM and are being used to measure treatment success, namely observational studies like cohort studies and case-reports. Cohort studies in particular are seen as valuable tools for determining diagnostic test accuracy and prognostic factors or answering questions concerning safety and harm.224 However, such studies are seen as inferior for producing evidence and are thus located beneath RCTs in the hierarchy of evidence.225 The Oxford center for EBM226 ranks RCTs at level one, cohort studies at level two, and the German IQWiG227 institute - which is at the limit for non-randomized studies producing reliable qualitative results - at “low”. The relatively low significance of observational studies in clinical research must be questioned in light of the massive amounts of medical data and data mining tools becoming available.

To begin, a 2005 study reveals many shortcomings of modern EBM-based research. It found that a considerable amount of published study results may be wrong for reasons of overconfidence in studies that

222 Kate 2016, http://www.ncbi.nlm.nih.gov/pubmed/27025458223 Sackett 1996, http://www.bmj.com/content/312/7023/71224 http://guides.dml.georgetown.edu/ebm/ebmclinicalquestions225 Kovesdy 2012, http://www.ncbi.nlm.nih.gov/pubmed/22364796226 http://www.cebm.net/oxford-centre-evidence-based-medicine-levels-evidence-march-2009/227 p.86, https://www.iqwig.de/download/IQWiG_Methoden_Version_4-2.pdf228 Ioannidis 2005, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1182327/229 Angus 2015, http://jama.jamanetwork.com/article.aspx?articleid=2429723230 Bothwell 2016, http://www.nejm.org/doi/full/10.1056/NEJMms1604593231 http://clinicaldevice.typepad.com/cdg_whitepapers/2011/07/registry-studies-why-and-how.html232 Bothwell 2016, http://www.nejm.org/doi/full/10.1056/NEJMms1604593

rely on statistical p-values of 0.05, publication bias, lack of replication studies, a suboptimal environment of economic incentives that encourages quantity over quality, and many more.228 The weaknesses of RCTs can be added to this list. A causal link between the treatment in question and an observed outcome can be determined with high degree of certainty if randomization mechanisms and rigorous inclusion/exclusion criteria in RCTs are applied. However, the high internal validity of RCTs stands in stark contrast to the low external validity, or the inability to correlate results from the outcomes in study participants to the population at large. One cannot assume that clinical efficacy proven in RCTs is generalizable to subpopulations not studied in these trials.229 Thus, a different approach is needed to permit claims about efficacy in the real world to be made. Moreover, RCTs are costly and time-intensive in nature, rendering it infeasible to (1) conduct research on all topics of genuine significance to medicine (all combinations of treatments/individuals) and (2) delaying the point at which innovations are actually implemented into medical day-to-day practice.230 Comparative effectiveness research might also be problematic if conducted in a RCT-setting, if for instance the effectiveness of a drug should be compared to that of a device.231 Also, potential ethical concerns associated with the retention of possibly valuable medication to control groups can hardly be mitigated. Only through observational studies can the effects of smoking on lung cancer be investigated while at the same time respecting modern ethical standards.232

With these aspects in mind, big data can shape the workings of the evidence-creating mechanisms of EBM in the following ways: (1) improving RCTs through


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digital means, (2) guiding RCTs by delivering promising hypotheses or (3) complementing RCTs with large-scale observational studies. These aspects might bridge the effectiveness/efficacy gap and help in the quest for controlling costs and stopping the decline in ROI.

The clinical trial process presents many touchpoints where digital solutions can be introduced to improve the way candidates are managed and thereby increase overall efficiency and effectiveness233 .

First, the recruitment process can be accelerated and more suitable candidates can be selected. Based on factors like age, disease severity, genetic composition and several other novel criteria, the ‘right people’ can be chosen. Parting ways with the manual process of matching patients to clinical trials, IBM Watson for Clinical Trial Matching automatically provides a list of suitable candidates while maintaining transparency and disclosing the criteria used in the evaluation process.234 Other studies show that structured data in EHRs is an important source for determining a patient’s eligibility for clinical trial enrollments.235 In later stages of the development process, filtering out patients with non-responsive genes can prove valuable236, and insights derived from data analysis might cause entry criteria to be changed in an adaptive trial in order to maximize the suitability of patients responding to the therapy being examined.237 Additionally, reaching potential candidates via communication channels like social media contributes to a reduction

233 Cattell 2013, http://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/how-big-data-can-revolutionize-pharmaceu-tical-r-and-d234 http://www.ibm.com/watson/clinical-trial-matching.html235 Ateya 2015, http://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-016-0239-x236 (Malik 2010, 882)237 Angus 2015, http://jama.jamanetwork.com/article.aspx?articleid=2429723238 Cattell 2013, http://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/how-big-data-can-revolutionize-pharmaceu-tical-r-and-d239 Cattell 2013, http://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/how-big-data-can-revolutionize-pharmaceu-tical-r-and-d240 https://investor.fitbit.com/press/press-releases/press-release-details/2016/Fitbit-and-Fitabase-Innovate-Health-Research-Practices-to-Enable-Re-al-Time-Continuous-Measurement-Better-Participant-Engagement-and-Innovative-Study-Design/default.aspx241 http://www.bloomberg.com/news/articles/2015-09-14/big-pharma-hands-out-fitbits-to-collect-better-personal-data242 http://www.wareable.com/saves-the-day/what-is-apple-researchkit-iphone-watch-everything-you-need-to-know-931243 http://www.hopkinsmedicine.org/epiwatch244 http://www.euroforum.de/healthcare/review-2014/

in time and presents a new way of marketing. 238

Second, real-time monitoring and the employment of connected devices allows for quick reactions to issues arising during trials. For instance, drug-safety signals can be processed and reacted to much faster.239 With wearable technology becoming more affordable and delivering more accurate data, there has been a movement towards integrating connected devices into the traditional clinical trial setting. This allows researchers to design innovative study protocols that do not exclusively rely on self-reported data, which could be corrupted by human biases and other measurement errors.240 In 2015, wearables were being employed in at least 299 clinical trials.241 The roll-out of the Apple ResearchKit, for instance, has empowered researchers and developers to build many health monitoring applications for the iPhone and the Apple Watch. ‘Sleephealth’, powered by IBM Watson Health Cloud, monitors sleep quality and its effects on health, productivity and alertness.242 ‘EpiWatch’, built by John Hopkins researchers, is an app for Apple Watch that helps epilepsy patients manage their seizures and medications as well as corresponding triggers and side effects.243 Users are given the option of sharing their anonymized data with researchers.

One could argue that one thousand chronic patients with smartphones are the cheapest real world medical research source, making such projects immensely interesting.244 However, the validity and reliability of the data collected is still an important issue when it comes


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to wearable technology. Biases and human errors might be mitigated, but sleep monitoring devices that mistake non-trivial awakenings for asthma-induced awakenings can be problematic, to name one example.245 In addition, FitBit proclaims that their wearable “may be able to improve compliance rates among research participants”, referring to a small-scale study which showed that the device was worn on nearly all intervention days.246

Lastly, suboptimal-adherence in outpatient clinical trials could be addressed with promising technology that promotes medication adherence.

It is important to note that RCTs have never monopolized knowledge production in the sense that they have pushed observational studies completely out of the picture247. In contrast to RCTs, non-experimental approaches like observational studies present strong external validity and can succeed in proving clinical effectiveness under conditions outside the confines of controlled trials. However, they generally fail to generate evidence capable of warranting a causal link between studied variables, owing to their non-randomized nature. Despite these obvious drawbacks, observational studies are generally seen as complementary to RCTs. The official NICE guide proclaims that “data from non-randomised studies may be required to supplement RCT data”.248 With data accumulating and controlled registry studies with 100s of millions of patients becoming feasible, different positions exist on how the relationship between RCTs and observational studies might change. The following paragraphs examine two possible roles for big data studies to change research performed under the principles of EBM: serving as a guide to steer RCTs into promising directions, or serving as standalone methods of investigating and proving what actually works in the real world.

245 http://www.appliedclinicaltrialsonline.com/wearables-clinical-trials-active-interest?pageID=2246 https://investor.fitbit.com/press/press-releases/press-release-details/2016/Fitbit-and-Fitabase-Innovate-Health-Research-Practices-to-Enable-Re-al-Time-Continuous-Measurement-Better-Participant-Engagement-and-Innovative-Study-Design/default.aspx247 Bothwell 2016, http://www.nejm.org/doi/full/10.1056/NEJMms1604593248 https://www.nice.org.uk/process/pmg9/chapter/the-reference-case249 Sim 2016, http://www.ncbi.nlm.nih.gov/pubmed/26809201250 Frankovic 2011, http://www.nejm.org.eaccess.ub.tum.de/doi/full/10.1056/NEJMp1108726

2.4.2 Guiding RCTs: Generating Promising Hypotheses / Quickly Testing Hypotheses

Besides the mutual goal of generating evidence, the traditional EBM approach and big data approaches differ greatly in the mechanisms by which they produce this evidence: in EBM, a hypothesis lays the groundwork for an RCT in which data is acquired to prove or disprove the hypothesis, the result being internally valid evidence for a causal link present under test-conditions.

In contrast, a purely data-driven approach values raw observations over a priori hypothesized relations. The strength of data-mining and deep learning algorithms lies in the recognition of patterns – patterns that might go undetected by human eyes. It follows the principle that “more data are better than better data”. The result is externally valid, precise evidence for correlations present under real-world conditions. Whether the result is true or affected by bias cannot be concluded.249

A practical example puts the two approaches into context: In the case of a young systemic lupus erythematosus (SLE) patient with nephrotic-range proteinuria and pancreatitis, clinicians based their decision to give anticoagulation medicines on the results of an institution-wide search in the EMR database which showed a correlation between the complications and an increased risk for thrombosis. With a lack of RCTs in the area of pediatrics to base their decision on, EMR data was used to guide real-time clinical decisions.250 Thus, a hypothesis derived from experience was quickly tested and indeed a correlation was found. Statistical methods like ‘resampling’ can validate a relation in such a case and ensure that it really exists.

This is not to say that a causal relation between (1) nephrotic-range proteinuria and pancreatitis and (2) an increased risk for thrombosis in SLE patients


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was proven, but it suggests the role that big data can take: rapidly assessing the (external) validity of a hypothesis in real life scenarios, in case a first assumption exists, or simply discovering correlations that could make powerful new hypotheses, in case no prior assumptions exist. Applying such filters in the process of hypotheses-choosing can allow RCTs to focus on the most promising hypotheses and thereby increase the probability of success. In case of the SLE patient, the hypothesis based on the existing correlation between (1) and (2) could later be subjected to rigorous testing under controlled conditions in a clinical trial.

Pfizer is taking a similar approach with its Precision Medicine Analytics Ecosystem by first scanning for patterns in data from hundreds of millions of EMRs, guiding the researchers to a new hypothesis. A clinical trial database as well as a genomic database are then consulted to design a more focused clinical trial. The open source data management system ‘tranSMART’ is used to connect the three types of data. Following this regimen in search of a lung cancer drug specifically targeted at patients with an ALK mutation, Pfizer successfully developed the drug Xalkori in 2011. In the words of Pfizer CIO Jeff Keisling: “Had this compound been tested against a broad spectrum of lung cancer patients, it likely would not have been found to be effective. With this analytics-based approach, it was found to be very effective, but we had to be able to identify a subset of cancer patients with a specific gene mutation who previously did not have this treatment option”.251 Big data can also assume this guiding role outside of the treatment-related area and involve a great variety of sources. For instance, aggregated search data from large numbers of Internet users can deliver insight into previously unknown ADEs. If queries for a given drug correlate with searches for information about certain symptoms, a potential ADE has been found. Clinical trials could then validate the correlations found using this low-cost method. 252

251 http://www.informationweek.com/strategic-cio/executive-insights-and-innovation/pfizer-connects-dots-to-deliver-better-treatments/d/d-id/1141527252 Yom-Tov 2013, http://www.jmir.org/2013/6/e124/253 Kovesdy 2012, http://www.ncbi.nlm.nih.gov/pubmed/22364796254 Frakt 2016, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4758464/255 Sim 2016, http://www.ncbi.nlm.nih.gov/pubmed/26809201256 Rönicke 2015, http://www.strategyand.pwc.com/reports/revitalizing-pharmaceutical-rd

2.4.3 Complementing RCTs

The role of big data in EBM as a complement to RCTs has been proposed by many. They either proclaim that all should be considered “as pieces of the same puzzle, with all of them being able to provide useful information and increase our knowledge about a subject, but without any of them being infallible” 253, that big data’s strength lies in “its potential to see what RCTs won’t, thereby improving care in ways RCTs can’t”254 or that “evidence-based medicine needs the computational power of big data, and big data need the epistemological rigor of EBM” 255.

Strategy& proposes a tangible example in which RCTs and big data are fused together. They argue that a new kind of R&D model with real-world evidence and RCTs working hand in hand could shorten launch cycles by approximately five years and reduce R&D investments per product by approximately 60 percent. The model focuses on mitigating late-stage product failure by collecting real-world data in earlier stages. As real-world evidence (RWE) provides a means to prove clinical effectiveness and the major reason for late-stage attrition is a lack thereof, potential failures can be filtered out early on. In this scenario, the accumulation of RWE begins after the proof of concept in phase IIa and is accompanied by RCTs before and after. The model is based on the critical assumption of a sufficiently proven safety profile so that evaluations at such early stages in real-world environments are possible.256

Any scenario that incorporates big data as the main tool to demonstrate treatment effectiveness must inevitably deal with the disparity between correlation and causation. Some argue that causation might indeed be found without clinical trials through the careful selection of observational research designs and the deployment of statistical methods to minimize confounding effects. A comparative effectiveness study that investigated the outcomes in 80,000 patients (e.g. mortality) of


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medications for type 2 diabetes increased the likelihood that the found associations were in fact causal through the use of falsification tests. Other statistical methods that could be applied to minimize the effect of confounders are random effects models, bootstrap estimation of coefficients, or an instrumental variables approach. However, finding causation in these study designs is only possible in certain cases and one should not view big data studies as a general purpose tool for inferring causation. Nonetheless, it is an important area to investigate since techniques that could successfully separate the signal from the noise would be invaluable.

2.4.4 What Kind of Impact will Ubiquitous Computing and Wearables (Quantified Self) Have on the Way Medical Data are Collected in the Future?

When the smartphone revolution started in 2007 (and even slightly beforehand with powerful MP3-players), something interesting happened to the electronics market: suddenly, there was a market for mass production of sensors. Not only such sensors as are already found in modern smartphones, like accelerometers, compasses, magnetic sensors or air pressure sensors, but also add-ons made for a specific purpose which use a smartphone as their processing platform. So, instead of creating a costly sensor product that has its own CPU, connectivity and network platform, a manufacturer can simply add bluetooth, create an app and be done with it.

The first time a large manufacturer used this was Nike with their Nike+ sensor and app that allows users to track their running efforts. There was a small sensor pod that would be attached to an iPod or an iPhone and put into a small compartment in a Nike running shoe. Then, when you finished your morning jog, you could download your movement data and see exactly how many miles you ran and how many calories you burned. Naturally, in later years these functionalities have moved into the smartphones themselves, so that many of them now offer native step tracking, with some adding external options like weight tracking or heart rate monitoring.

Fitness tracking has become a mature market, with over

257 https://www.heise.de/newsticker/meldung/Umfrage-Jeder-Vierte-nutzt-Gesundheits-Apps-und-Fitness-Armband-3339518.html

a quarter of internet users wearing a device during their sports routine or even during regular walks257. There are, however, other areas where personal monitoring has become more common. There are internet-enabled personal scales which have been for sale in Apple stores for a long time. There are myriads of different sleep tracking devices which promise better sleep and better wake-up timing. There are insulin and cortisol trackers, wearable ECG monitors to track your body fitness, portable breathalyzers to measure alcohol intake and processing, and smartphone-connected blood pressure monitors.

Furthermore, services like 23andMe or uBiome allow you to analyze your genome and that of your microbiome; and other external such services exist. And then of course there are database apps that allow you to enter measured data yourself, complementing the sensory data with varied other data points about calorie intake, sleeping times, activity, arousal and mood levels, and general mental states.

Given the speed of technological change and the frequency with which consumer devices arrive on the market, it is safe to assume that more such devices and services will appear in the future.

Data protection already plays an important role in the public discussion about these quantified-self data. On the one hand, insurance companies would love to incorporate fitness levels into their health insurance and life insurance policies, or even making them conditional on certain fitness goals. On the other hand, customers have to be protected from unwillingly or unwittingly giving out these data, most of which are stored on American companies’ servers.

All of these data are of course medically relevant, both in a clinical setting and in a research setting. Imagine a doctor getting access to the fitness data of a patient and being able to examine their general condition very quickly. Or a heart patient who can prove a condition they have simply by bringing long-term EKG data with them. Or a patient with a previously-unknown allergy that could be detected from their cortisol levels. Or even as a diagnostic tool, where


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in the future conditions might be detected simply because they are visible in the data that is present.

At the same time, scientists doing a study on a particular condition might look for patients with a specific marker or a specific pattern of data, simply to find suitable and relevant participants. Tying in these previous measurements with in-study measurements could complement the picture we have of a participant’s development far better than was possible just a few years ago.

And what about giving study participants the necessary sensors to take part in a study? Instead of taking cortisol measurements at the beginning and ending of a fitness study, why not have one each day, or each hour even? With the availability of low-cost, easy-to-use sensory equipment, the cost can be decreased while at the same time increasing the robustness of measurements, leading to much more robust outcomes.

In the last paragraphs, we have only talked about singular data lines. However, many people who practice quantified self do not simply measure one aspect of their lives, they collect data on many different aspects. Correlating events in their life with data series on calorie intake or mood levels allows them to form a very accurate picture of themselves.

The same could be true for medical analysis. Instead of just collecting data that is relevant to a study at hand, why not collect many more different data series, even though they might not be immediately useable for a specific study? Since many devices offer multiple tracking options, from a cost perspective, nothing changes. For the participant and the examiner, nothing changes. Yet the collected data might become relevant when cross-examined against other participants’ data or even other study data, allowing post-hoc analysis of previously unknown aspects of a patient’s status. As we have seen in other chapters of this paper, more data can be used to find out more things, so it seems prudent to collect more, rather than less. In fact, given the trajectory of adoption of personal tracking devices, many more people will do so anyway.

Personal data tracking and the quantified-self

movement have lead to a multitude of available data about individuals. This is aided by the wide availability of consumer devices that allow tracking of various data, including movement and body activity, as well as chemical and genetic data. More people will use those devices in the future, and more such devices will be available, possibly even converged into regular smartphones. It would be wasteful to not use these data in medical and scientific contexts. At the same time, the availability of simple-to-use measuring devices will make scientific data collection easier, less costly, and give it much wider coverage than was possible before.


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2.5The Internet

of Healthy Things(IOHT)

It is very possible that ... one machine would suffice to solve all the problems that are demanded of it from the whole country. - Sir Charles

Galton Darwin, 1946

Computing technology has made phenomenal advances in its seventy years of existence258. From the humble beginnings of the mainframe era, where each computer was weighed in tons, through the mini- and microcomputer and the personal computer up to the world of smartphones and smart devices we live in today, the number of computers in the world has grown exponentially. While in 1996 there were an estimated 275 million personal computers in the world259, only 20 years later more than 400 million new smartphones are shipped each quarter260. At the same time, computers have gotten smaller, more energy efficient and more powerful. This, of course, contributed to their proliferation. It is thus not hard to extend that trend into the future to predict that soon, there will be enough computers in the world that we can put one into each device that we own. This is what is known as The Internet of Things (IoT), and while it is not

258 We use the date of dedication of the first electronic computer, ENIAC, on February 15th, 1946, as a reference. Note that single-chip silicon CPUs appeared on the market only in 1971.259 http://stats.areppim.com/stats/stats_pcxfcst.htm260 https://en.wikipedia.org/wiki/Mobile_operating_system#World-Wide_Share_or_Shipments

clearly defined what exactly will be part of it, we can expect that such smart, connected devices will make our lives easier and more comfortable, allow better customization of every aspect of private and work life and make many tasks more efficient, accurate and economical. They will also allow insights into many aspects of life that are hidden from analysis, simply because they cannot be accurately measured right now.

Imagine that there will be computers in everything, from hairdryers to light bulbs, from books to toys, from clothes to accessories, from furniture to kitchen devices, from cars to bikes to shoes. Not only are there a myriad of applications, there is also a wealth of data that can be collected. How long did you use your electronic devices? How long did you read and play, and what did you do? What clothes did you wear, and where did you go with them?


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2.5 The Internet of Healthy Things (IOHT)

What, then, is the Internet of Healthy Things? It is the application of the concepts in IoT to health applications, with the basic question: what devices could be made more efficient, accurate and safe by adding connection and computing to them? Devices in this context does not simply mean electrical devices, but all things we interact with in regards to health questions.

This is not science fiction, but has already started. Some manufacturers build smart pill boxes that dispense medication only on specific dates and monitor whether the medication was taken out of the box. Fitness tracking has been added to all recent smartphones, so each user can track how far they walked or cycled on a given day261. Blood sugar testing devices have been fitted with bluetooth connections for easier display and tracking of blood sugar levels on smartphones.

From the graph above, it is clear that this trend will continue. But what applications will we see?

261 https://www.heise.de/newsticker/meldung/Umfrage-Jeder-Vierte-nutzt-Gesundheits-Apps-und-Fitness-Armband-3339518.html


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2.6Augmented Reality:

An Extraordinary Evolution of

Technology Tools with Limitless Applications

2.6.1 Introduction

Augmented reality (AR) has become a hot topic over the last few years. In this review we look at the principle and uses of AR in the medical world, with some examples of companies and research groups using and investigating AR today.

In the ever-increasing world of technology there has been a push to integrate what is seen virtually with our reality - that is, virtually rendered262 images placed within our everyday view of our environment. This is the principle of augmented reality (AR). Unlike virtual reality (VR), which is a totally immersive artificial environment, AR allows the use to see his/her environment and focuses on supplementing it with images and information.

Of late, since around 2015, with the introduction of Microsoft’s HoloLens there has been vast amounts of interest and new applications into augmented reality. Although the buzz has come from Microsoft’s media approach, there are companies that started before Microsoft did. One company of note is Meta. They released in 2013 their Meta1 AR glasses and thus were one of the first to have a fully functional development AR glass system. Although when hearing the term augmented reality one thinks of HoloLens and/or Meta and thus AR glasses, the truth is that AR has been around for many years and does not only include projection on glasses. In reality, as has been stated, AR is the adding of rendered images into our environment with the use of any display or projecting

262 To render is the process of creating an image from either a 2D or 3D model263 Steven Feiner, Blair MacIntyre, Dorée Seligmann, Knowledge-based Augmented reality for Maintenance Assistance (KARMA) Columbia University Computer Graphics and User Interfaces Lab, http://monet.cs.columbia.edu/projects/karma/karma.html264 Virtual Reality: Patent Landscape Analysis, LexInnova 2015, http://www.lex-innova.com/resources-reports/?id=39.

device. This means that AR can and is being done with computers, digital projectors, mobile devices such as tablets and smart phones, and yes, AR glasses.

As has already been said, AR is not a new idea, it is only now that technology has caught up to the conceptual idea. The first mention of AR was in 1901 by L. Frank Baum who proposed the idea of wearable displays or spectacles that overlaid information onto real life. It was only in 1992 that Steven Feiner et. al263. released the first major publication on AR known at that time as KARMA (Knowledge-based Augmented Reality Maintenance Assistant). From here the uses of AR have developed exponentially to include the number of applications we have today. The purpose of this white paper is to provide an outline of the current and future direction AR will take in the medical world and its future market.

2.6.2 Market and Pharmaceutical Marketing

Market research released by LexInnova264 in 2015 predicts that the virtual/augmented reality market will rise from $93.21 billion in 2013 to $279.27 billion in 2018 and further to $470.86 billion by 2020. This large increase in the market is due to hardware and software companies entering the field of 3D and 4D technology. These companies include Microsoft, Magic Leap, Facebook, Meta and Unity3D, to name a few. With respect to the medical field, there is a desire to strengthen and introduce new fields in medical education, surgical simulators, telepresence surgery, multi-image and complex data visualization, rehabilitation, and surgical navigation. As of 2015


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2.6 Augmented Reality: An Extraordinary Evolution of Technology Tools

there were 630 and 369 filed patents for the use of VR/AR for medical application in both the categories of Medical Devices and Identification respectively.

Pharmaceutical companies have also taken to AR applications on tablet devices to enhance their marketing strategies. Take Merck’s Spectroquant® Prove 600 Augmented Reality App265, for example. With the use of a tablet device, the sales person can show a 3D representation of the product popping out of a 2D brochure. The customer gets a more in-depth view of the product and the apparent feeling of being able to physically interact with the product. This type of marketing application will become the standard for different pharmaceutical companies. By developing an app that reads a QR code on a brochure and displays a 3D model of the product, the sales process is enhanced on a mental and physical level for the customer.

2.6.3 Medical Education with Augmented Reality

As the demand for AR and VR techniques has increased, so has the number of software apps used to build these desired techniques. These apps have enabled different groups in research and industry to change the way in which medicine, anatomy, and even surgery can be taught. Some companies leading the way are Medical realities, which uses Google Glass to record and live stream surgical procedures to students in 32 different countries; ARnatomy, which uses QR codes as markers to flash medical names of different anatomical parts; and Vipaar, a group that allows a student surgeon to wear google glasses and see the hands of an experienced surgeon projected onto the patient or medical image. Since the prototype release of Microsoft HoloLens, Case Western Reserve University, Cleveland, started working on an AR means of visualising the human body and different anatomical structures. Furthermore, it allows the wearer of the HoloLens to walk around the projected hologram and interact with it while learning.

Medical education is also being brought to the training of surgeons. In the past, a student would train on silicone-

265 Merck, Spectroquant® Prove 600 Augmented Reality App , http://www.merckmillipore.com/DE/de/support/mobile-apps/spectro-quant-prove-600-augmented-reality/f92b.qB.T6YAAAFT7OUR91.D,nav

based phantoms without anatomical detail. This becomes more difficult in today’s world as the surgical approaches are less invasive due to the higher demand for endoscopic procedures. This led to the training of surgeons with the field of haptic AR. In such an approach the trainee holds a device that provides the same haptic feedback as he/she would have while undertaking a surgical procedure. At the same time, the trainee is seeing an image on a computer monitor of an artificially rendered body part acting as the area being operated on. In this way a silicon phantom can give the visual and physical impression of operating on a human and thus enhance the training of the student. Examples of such devices and companies include Virtual Botox by Allergan, hapTEL virtual system by Kings College London, and the Tempo surgical simulator by Voxel-Man.

2.6.4 Augmented Reality within the Hospital/Private Practice As wearable AR glasses decrease in size and increase possible application, their us in Hospitals and private practices will become an everyday event. Today, there are a few groups investigating the use of these applications. One company to note is Streye. Based in both the United States of America and Spain, the company offers a solution to view patient data on the go. By connecting to data bases holding patient information, the doctor can call up data anywhere within the hospital. Furthermore, this system offers the ability to live stream video information from medical devices such as endoscopes directly to the AR glasses the doctor is wearing at the time. Further applications have been investigated and are being developed. One of which is the ability, with the aid of patient data and simulated images, to explain the intended procedure to the patient. This way the patient will have a better understanding of how the anatomical structure it should look, the patient’s problem, and the medical/surgical approach that will be done to correct this.


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2.6 Augmented Reality: An Extraordinary Evolution of Technology Tools

2.6.5 Augmented Reality within the Surgical Theatre

Augmented reality has already been playing a vital role within our surgical room, unbeknownst to users. In previous years before technology caught up with the application, AR would have been undertaken by using a digital projector to project a medical image onto the surgical field. Today, with the use of image guided techniques and computer monitors, surgeons are able to render surgical targets on top of the patients’ previously acquired images for better surgical navigation. The work of Meola et. al.266 2016 shows that in the field of neurosurgery “AR is a reliable and versatile tool when performing minimally invasive approaches in a wide range of neurosurgical diseases”. That is, the doctor has a better understanding of where the trauma or tumor is located.

Today, technology has aided in using the true power of augmented reality. Some companies have started developing especially for this field. One company, Mbits, uses an iPad to video the surgical site while the application renders and models the internal organs. By doing so the surgeon gains more information regarding the anatomical structures, the relation to them and the surgical instrument and the intended surgical target. Augmedics is another company using AR for surgical localization. Unlike Mbits, Augmedics uses AR glasses which give the surgeon the ability to overlay segmented anatomical structures over the surgical site. This overlay allows the doctor to see what lies under the skin and can navigate to the surgical target more accurately and less invasively. Another reason for AR’s favorability in surgical interventions is the ability to show and analyze complex data within the surgical room, and to overlay this on the patient while he/she is on the surgical table. Other applications that are being developed include interaction with computer software via hand gestures and the Microsoft Kinect Infra-red camera267.

266 Meola A, Cutolo F, Carbone M, Cagnazzo F, Ferrari M, Ferrari V. Augmented reality in neurosurgery: a systematic review. Neurosurgical review. 2016:1-12.267 Jacob MG, Wachs JP, Packer RA. Hand-gesture-based sterile interface for the operating room using contextual cues for the navigation of radiological images. Journal of the American Medical Informatics Association : JAMIA. 2013;20(e1):e183-e186. doi:10.1136/amiajnl-2012-001212.

2.6.6 Future Uses of AR

As can be seen, although AR was a conceptual idea almost 115 years ago, it is only now, as technology has caught up, that it is pushing the medical technology field forward. The available applications, although limited, are well spread out, and include fields of medical marketing, medical education, day-to-day clinical routine, surgical training and surgical intervention. It goes without saying that because of the power of AR, as the technology increases, so will the number of AR applications and companies. One major technology that is in the focus for future application is smart glass or AR glass. This technology will allow a hands-free solution for visualizing and interacting with rendered objects supplemented within our everyday environment. This technology is being focused on by many companies including Microsoft, Meta, Epson, Sony, Samsung, Google, Magic leap, Atheen Labs and more. Smart glass devices will be the commonly used hardware in a clinical environment.


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2.7Competitor

Analysis

2.7.1 What are Other Companies Doing and How Successful are They?

[Google, IBM, SAP etc.] have a lot of capabilities around […] analyzing big data […]. But what they miss is the medical knowledge, the

understanding of biology. They can’t ask the right questions. They can program but they don’t know what to program.

Severin Schwan, CEO Roche Group268

Given the unequal distribution in the market when it comes to medical and data analytics knowledge, the growing number of Joint Ventures (JV) between the pharmaceutical industry and technology companies seems unsurprising. Google and Qualcomm have become partners of J&J, GSK, Sanofi and Novartis in efforts to create virtual coaches for post-surgery patients269, develop comprehensive IT solutions to empower diabetes type 2 patients270, and create smart devices like internet-connected inhalers for COPD patients271. They all seek to compensate for missing digital capabilities and expertise in data collection and analysis. These capabilities are of great significance for big data projects as well. Looking at the past years, there is no shortage of pharmaceutical companies collaborating with companies outside and inside the Life Science industry to build up analytics capabilities, establish access to new data sources or conduct big data projects. This section gives a description of exemplary projects of several pharmaceutical companies with big data being the key ingredient. The analysis is based on openly disclosed information. Although it is

268 http://asia.nikkei.com/Business/Executive-Lounge/Here-s-where-drug-makers-are-heading269 https://www-03.ibm.com/press/us/en/pressrelease/46582.wss270 http://www.heise.de/newsticker/meldung/Google-Unternehmen-und-Sanofi-wollen-Diabetikern-helfen-3318656.html271 https://www.novartis.com/news/media-releases/novartis-pharmaceuticals-collaborates-qualcomm-digital-innovation-breezhalertm272 Translated from German quote, available at http://tablet.fuw.ch/article/roche-ist-vorreiter-bei-big-data/273 http://investors.foundationmedicine.com/releasedetail.cfm?releaseid=905240

not possible to conclude from this information how highly big data ranks in each individual company’s business, it indicates that big data is an important topic for many top companies in the industry, with large investments flowing and success stories emerging.

2.7.2 Roche

We will accelerate R&D, new drugs will be introduced to market earlier and cancer patients will live much longer.

Daniel O’Day, CEO Roche Pharmaceuticals 272

Roche has formed several strategic alliances in order to access more patient data than was internally available. The leader in oncology research has invested $1 bn to acquire a majority stake in ‘Foundation Medicine’, a molecular information company specializing in the sequencing of cancer tissue273. A deep understanding of the molecular structure of specific cancers can help Roche in identifying novel drug targets, match patients with suitable clinical trials and enable precision medicine. A similar project feeding Roche’s pool of data with more data points is the collaboration with ‘Flatiron’. Roche plans to use the real-world patient data on cancer treatments that Flatiron collects to accelerate clinical trials and advance personalized medicine. As CEO of Roche Pharmaceuticals, Daniel O’Day stated that the project aims at understanding how drugs react in patients in the real world and can


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be classified as a “long-term strategic investment”274.

In order to build capabilities to analyze big data, the acquisition of ‘Bina’ played an important role in the past year275. Bina’s Genomic Management Solution presented a valuable tool for Roche in managing and processing next generation sequencing data.

In 2015, Roche’s daughter company Genentech initiated a big data project in collaboration with 23andMe276. Data from 12.000 Parkinson’s patients is being used for a study to find genetic links to the disease to assist in drug development.

“Roche considers Big Data as a huge opportunity”, “is currently working on new projects related to Big Data and is in the process of launching pilot projects”, according to Isabelle Vitali, Head of Innovation and Alliances Development.277 Roche’s growing interest in strategic alliances with technology companies, of which “[w]e will probably see more […] in the future”278 as stated by Roche’s CEO, clearly reflects this view. In general, Roche is pursuing big data-related work in four different areas:

1. Social analysis, analyzing data from online patient platforms

2. Data mining, analyzing unstructured data using statistical methods (e.g. for predictive health purposes)

3. Data warehouses 4. Processing data from connected objects

274 http://bits.blogs.nytimes.com/2016/01/06/roche-leads-a-175-million-investment-in-flatiron-health/?_r=0275 http://sequencing.roche.com/news---media/press-releases/roche-acquires-bina-technologies-and-enters-the-genomics-informa.html276 http://www.forbes.com/sites/matthewherper/2015/01/06/surprise-with-60-million-genentech-deal-23andme-has-a-business-plan/#323700af7927277 http://healthcaredatainstitute.com/2016/07/19/at-roche-big-data-is-about-revealing-the-invisible/278 http://asia.nikkei.com/Business/Executive-Lounge/Here-s-where-drug-makers-are-heading279 http://venturebeat.com/2015/01/14/23andme-has-signed-12-other-genetic-data-partnerships-beyond-pfizer-and-genentech/280 https://www.genomeweb.com/clinical-genomics/23andme-pfizer-launch-inflammatory-bowel-disease-genetics-study281 http://files.shareholder.com/downloads/AMDA-23Y63R/2971088260x0x716636/a441869c-74f1-4229-9dad-4d7eb48b0b34/FMI_News_2014_1_6_General_Releases.pdf282 https://www.nibr.com/our-research/disease-areas/oncology283 http://www.covance.com/content/dam/covance/pdf/data-warehouse.pdf

2.7.3 Pfizer

The previous section (see section 2.2.1 Future of EBM) already included two cases of Pfizer’s efforts in the area of big data: Pfizer’s Precision Medicine Analytics Ecosystem, which serves as a prime example for quick hypothesis testing and the integration and analysis of diverse and large amounts of data, and the large-scale 23andMe depression study. In addition, Pfizer has initiated two more projects based on customer data of 23andMe users: A study using the data of 5.000 Lupus patients279 and another study involving 10.000 23andMe users with inflammatory bowel disease280. Their goal is to find new associations between genetic markers.

2.7.4 Novartis Just like Roche, Novartis has set foot into the world of tech-collaborations under the auspices of big data. ‘Foundation Medicine’ has been a partner of Novartis since 2011, providing valuable molecular information and genomic profiling analytics which has found its way into Novartis oncology clinical trials281. The result of a joint project with MIT and Harvard, also initiated with the goal of growing Novartis’ access to genetic data in mind, the ‘Cancer Cell Line Encyclopedia’ comprises detailed genetic characterization of 1.000 human cancer cell lines282. In order to better handle data integration and analysis for both preclinical and clinical research, a collaboration with ‘Covance’ was initiated in 2014 to develop a clinical data warehouse283. For an improved integration and analysis of diverse next-generation-sequencing data from external organizations, Novartis uses a solution


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called ‘MapR’284. Both the data warehouse and the MapR solution contribute to the acceleration of R&D.

Novartis is increasingly building cross-sectional teams, including biologists, chemists, clinicians, and data scientists. Since 2013, Novartis has been presenting a success story of their big data efforts on the official Novartis website285. The Novartis Institutes for BioMedical Research (NIBR) uncovered the cause of a rare kidney disease from great amounts of genomic data: previously undetected mutations in the gene LMX1B caused focal segmental glomerulosclerosis which in turn affected the kidneys’ filtering system. “Big data was the game changer”, according to Joseph Szustakowski, head of Bioinformatics in Biomarker Development at NIBR.

2.7.5 Merck

Merck’s project to speed up the process of vaccine manufacturing makes a case for the usefulness of big data analytics in areas other than R&D286. Particularly low yield rates resulting in high costs in the production of certain vaccines prompted Merck to investigate the underlying causes. The ‘Hortonworks Data Platform’ was used to meet the previously unsurmountable challenges of siloed data and high costs involved in testing hypotheses. Data from disparate sources, namely (1) a process system that tags and tracks each vaccine-batch, (2) a maintenance system that presents calibration settings and (3) a building management system that measures air pressure, temperature, humidity levels and flow rates, were aligned to create a fruitful data environment for the analysis of the root cause. This procedure turned out to be much faster and more effective than traditional spreadsheet-based analysis: 5.5 million batch-to-batch comparisons revealed that certain characteristics in the fermentation phase were strongly linked to low yield rates. Using this method, Merck can test different hypotheses

284 https://www.mapr.com/resources/novartis-relies-mapr-flexible-big-data-solutions-drug-discovery285 https://www.novartis.com/stories/discovery/surfing-wave-big-data-analytics286 http://www.informationweek.com/strategic-cio/executive-insights-and-innovation/merck-optimizes-manufacturing-with-big-data-analyt-ics/d/d-id/1127901287 http://hortonworks.com/blog/hdp-for-manufacturing-yield-optimization-in-pharma/288 https://ieondemand.com/presentations/building-big-data-capabilities-at-mylan289 http://numedii.com/numedii-allergan-collaboration-psoriasis/

in a quick and low-cost way287. Instead of directly testing all hypotheses in the costly lab environment, correlations in the data can be applied as a filter so that only promising hypotheses are passed on to the lab. For instance, the scientists’ intuition that ingredients might have been diluted at a certain process stage was disproved by the data. Thus, Merck was able to avoid an unnecessary lab test which would have demanded high investments of both money and time.

2.7.6 Mylan and Allergan

Not much information can be found on Mylan’s involvement in big data projects. However, the existence of a ‘Global Director of Enterprise Business Analytics Mylan Inc.’ who manages data scientists and is responsible for planning and executing big data strategy suggests that Mylan is at the very least building big data capabilities288.

Allergan formed a research alliance with ‘NuMedii’ at the end of 2015289. NuMedii offers predictive big data intelligence platform technology which provides Allergan with information on existing compounds which could be used in the treatment of psoriasis. Hundreds of millions of clinical data points are the basis of NuMedii’s predictive analytics engine that tries to identify new uses for existing drug compounds.


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Table 1a: Summary of categories of digital health applications and their current status of implementation in Germany. Further details can be found in the comprehensive table in the electronic appendix (continued on next pages).

Appendix


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Appendix

Table 1b: Summary of categories of digital health applications and their current status of implementation in Germany. Further details can be found in the comprehensive table in the electronic appendix .


84

Digital Transformation is in full swing whether we like it or not. It has already changed some of the industries for good and now is on the verge of percolating into the areas of healthcare, medicine, and life-sciences.

In this whitepaper, we have made clear that this is a market unlike any other. We have also shown the triggers and technology levers of business success and the layers of change where we can expect transformation to take place.

Often, technology alienates people and causes fear of being made redundant. We think that digital transformation in healthcare will on the contrary not deprive medicine of its workforce but will make this domain even more accessible. But the jobs will have changed at the end.

The core of this belief is the fact that in health, there is no diminishing marginal utility. This means, for every unit invested, we still gain utility. In most markets, this is not the case, because at some point they become saturated. Even if you have an infinite number of sport cars, you can only drive one at a time. Not so in healthcare where public systems might experience more and more financial pressure, but where we also see a huge self-pay potential that can be tapped in using digital methods. The literate and conscious consumer of healthcare services, the civilian scientists, will be able to make more choices as to how to live longer, how to feel better, how to sleep better, how to prevent diseases, etc.

As always, when there are big changes ahead, there is also the feeling of significant insecurity. We want to contribute with this whitepaper to get our readers looking somewhat more optimistically into the future of healthcare in terms of digital transformation. The move will be slowly but steadily in established markets and of course it will be international in nature. Company business models might be challenged already, jeopardized and if they cannot adapt they might be adapted.

It is not a question of whether individuals, companies, institutions etc. want join this transformation, it is rather the question of how they will do it. To us it is beyond doubt that healthcare is and will continue to be one of the most relevant global industries in terms of revenue and sheer broadness of goods and services

offered to even more customers. However we also have a humanitarian obligation to not leave behind those who are not at the cutting edge and forefront of technology due to socioeconomic status or location. No one in the long run can be excluded and therefore condemned to window shopping on the internet without being able to participate: personalized genome testing will at one point become the norm. Pharmaceutical companies will try more and more to become solution providers for affordable, accessible and accountable care. Sick funds and insurance companies are already in search of digital USPs that can be sold to customers. Prevention will have impact on life style in terms of functional food, 3D printed mass customized medication and personal dietary and health bots. Wearables will eventually evolve to imprintables transmitting data on personal levels of fitness and wellbeing from different body compartments. Augmented reality will help us target tumors and exercise operation procedures better. Old study data will be subject to data archeology with data scientists running at the forefront of reverse drug discovery out of hard drives rather than test tubes.

Professions will change and entire industry sectors must reinvent themselves, because value chains break down and take with them all those who rest on their laurels. In this sense, digital transformation in healthcare can also be looked at as a necessary evolutionary process, a process of creative destruction where old domain knowledge forms only one part of the equation and agile, unpretentious transdisciplinary approaches will make the differences.

This whitepaper should enable you to get oriented towards where you want to go tomorrow; for the future is not something that is done, it is imagined and made by us. Today for tomorrow, day by day.

Concluding remarks

Tobias D. Gantner, MD, PhD, MBA, LL. M.Founder & CEO HealthCare Futurists GmbH

Digital Transformation in Healthcare is a sensitive and comprehensive enterprise; it does not mean switching from fax to e-mail. It is an irreversible process of change that has at its core the intelligent and secure connection of data-producing devices, data-weighing algorithms, immersed and educated healthcare consumers, and well-trained healthcare professionals who know how to act on this intelligence responsibly and with participatory transparency.

This is a transformation we need to be observant but not afraid of. In fact, the technology employed will put more time on our hands where no machine can ever replace us: Being human to other humans when they need it most.