Digital Healthcare

Healthcare Delivery is currently undergoing a global transformation – with

Digital Healthcare Technologies leading the way. Companies such as BT

Health, Blueprint Health, BUPA, Microsoft, Telefonica Digital and Rockhealth -

are all shaping novel and emerging Digital Healthcare Technologies - bringing

new and innovative business propositions to market.

Atlantic Force: Digital Healthcare

Next-Generation Social Enterprise (NGSE) Business Models

– are driving emerging Digital Healthcare service providers.

The Digital Social Enterprise is all about doing things better today

in order to deliver a better tomorrow. Digital Healthcare is driven

by rapid response to changing social conditions so that we can

create and maintain increased stakeholder value - and everyone

share in a brighter future for our stakeholders to enjoy today.….

Atlantic Force: Digital Healthcare Map

Value Pathways in Digital Healthcare

• One of the key obstacles to rolling out the Digital Healthcare Ecosystem is bio-medical

data availability, immediacy and liquidity - the flow of clinical data to every stakeholder -

including patients, clinical practitioners, service providers and fund holders. Many

stakeholders are now using “Big Data” methods to overcome this challenge, as part of

a modern data architecture. This section describes some example Digital Healthcare

use cases, a Digital Healthcare reference architecture and how “Big Data” methods

can resolve the risks, issues and problems caused by poor clinical data latency.

• In January 2013, McKinsey & Company published a report entitled “The ‘Big Data’

Revolution in Healthcare”. The report points out how big data is creating value in five

“new value pathways” allowing data to flow more freely between stakeholders. The

Diagram below is a summary of five of these new value pathway use cases and an

example of how “Big Data” can be used to address each use case. Examples are

taken from the Clinical Informatics Group at UC Irvine Health - many of their use

cases are described in the UCIH case study.

CASE STUDY 1: – Medical Analytics Digital Healthcare Value Pathways

Pathway Benefit “Big Data” Use Case

Patient Health

and Wellbeing

Patients can build stakeholder value

by taking an active role in their own

health, wellbeing and treatment,

including disease prevention.

Predictive Analytics: Heart patients weigh themselves at home

with scales that transmit data wirelessly to their health center.

Algorithms analyze the data and flag patterns that indicate a

high risk of readmission, alerting a physician.

Patient

Monitoring

Patients get the most timely and

appropriate diagnoses, treatment and

clinical intervention available.

Real-time Monitoring: Patient vital statistics are transmitted

from wireless sensors every minute. If vital signs cross certain

risk thresholds, staff can attend to the patient immediately.

Healthcare

Provisioning

Healthcare Provider capabilities

matched to the complexity of the

assignment— for instance, nurses or

physicians’ assistants performing

tasks that do not require a doctor.

Also the specific selection of the

provider with the best outcomes.

Historical EMR Analysis: Big Data reduces the cost to store

data on clinical operations, allowing longer retention of data on

staffing decisions and clinical outcomes. Analysis of this data

allows administrators to promote individuals and practices that

achieve the best results.

Patient Value

Proposition

Ensure cost-effectiveness of care

provision, such as tying Healthcare

Provider reimbursement to patient

outcomes, or eliminating fraud, waste,

or abuse in the system.

Medical Device Management: Biomedical devices stream geo-

location and biomedical sensor data to manage patient clinical

outcomes from medical equipment. The biomedical team know

where all the patients and equipment are, so they don’t waste

time searching for a location. Over time, determine the usage of

different biomedical devices, and use this information to make

rational decisions about when to repair or replace equipment.

Digital

Innovation

The identification of new therapies

and approaches to delivering care,

across all aspects of the system and

improving Medical Analytics engines

themselves.

Collaborative Research : Clinical Researchers attached to

hospitals can access patient data stored in Hadoop Cluster

“Big Data” Stores for discovery, then present the anonymous

sample data to their Internal Review Board for approval, without

ever having seen uniquely identifiable information.

CASE STUDY 1: – Medical Analytics Digital Healthcare Value Pathways

• Changing demographics and regulations are putting tremendous pressure on the

healthcare sector to make significant improvements in care quality, cost control,

clinical management, organizational efficiency and regulatory compliance. To stay

viable, it is paramount to effectively address issues such as missed and mis-

diagnosis, coding error, over / under treatment regimes, unnecessary procedures

and medications, insurance fraud, delayed diagnosis, lack of preventive health

screening and proactive health maintenance. To that end, better collaboration

across and beyond the organization with improved information sharing, and a

holistic approach to capture clinical insights across the organization are critical.

• In an environment prevalent with multiple unstructured data silos and traditional

analytics focused on structured data, healthcare organizations struggle to

harness 90% of their core data - which is mostly medical images, biomedical data

streams and unstructured free text found in clinical notes across multiple

operational domains. Connecting healthcare providers directly with patient data

reduces risk, errors and unnecessary treatments; thus enabling better

understanding of how delivery affects outcomes - and uncovering actionable

clinical insights in order that proactive and preventive measures decrease the

incidence of avoidable diseases.

Digital Healthcare Digital Healthcare

• Digital Healthcare is a cluster of new and emerging applications and technologies

that exploit digital, mobile and cloud platforms for treating and supporting patients.

The term is necessarily general as this novel and exciting Digital Healthcare

innovation approach is being applied to a very wide range of social and health

problems, ranging from monitoring patients in intensive care, general wards, in

convalescence or at home – to helping doctors make better and more accurate

diagnoses, improving drugs prescription and referral decisions for clinical treatment.

• Digital Healthcare has evolved from the need for more proactive and efficient

healthcare delivery, and seeks to offer new types of prevention and care at reduced

cost – using methods that are only possible thanks to sophisticated technology.

• Digital Healthcare Technologies – Bioinformatics and Medical Analytics. Novel

and emerging high-impact Biomedical Health Technologies such as Bioinformatics

and Medical Analytics are transforming the way that Healthcare Service Providers

can deliver Digital Healthcare globally – Digital Health Technology entrepreneurs,

investors and researchers becoming increasingly interested in and attracted to this

important and rapidly growing Life Sciences industry sector. Bioinformatics and

Medical Analytics utilises Big Data / Analytics to provide actionable Clinical insights.

Bioinformatics and Medical Analytics Digital Healthcare Technologies

• Healthcare is undergoing a global transformation – with Digital Healthcare

Technologies leading the way. Companies such as BT Health, Blueprint Health,

BUPA, Cisco, ElationEMR , Huawei, GE Healthcare, Microsoft, Telefonica Digital

and Rockhealth - are all developing novel and emerging Digital Healthcare

technologies - from Mobile Devices and Smart Apps to “Big Data” Analytics -

bringing new and exciting Digital Healthcare business propositions to market.

• Private Equity and Corporate Investment Funds are pouring seed-money and

Capital into Digital Health start-up ventures - in the hope of funding a “quick win”.

Applied Proteomics has just received an investment of $28 million from Genting

Berhad, Domain Associates and Vulcan Capital. The State of Essen in Germany

has recently invested 55m Euros on a SAP HANA Digital Health Proof-of-concept.

• Telefónica Digital is sponsoring research into Smart Wards with St. Thomas's

Hospital in London. At the Institute of Digital Healthcare, part of the Science City

Research Alliance, researchers are not only looking to develop biomedical

technologies, but to base this firmly on a pragmatic understanding of both the

benefits and limitations of integrating biomedical technologies within the existing

range of commercial Digital Healthcare products and services currently on offer.

Digital Healthcare Digital Healthcare Technologies

• Case Study 1 – HP Autonomy Medical Analytics. Changing healthcare service

provisioning, regulation and patient demographics are putting increasing pressure

on the healthcare industry to make significant improvements in care quality, cost

management, organizational efficiency and compliance. Priorities include the need

to address challenging issues such as misdiagnosis, coding error, over / under

treatment, unnecessary procedures and medications, fraud, delayed diagnosis,

lack of preventive screening and proactive health maintenance. Improved

collaboration within the organization with better information sharing, and a holistic

approach to capture and action medical insights across the organization are crucial

to success.

• Case Study 2 – Telefónica Digital was created as a Special Purpose Vehicle to lead

Telefónica’s transformation into an M2M / M2C / C2C Digital Services provider -

cloud computing / digital telecommunications value added network services

(VANS). Telefónica Digital is the vehicle for launch / bringing to market digital

products and services - which will help to improve the lives of customers by

leveraging the power of digital technology. This ranges from developing new

technologies for healthcare providers to communicate with other stakeholders, to

helping Healthcare Providers, Life Sciences businesses and government Health

Departments discover actionable clinical insights, address new opportunities,

improve operations, increase efficiency.

Case Studies Summary – Digital Healthcare Transformation Digital Healthcare Technologies

The Cone™ – Digital Healthcare

The Cone™ – Patient Model

The Cone™ - Patient Model – turning Biomedical Data Streams into Actionable Medical

Insights…

• Acute – (10%) Active Patient Monitoring – Alerts and Alarms • Chronic – (20%) Passive Monitoring – Biomedical Data Streaming • Casuals – (30%) Walk-in – Treat On-demand • Indifferent – (40%) See Annually – Health-check / Review

Electronic Medical Records (EMR)

The Cone™ - Patient Types

Acute - 10%

Chronic- 20%

Casuals - 30%

Indifferent - 40%

The Cone™ Patient

Biomedical Analytics

Actionable Medical Insights

Presentation

Clustering

Biomedical Profile Biomedical Epidemiology – Groups (Streams), Types (Segments)

Hybrid Cone – 3 Dimensions Biomedical Analytics

The Cone™ - Eight Primitives

Primitive Domain Function Product

Who ? People - Patient EMR SalesForce.com

What ? Event Appointment, Walk-in,

Referral, 1st Responders

and Emergency Services

Primary Care, GPs

Healthcare Provider

Hospitals, Clinics

Why ? Motivation Triage - Acute / Chronic Biomedical Analytics

Where ? Places - Location GIS / GPS / Analytics Geospatial Analytics

When ? Time / Date Procedure Biomedical Analytics

How ? Biomedical Data Streaming Medical Data Smart Devices / Apps

Mobile Platforms, IoT

Which ? Clinical Procedure Investigate, Diagnose,

Treatment, Follow-up

Nurse, Consultant

Via ? Referral Channel

Delivery Partner

Healthcare Service

Delivery, Procedure

Healthcare Provider

Hospitals, Clinics

The Cone™ – EIGHT PRIMITIVES

Event

Dimension

Party

Dimension Geographic

Dimension

Motivation

Dimension

Time

Dimension

Data

Dimension

Cone™

MEDIA

FACT

WHO ? WHAT ? WHERE ?

HOW ? WHEN ? WHY ?

• Indifferent

• Casuals

• Chronic

• Acute

• Temperature

• Breathing Rate

• Heart Rate

• Blood Pressure

• Blood Sugar

• Brain Activity

• Consultation

• Clinical Tests

• Diagnosis

• Treatment

• Appointment

• Attendance

• Phone Call

• Letter

• Location

• Attitude

• Movement

• Region / Country

• State / County

• City / Town

• Street / Building

• Postcode

• Person

• Organisation

Procedure

Dimension

WHICH ?

• Procedure

• Prescription

Channel

Dimension

VIA ?

• Channel / Partner

• Hospital / Clinic

Patient Data

Delivery Channel

Environment

Data

Subject

Location

Biomedical Data

Event

• Referral

• Walk-in

Motivation

Patient

Time / Date

Version 3 –

Healthcare

CASE STUDY 1: – HP Autonomy Medical Analytics - actionable insights from clinical data

• HP Healthcare Analytics delivers a robust and integrated set of core and healthcare industry specific capabilities which organises and interprets unstructured data in context - designed to harness this untapped clinical data and unlock actionable medical insights. This helps to improve care quality by connecting healthcare providers directly with their data through self-service analytics; providing intelligence for more accurate diagnoses so reducing errors, risk and unnecessary treatments; enabling better understanding of how delivery affects outcomes and uncovering insights for preventive measures to decrease the rate of avoidable diseases.

• Changing demographics and regulations are putting tremendous pressure on the healthcare industry to make significant improvements in care quality, cost management, organizational efficiency and compliance. To stay viable, it is paramount to effectively address issues such as misdiagnosis, coding error, over/under treatment, unnecessary procedures and medications, fraud, delayed diagnosis, lack of preventive screening and proactive health maintenance. To that end, better collaboration within the organization with improved information sharing, and a holistic approach to capture actionable insights across the organization becomes crucial.

• In an environment prevalent with multiple unstructured data silos and traditional analytics focused on structured data, healthcare organizations struggle to harness 90%* of their core data - which is mostly medical images, biomedical data streams and unstructured free text found in clinical notes across multiple operational domains. This rich and rapidly growing data asset containing significant biomedical intelligence supports actionable Clinical Insights..

CASE STUDY 1: – Medical Analytics Digital Healthcare Technologies

The Biomedical Cone™ Converting Data Streams into Actionable Insights

Salesforce

Anomaly 42

Cone

Unica

End User

BIG DATA

ANALYTICS

BIOMEDICAL DATA

Patient Monitoring

Platform

INTERVENTION

• Treatment

• Smart Apps

The Cone™ Patient

Biomedical Analytics

Actionable Medical Insights

Electronic Medical Records

(EMR)

• Geo-demographics

• Streaming

• Segmentation

• Households

PATIENT RECORDS

• Medical History

• Key Events

Insights

Insights Insights

Anomaly

42 Unica

Biomedical

Data Streaming

People, Places

and Events

Health

Campaigns

• Clinical and Biomedical Data

• Images – X-Ray, CTI, MRI

• Procedures and Interventions

• Prescriptions and Treatment

Social

Media

EXPERIAN

Mosaic

CASE STUDY 2: – Digital Healthcare SMAC – Smart, Mobile, Analytics, Cloud

• Digital Healthcare is a cluster of new and emerging applications and technologies that exploit digital, mobile, analytic and cloud platforms for treating and supporting patients. Digital Healthcare is necessarily generic as this novel and exciting Digital Healthcare innovation approach is being applied to a very wide range of social and health problems, ranging from monitoring patients in intensive care, general wards, in convalescence or at home – to helping doctors make better and more accurate diagnoses, improving drugs prescription and referral decisions for clinical treatment.

• Digital Healthcare has evolved from the need for more proactive and efficient healthcare delivery, and seeks to offer new types of prevention and care at reduced cost – using methods that are only possible thanks to sophisticated technology.

• Telefónica Digital is sponsoring research into Smart Wards with St. Thomas's Hospital in London. At the Institute of Digital Healthcare, part of the Science City Research Alliance, researchers are not only looking to develop new technologies, but to base this firmly on a pragmatic understanding of both the benefits and limitations of integration with commercial Digital Healthcare products which are currently on offer.

CASE STUDY 2: – SMAC Digital Healthcare Digital Healthcare Technologies

CASE STUDY 1: – Medical Analytics Data Science in Digital Healthcare

CASE STUDY 4: – Digital Healthcare in the Cloud

• Digital Healthcare is a cluster of new and emerging applications and technologies that exploit digital, mobile, analytic and cloud platforms for treating and supporting patients. Digital Healthcare is necessarily generic as this novel and exciting Digital Healthcare innovation approach is being applied to a very wide range of social and health problems, ranging from monitoring patients in intensive care, general wards, in convalescence or at home – to helping doctors make better and more accurate diagnoses, improving drugs prescription and referral decisions for clinical treatment.

• Digital Healthcare has evolved from the need for more proactive and efficient healthcare delivery, and seeks to offer new types of prevention and care at reduced cost – using methods that are only possible thanks to sophisticated technology.

• Telefónica Digital is sponsoring research into Smart Wards with St. Thomas's Hospital in London. At the Institute of Digital Healthcare, part of the Science City Research Alliance, researchers are not only looking to develop new technologies, but to base this firmly on a pragmatic understanding of both the benefits and limitations of integration with commercial Digital Healthcare products which are currently on offer.

CASE STUDY 4: – Digital Healthcare Digital Healthcare Technologies

CASE STUDY 5: – HP Autonomy Medical Analytics - actionable insights from clinical data

• HP Healthcare Analytics delivers a robust and integrated set of core and healthcare industry specific capabilities which organises and interprets unstructured data in context - designed to harness this untapped clinical data and unlock actionable medical insights. This helps to improve care quality by connecting healthcare providers directly with their data through self-service analytics; providing intelligence for more accurate diagnoses so reducing errors, risk and unnecessary treatments; enabling better understanding of how delivery affects outcomes and uncovering insights for preventive measures to decrease the rate of avoidable diseases.

• Changing demographics and regulations are putting tremendous pressure on the healthcare industry to make significant improvements in care quality, cost management, organizational efficiency and compliance. To stay viable, it is paramount to effectively address issues such as misdiagnosis, coding error, over/under treatment, unnecessary procedures and medications, fraud, delayed diagnosis, lack of preventive screening and proactive health maintenance. To that end, better collaboration within the organization with improved information sharing, and a holistic approach to capture actionable insights across the organization becomes crucial.

• In an environment prevalent with multiple unstructured data silos and traditional analytics focused on structured data, healthcare organizations struggle to harness 90%* of their core data - which is mostly medical images, biomedical data streams and unstructured free text found in clinical notes across multiple operational domains. This rich and rapidly growing data asset containing significant biomedical intelligence is exploited using HP Medical Analytics,.

CASE STUDY 5: – Medical Analytics Digital Healthcare Technologies

The Cone™ – Actionable Clinical Insights

Digital Healthcare

Digital Healthcare Technologies

These are some of the most important DIGITAL HEALTH CATEGORIES.....

• Digital Imaging – (MRI / CTI / X-Ray / Ultrasound)

• Robotic Surgery – (Microsurgery / Remote Surgery)

• Patient Monitoring – (Clinical Trials / Health / Wellbeing)

• Biomedical Data – (Data Streaming / Biomedical Analytics)

• Emergency Incident Management – (Response Team Alerts)

• Epidemiology – (Disease Transmission / Contact Management)

Here are some of the most important DIGITAL MONITORING SMART APPS.....

• Activity Monitor – (Pedometer / GPS)

• Position Monitor – (Falling / Fainting / Fitting)

• Sleep Monitor – (Light Sleep / Deep Sleep / REM)

• Cardiac Monitor – (Heart Rhythm / Blood Pressure)

• Blood Monitor – (Glucose / Oxygen / Liver Function)

• Breathing Monitor – (Breathing Rate / Blood Oxygen Level)

Digital Healthcare Technologies

These are some of the most influential FUTURE DIGITAL HEALTH leaders: -

– Huawei - John Frieslaar (Digital Futures)

– Cisco - Andrew Green (Digital Healthcare)

– ElationEMR - Kyna Fong (Digital Imaging)

– Microsoft - John Coplin (Digital Healthcare)

– Google - Eze Vidra (Head of Campus at Tech City)

– GE Healthcare - Catherine Yang (Digital Healthcare)

– MIT – Prof Alex “Sandy” Pentland (Digital Epidemiology)

– Telefónica Digital – Mathew Key – CEO (Digital Healthcare)

– Open University – Dr. Blain Price (Digital Patient Monitoring)

– UCLA – Prof. Larry Smarr (FuturePatient – Digital Patient Monitoring)

– Telefónica – Dr. Mike Short CBE (Digital Futures and the Smart Ward)

– Thames Valley Health Innovation and Education Cluster – David Doughty

– Department of Business, Industry & Skills – Richard Foggie, KTN Executive

– Science City Research Alliance – Sarah Knaggs (Strategic Project Manager)

Digital Healthcare – Executive Summary

• Digital Healthcare is a cluster of new and emerging applications and technologies that exploit digital, mobile

and cloud platforms for treating and supporting patients. The term "Digital Healthcare" is necessarily broad

and generic as this novel and exciting Bioinformatics and Medical Analytics innovation driven approach is

applied to a very wide range of social and health problems - from monitoring patients in intensive care,

general wards, in convalescence or at home – to helping general practitioners make better informed and

more accurate diagnoses, improving the effect of prescription and referral decisions for clinical treatment.

• Bioinformatics and Medical Analytics utilises Data Science to provide actionable clinical insights. Digital

Healthcare has evolved from the need for more proactive and efficient healthcare service delivery, and

seeks to offer new and improved types of pro-active and preventive monitoring and medical care at reduced

cost – using methods that are only possible thanks to emerging SMAC Digital Technology.

Digital Healthcare Technologies – Bioinformatics and Medical Analytics: -

Digital Patient Monitoring •

Biomedical Data Streaming •

Biomedical Data Science and Analytics •

Epidemiology, Clinical Trials, Morbidity and Actuarial Outcomes •

• Novel and emerging high-impact Biomedical Health Technologies such as Bioinformatics and Medical

Analytics are transforming the way that Healthcare Service Providers can deliver Digital Healthcare globally

– Digital Health Technology entrepreneurs, investors and researchers becoming increasingly interested in

and attracted to this important and rapidly expanding Life Sciences industry sector.

Digital Healthcare – Executive Summary

• While many industries can benefit from SMAC digital technology – Smart Devices, Mobile Platforms,

Analytics and the Cloud – this is especially the case for Life Sciences, Pharma and Healthcare

industry sectors – resulting in more accurate diagnosis, improved treatment regimes, more reliable

prognosis, better patient monitoring, care and clinical outcomes. Let’s take a look at some of the

Digital Technologies that are bringing significant improvements and benefits to Healthcare

• Today, thanks to the regulatory compliance requirements for HIPAA, HITEC, PCI DSS and ISO

27001, the reluctance to adopt Digital Technology has been overcome, and Digital Healthcare

adoption is gaining increased traction. Many of the security features required for data protection and

patient confidentiality are being addressed by Digital Healthcare service providers, therefore relieving

healthcare delivery organizations from tedious and complex security and data protection frameworks.

Biomedical Data Analytics:

• The exploitation of data by applying analytical methods such as statistics, predictive and quantitative

models to patient segments or groups of the population will provide better insights and achieve better

outcomes. As far back as 2010, there was evidence that: “93 percent of healthcare providers

identified the digital information explosion as the major factor which will drive organizational change

over the next 5 years.”

(Related article: Cloud and healthcare: A revolution is coming)

Digital Healthcare – Executive Summary

Data Security and Privacy:

• Today, thanks to the regulatory compliance requirements for HIPAA, HITEC, PCI DSS and ISO 27001, reluctance to adopt emerging technologies is starting to be addressed and digital technology is beginning to gain traction - bear in mind also that many of the security features required for data security and protection are addressed by the service providers, therefore relieving the healthcare organization from tedious and complex security frameworks.

Mobility: • Mobility Services, where Smart Devices, Smart Apps, Mobile Platforms and Cloud

Infrastructure is providing the backbone for medical personnel to access all sorts of patient information from any place, any where - and from a wide range of mobile devices.

Collaboration with patients: • Mobility means that complete patient records are now available to healthcare professionals

anytime, anywhere – allowing physicians to access historical patient case records , images and clinical data to fine-tune their diagnosis and make informed decisions on treatment – thus reducing diagnosis latency, increasing accuracy and improving patient care and clinical outcomes from initial consultation to specialist referrals. Some scenarios are illustrated in the following: -

• Physician Collaboration Solutions (PCS) • • PCS solutions offers video conferencing to facilitate remote consultations and care

continuity, allowing patients to be viewed remotely. PCS allows physicians to consult with patients and even perform remote robotic surgery. This is dubbed “tele-health solutions.”

Digital Healthcare – Executive Summary

• Electronic Medical Records (EMR) • • Every piece of information pertaining to a specific is recorded and stored. The solution is

designed to capture and provide a patient’s data at any time of the patient’s monitoring cycle, including the complete medical records and history.

• Patient Information Exchange (PIE) • • This allows for the healthcare information to be shared electronically across organizations

within a region, community or hospital system. There are currently several Digital Healthcare cloud service providers addressing this market, taking the role of collecting and distributing medical information from and among multiple organizations.

• The New York Times has published an interesting article illustrating the use of the cloud in healthcare - leveraging big data in the cloud to manage patient relationships and clinical outcomes.

Collaboration among peers: • Technology can provide medical assistance to doctors in the field, b e it in remote areas or

in emergency relief operations through satellite communications. Refer to the Remote Assistance for Medical Teams Deployed Abroad (T4MOD project) which could easily find its place in the Digital Healthcare cloud space.

Digital Futures: - Creating new roles and value chains

Digital Healthcare - Overview

Digital Futures: - Creating new roles and value chains Novel and emerging Biomedical Health Technologies are transforming the way that

Healthcare Providers can deliver Healthcare globally – with Digital Health Technology entrepreneurs and investors becoming increasingly attracted to this

rapidly growing industry sector.

Healthcare Delivery is currently undergoing a global transformation – with Digital Healthcare Technologies leading the way. Companies such as BT Health, Blueprint

Health, BUPA, Microsoft (John Coplin), Telefonica Digital (Dr. Mike Shaw) and Rockhealth - are all shaping novel and emerging Digital Healthcare Technologies -

bringing new and innovative business propositions to market.

Changing the patient experience

• Advances in technology are already changing patient experiences - making

healthcare better, easier, more accurate and more efficient for physicians, patients,

hospital staff and administrators are

• These changes will no doubt affect the role of hospitals and emergency departments.

As continuous monitoring of biometric data becomes the norm, the ER will be used

as a dispatch center, with patients' information reaching the hospital before they do.

This will eliminate wait times and decrease the risk of disease transmission,

especially important when immune-compromised patients face hours in the ER.

• All of these advances translate into one main objective: improving patient outcomes.

With access to more powerful tools that are cheaper, faster and better than their

predecessors, patient outcomes are certain to improve. People will become

increasingly responsible for their own health. This will lead to more effective care, as

people will be able to detect problems much earlier in the process. Patients will no

longer put off appointments for years because personal health will be ever-present.

This will reduce healthcare costs on several levels and change the type of medical

professionals the industry needs most.

Diagnostics @ Point of Care

• Point of Care Diagnostics: Technology promises to put the burden of care and

diagnosis directly in the hands of patients. The Qualcomm Tricorder XPRIZE

Challenge is sponsoring a $10 million race to develop a handheld, non-invasive

electronic device that can diagnose 15 diseases and track 5 vital signs in the

field. Patients would no longer have to go to a doctor's office or hospital.

Instead, a device in their homes would analyze their data, diagnose the problem

and send their information up to the cloud, where a physician could treat them

remotely. Such a device could make healthcare more accessible in rural areas

and developing nations.

• One of the devices up for the challenge is being developed by Scanadu, which

also has an electronic urinanalysis stick, similar to a pregnancy test, which

performs up to 9 different tests and sends the results through the cloud to the

treating physician, eliminating the need for routine lab visits.

Biomedical Robotics

• Robotics: Robotics are quickly advancing medical treatment. Ekso Bionics has

already launched the first version of its exoskeleton, which enables paraplegics to

stand and walk independently. This revolutionary technology allows a person who has

spent 20 years in a wheelchair to stand on her own. This holds huge promise for the

next generation of robotics.

• Robotic home health care workers are on the horizon. Honda’s robot ASIMO is a

humanoid robot with the ability to navigate through crowds and objects using sensor

technology. Fully autonomous, in the future, we’ll see ASIMO and similar robots in the

home to help when you’re sick or elderly – or just need an extra set of hands. The

possibilities for technology and healthcare really are endless. Now, just think of all the

things your own personal Rosie the Robot will do ….

• BCI and BBIs: As brain-computer interfaces become more advanced, healthcare will

incorporate more complex human-computer connections. The uses range from

helping people manage pain to controlling robotic limbs. Harvard University

researchers recently created the first brain-to-brain interface that allowed a human to

control a rat's tail — and another human's movements — with his mind, proving that

controlled robotic limbs have far-reaching possibilities for patients.

Biomedical Robotics

• Artificial intelligence: IBM's Watson Super Computer is just the first step toward

using artificial intelligence in medicine. The supercomputer, which defeated two

human champions on "Jeopardy!" two years ago, has gone to medical school.

Watson not only gives the top 3 probabilities for a diagnosis, but what physicians

most appreciate is Watson gives the evidence behind these probabilities.

• IBM opened up their API for anyone to use – whether you are 2 kids in a garage or a

Fortune 500 company. Why would they give their technology to their competitors?

Easy. Because Watson improves with use. So the more people and organizations use

Watson, the faster it learns, the better it becomes.

• Biomedical 3D printing: California-based research company Organovo has printed

human liver tissue to test drug toxicity on specific sections of the liver. Although

printing organs for transplants may still be far off, this technology could be used in the

near future with individual patients to test their toxicity reactions to specific drugs.

• Recently researchers have printed out exact replicas of kidneys with tumors for

simulated surgery before going into a patient. These 3D printed kidneys are

transparent so the surgeons can discern where the blood vessels are located. In one

case, this reduced the amount of time a patient’s blood flow to the organ was

interrupted from 22 minutes to 8 minutes during surgery.

The Bacteriophage Revolution

• The emergence of pathogenic bacteria resistant to many, if not most, currently

available anti-microbial agents has become a critical clinical problem in modern

medicine - particularly in the concomitant increase in immuno-suppressed patients.

The concern that the treatment of disease is re-entering the “pre-antibiotics” era

has become real, and the development of alternative anti-infection modalities is

now one of the highest priorities of modern medicine and biomedical technology.

• Prior to the discovery and widespread use of antibiotics, it was suggested that

bacterial infections could be prevented and/or treated by the administration of

viruses which attacked bacteria - bacteriophages. Although the early clinical

studies with bacteriophages were not vigorously pursed in the United States and

Western Europe, phages continued to be utilized in the former Soviet Union and

Eastern Europe. The results of these studies were extensively published in non-

English (primarily Russian, Georgian, and Polish) journals and, therefore, were not

readily available to the western scientific community. In this review, we briefly

describe the history of bacteriophage anti-microbial research in the former Soviet

Union and the reasons that the clinical use of bacteriophages failed to take root in

the West. Further, we share our thoughts about future prospects for phage therapy

in biomedical research – the Bacteriophage Revolution.

• .

Digital Healthcare – Technical Appendices

HP – Outlook for 2015 Biomedical Analytics

HP Autonomy Medical Analytics - actionable insights from clinical data

• HP Healthcare Analytics delivers a robust and integrated set of core and healthcare industry

specific capabilities which organises and interprets unstructured data in context - designed to

harness this untapped clinical data and unlock actionable medical insights. This helps to improve

care quality by connecting healthcare providers directly with their data through self-service

analytics; providing intelligence for more accurate diagnoses so reducing errors, risk and

unnecessary treatments; enabling better understanding of how delivery affects outcomes and

uncovering insights for preventive measures to decrease the rate of avoidable diseases.

• Changing demographics and regulations are putting tremendous pressure on the healthcare

industry to make significant improvements in care quality, cost management, organizational

efficiency and compliance. To stay viable, it is paramount to effectively address issues such as

misdiagnosis, coding error, over/under treatment, unnecessary procedures and medications,

fraud, delayed diagnosis, lack of preventive screening and proactive health maintenance. To that

end, better collaboration within the organization with improved information sharing, and a holistic

approach to capture actionable insights across the organization becomes crucial.

• In an environment prevalent with multiple unstructured data silos and traditional analytics focused

on structured data, healthcare organizations struggle to harness 90%* of their core data - which is

mostly medical images, biomedical data streams and unstructured free text found in clinical notes

across multiple operational domains. This rich and rapidly growing data asset containing

significant biomedical intelligence supports actionable Clinical Insights..

IBM – Outlook for 2015 Wave-form Analytics

IBM Infosphere - Excel Medical Streaming Analytics Platform

• Excel Medical Electronics’ BedMasterEx software is the industry leader in acquisition and storage of complex physiological data (waveforms, vital signs, and clinical alarms) acquired from hospital patient monitoring networks and medical devices.

• Excel Medical Electronics has tightly integrated their BedMasterEx solution with IBM’s InfoSphere Streams to create a groundbreaking new platform to analyze volumes of unstructured clinical data in real time with the goal of creating predictive medical algorithms. In conjunction with IBM Watson Research Center, IBM and Excel Medical Engineers developed adapters to the BedMasterEx system.

• These adapters feed data for both real time analytics and retrospective research databases. The Excel Medical Streaming Analytics Platform provides a common development channel among academic researchers to collaborate and speed up validation of algorithms.

IBM – Outlook for 2015 Mobile Access Platforms

IBM and the Boston Children Hospital • This is exemplified by the recent announcement from IBM and the Boston Children

Hospital, creating “the world’s first cloud-based global education technology platform to transform how paediatric medicine is taught and practiced around the world. The initiative aims to improve the exchange of medical knowledge on the care of critically ill children, no matter where they live.”

• As with everything, you have to be aware of a few shortcomings, the most significant of all being data security and breach of confidentiality. This recurrent theme acted as an inhibitor to healthcare embracing cloud technology. While many cloud providers are now claiming to be able to ensure compliance with HIPAA, the healthcare organizations do still have to figure out how exactly to address these requirements in a cloud environment.

• The organizations now entrusting their cloud providers to host sensitive data and infrastructure do need to understand that they are actually handing over sensitive data to the cloud provider. This in turn will imply the need to explore how the cloud provider will indeed provide the level of security, the quality of service and the availability of the stored information.

• While the healthcare industry is starting to embrace cloud computing, we can already foresee the tremendous potential of this technology leveraging on big data and analytics and all the applications that may come from its many uses. While there might be shortcomings, these are far outweighed by the benefits for both the industry and the patients. What do you think?

Microsoft – Outlook for 2015

• Big Data in Digital Healthcare offers a path towards clinical insight and medical

advances through a culture-challenging information strategy and effective data

management. The global amount of data and internet content is expected to reach

a staggering 5,247 gigabytes per person by 2020. Translated into physical terms,

there are twice as many bytes of data in the world than there are litres of water in

our oceans – that’s a lot of data out there to manage. Further fuelling the rapid

increase in data abundance are falling hardware costs coupled with the

proliferation of vast amounts of machine-generated data in the Cloud from fixed

and mobile appliances, devices and sensors.

• At Microsoft, our goal is to bring Data Science, its applications, information and

Biomedical Data insights to one billion people through secure, scalable and easy-

to-use enterprise-class tools. Data Science and Big Data are driving clinical insight

and medical advances, are fast becoming the major factor for competitive

advantage and business growth. Big Data is just one of several important

trends because through the strategic use of information, businesses can innovate

more quickly, lower operational costs, improve clinical outcomes and drive up

patient health and wellbeing.

Oracle – Outlook for 2015

• The number of new and emerging technologies that employ ubiquitous appliances, monitors,

sensors and devices in order to generate, transmit. store and analyse vast amounts of automatic

machine-generated data will continue to grow as consumers embrace their new digital lifestyles. For

one example, wearable digital technology will start to enter the mainstream market and begin

generating vast amounts of new consumer data from which companies will be able to draw new

meaningful insights. In 2015 we expect big data to finally go mainstream and emerge at a scale

much more significant than just a simple tool for capturing and analysing digital consumer insights.

Scientific Research • Advanced scientific research is a game played in the minutiae of life, in the place where discoveries

made on the tiniest scale can have enormous implications for the entire human population. Projects

are often long and labour-intensive, as researchers conduct a seemingly endless number of iterative

analyses on these microscopic events as they look for trends that point to new discoveries.

Health and Life Sciences • Data Science and Big Data have the potential to drive meaningful progress in the biomedical field,

particularly as health experts seek cures for life-threatening illnesses that affect more and more

people each year. In the medical research arena, for example, the ability to consolidate health data

from patients in hospitals all over the world and trend it in real-time against demographic and

geographic epidemiology, treatment and prescriptive factors - weather, local social customs and

family history becomes very powerful. Armed with the new insights that big data analyses will give

them, medical professionals can focus their efforts and accelerate the race to cure terminal disease.

SAP – Outlook for 2015

• SAP is a Growth Company. SAP wishes to elevate itself to become a trusted innovator for all

of their customers – whether it’s achieving business outcomes, simplifying everything through

the cloud or driving business efficiency and growth using Mobile and In-memory Computing.

• Industry Focused. In 2013 SAP was global the market leader for supplying ERP application

software across 25 different Industry Sectors – and will continue to increase its Industry Sector

focus to make SAP HANA the standard business platform for world-class Industry Sector

applications and process execution.

• The Digital Enterprise. SAP grew its mobile, cloud and in-memory computing businesses

heavily in 2013 and will continue to strengthen its transition into products supporting the Digital

Enterprise area even more so in 2015. BIW (Business Information Warehouse) and ECC6 (ERP

Central Components version 6) Business Suite – will ultimately be fully integrated into Cloud,

Mobile and SAP HANA High-availability Analytics in-memory computing platform environments.

• Key Technology Platforms and Industry Sector areas for SAP in 2015 include the following: -

1. Digital Healthcare

2. Multi-channel Retail

3. Financial Technology

1. Cloud Services

2. The Mobile Enterprise

3. In-memory Computing

Industry Sectors Technologies

Healthcare: - SAP Solution Roadmap

• Patient Experience and Journey – Patient Administration and Billing – Patient Relationship Management

• Clinical Delivery – Clinical Treatment and Care

• Digital Imaging – (MRI / CTI / X-Ray / Ultrasound)

• Robotic Surgery – (Microsurgery / Remote Surgery)

• Patient Monitoring – (Clinical Trials / Health / Wellbeing)

• Biomedical Data – (Data Streaming / Biomedical Analytics)

• Emergency Incident Management – (Response Team Alerts)

• Epidemiology – (Disease Transmission / Contact Management)

– Enterprise Healthcare Mobility (Mobile Devices / Smart Apps) • Activity Monitor – (Pedometer / GPS)

• Position Monitor – (Falling / Fainting / Fitting)

• Sleep Monitor – (Light Sleep / Deep Sleep / REM)

• Cardiac Monitor – (Heart Rhythm / Blood Pressure)

• Blood Monitor – (Glucose / Oxygen / Liver Function)

• Breathing Monitor – (Breathing Rate / Blood Oxygen Level)

• Care Collaboration – Connected Care – Referral Management

From sports to scientific research, a surprising range of industries will begin to find value in big data.....

“Big Data” in Digital Healthcare

“Big Data” in Pharma / Life Sciences

• Big data now plays an important role in medical and clinical research. Digital Patient Records are now being harvested and analysed in large-scale patient population studies – which are yielding actionable clinical insights. The UK Government has made anonymised patient records from the National Health Service openly available. Medical Centres, Research Institutes and Pharma / Life Sciences funding agencies have all made major investments in this area.

Big Data” in Clinical Medicine

“Big Data” in Clinical Medicine

• Big data plays an important role in medical and clinical research and has been exploited in clinical data studies. Major research institute centres and funding agencies have made large investments in the arena. For example, the National Institutes of Health recently committed US $100 million for the big data to Knowledge (BD2K) initiative [40]. The BD2K defines “biomedical” big data as large datasets generated by research groups or individual investigators and as large datasets generated by aggregation of smaller datasets. The most well-known examples of medical big data are databases maintained by the Medicare and Healthcare Cost and Utilization Project (with over 100 million observations).

• One of the differences between medical big data and large datasets from other disciplines is that clinical big data are often collected based on protocols (ie, fixed forms) and therefore are relatively structured, partially due to the extraction process that simplify raw data as mentioned above. This feature can be traced back to the Framingham Heart Study [41], which has followed a cohort in the town of Framingham, Massachusetts since 1948. Vast amounts of data have been collected through the Framingham Heart Study, and the analysis has informed our understanding of heart diseases, including the effects of diet, exercise, medications, and obesity on risk [42]. There are many other clinical databases with different scopes, including but not limited to, prevalence and trend studies, risk factor studies, and enotype-phenotype studies.

“Big Data” – Analysing and Informing

• SENSE LAYER – Remote Monitoring and Control – WHAT and WHEN? – Remote Sensing – Sensors, Monitors, Detectors, Smart Appliances / Devices

– Remote Viewing – Satellite. Airborne, Mobile and Fixed HDCCTV

– Remote Monitoring, Command and Control – SCADA

• GEO-DEMOGRAPHIC LAYER – People and Places – WHO and WHERE? – Person and Social Network Directories - Personal and Social Media Data

– Location and Property Gazetteers - Building Information Models (BIM)

– Mapping and Spatial Analysis – Landscape Imaging & mapping, Global Positioning (GPS) Data

– Temporal / Geospatial data feeds –Weather and Climate, Land Usage, Topology / Topography

• INFORMATION LAYER – “Big Data” and Data Set “mashing” – HOW and WHY? – Content – Structured and Unstructured Data and Content

– Information – Atomic Data, Aggregated, Ordered and Ranked Information

– Transactional Data Streams – Smart Devices, EPOS, Internet, Mobile Networks

“Big Data” – Analysing and Informing

• SERVICE LAYER – Real-time and Predictive Analytics – WHAT / WHEN NEXT? – Global Mapping and Spatial Analysis - GIS

– Service Aggregation, Intelligent Agents and Alerts

– Data Analysis, Data Mining and Statistical Analysis

– Optical and Wave-form Analysis and Recognition, Pattern and Trend Analysis an Extrapolation

• COMMUNICATION LAYER – Mobile Enterprise Platforms and the Smart Grid – Connectivity - Smart Devices, Smart Apps, Smart Grid

– Integration - Mobile Enterprise Application Platforms (MEAPs)

– Backbone – Wireless and Optical Next Generation Network (NGE) Architectures

• INFRASTRUCTURE LAYER – Cloud Service Platforms – Public, Mixed / Hybrid, Enterprise, Private, Secure and G-Cloud Cloud Models

– Infrastructure – Network, Storage and Servers

– Applications – COTS Software, Utilities, Enterprise Services

– Security – Principles, Policies, Users, Profiles and Directories, Data Protection

National Institute for Medical Research

• NIMR is one of the world's leading medical research institutes, dedicated to studying important questions about the life processes that are relevant to all aspects of health.

Francis Crick Institute

Digital Healthcare Skills Matrix

Cluster Theory – Digital

Digital Healthcare Skills Matrix

Cluster Theory – Digital

Abiliti: Future Systems

Slow is smooth, smooth is fast.....

.....advances in “Big Data” have lead to a revolution in Chronic Patient Management, Clinical Trials,

Epidemiology, Morbidity, Actuarial Science, Biomedical profiling, forecasting and predictive modelling – but it

takes both human ingenuity, and time, for Biomedical and Healthcare Models to develop and mature.....

Digital Futures: - Creating new roles and value chains

Digital Healthcare

• Digital Healthcare is a cluster of new and emerging applications and technologies

that exploit digital, mobile and cloud platforms for treating and supporting patients.

The term is necessarily general as this novel and exciting Digital Healthcare

innovation approach is being applied to a very wide range of social and health

problems, ranging from monitoring patients in intensive care, general wards, in

convalescence or at home – to helping doctors make better and more accurate

diagnoses, improving drugs prescription and referral decisions for clinical treatment.

• Digital Healthcare has evolved from the need for more proactive and efficient

healthcare delivery, and seeks to offer new types of prevention and care at reduced

cost – using methods that are only possible thanks to sophisticated technology.

• Digital Healthcare Technologies – Bioinformatics and Medical Analytics. Novel

and emerging high-impact Biomedical Health Technologies such as Bioinformatics

and Medical Analytics are transforming the way that Healthcare Service Providers

can deliver Digital Healthcare globally – Digital Health Technology entrepreneurs,

investors and researchers becoming increasingly interested in and attracted to this

important and rapidly growing Life Sciences industry sector. Bioinformatics and

Medical Analytics utilises Data Science to provide actionable Clinical insights.

Digital Healthcare Technologies

Scalable Enterprise Waveform Analytics Platform for Pharma

• Neural ID provides the only collaborative bio-signal analytics

platform spanning the pharmaceutical lifecycle. From Discovery

through Clinical and Health Information, Neural ID delivers a

scalable enterprise solution addressing the industry’s productivity

crisis. Our flagship product, IWS, delivers expert-driven machine

learning, massive data reduction and an interoperable data format to

help customers make better decisions, faster.

• Neural ID’s enterprise software platform is used by the world's

leading companies to deliver cutting-edge biosignal analytics,

including 4 of the top 10 pharmaceutical companies.

Helix Health Solutions

• Streaming Analytics - Physiological Wave-form Analysis Platform Excel Medical Electronics has developed a groundbreaking new research platform for analyzing volumes of unstructured data in real time by integrating their BedMasterEx data acquisition solution with IBM’s® InfoSphere™ Streams technology. Complex and high frequency medical data such as physiological waveforms have gone relatively unstudied in the healthcare industry due to substantial technology barriers.

Digital Healthcare Technologies

Medical Education and Remote Diagnostics

• Capabilities in Remote Diagnostics and Medical Education are evolving rapidly.

Companies that are innovating on this front and encompassing solutions such as

crowd-sourcing and peer-2-peer learning. Some of those companies really taking

advantage of the explosion in Biomedical “Big Data' include HP, GE Healthcare,

Siemens Healthcare, Boardvitals and AgileMD

Secure Storage and Sharing of Biomedical Information

• Box is a platform that is HIPAA and HITECH compliant for secure capture,

storage and management of Protected Personal Health Information (PPHI).

Medical Service Provider's Tools

• More and more service providers continue to jump on board with the new

Medical Service Provider's Tools that are out there. Two companies that are

particularly interesting are Clinicast and Reify Health (currently in beta test)

Digital Healthcare Technologies

Digital Diagnostics Tools

• Researchers are now taking advantage of new and emerging biomedical technologies which integrate with Mobile Phones and other Smart Devices in order to add diagnostic capabilities to the arsenal of the general and clinical physician. One company that looks promising in the future is Cellscope - FDA approved.

• Proteus Digital Health takes endoscopy to an extraordinary new level. This device is housed in a small capsule which can be swallowed - and contains a range of sensors and detectors, automatically streaming continuous digital information – and even images - to Mobile Phones and other Smart Devices. The device is capable of monitoring and tracking how the patient’s alimentary canal and digestive system behaves when an oral drug is being administered or when food or drink is being consumed. Nephosity - imaging - FDA approved.

• Dexcom markets a device that monitors blood glucose levels which is tucked neatly under the skin of the patient’s abdomen - FDA approved. Google are trialling a soft contact lens with an embedded bluetooth device and a sensor that monitors blood glucose levels - which continuously streams blood glucose level data to a monitoring service in the cloud, via a bluetooth mobile phone connection.

Digital Healthcare Technologies

Patient Communities – Chronic Disease Management • Reducing the cost of treating chronic illness is a major goal – because it can

dramatically improve health indices in populations of individuals suffering from chronic long-term illness Focusing on those highest-cost patient population's is an exciting approach that a number of companies are exploring. Chronic Disease management can be improved by supporting care providers and extenders that take on the task of assisting with the healthcare and improving the outcomes of these high-cost patients.

• Patients that have chronic illness have a variety of needs. Some patients require planned, regular interactions with support to their carers, focusing on function and prevention of acute episodes and complications. Community Healthcare Coaches can provide ongoing assessments in compliance with the treatment plan. Another important issue could be behavioural modification, and an organised support system for the patient. Planned interactions are overseen by the Primary Care Leader and any further intervention must be initiated by the medical practitioner and directed by clinically relevant information systems and continuing follow-up plans.

– Companies that are providing Chronic Disease Management software for

Patient Communities include: - Omada Health, Wallgreens and Safeway Health

Digital Healthcare Technologies

Electronic Medical Records (EMR's)

• EMR's are Active web applications that can intervene directly in order to effect

positive patient outcomes. “Prioritising positive patient care becomes a natural

consequence when the EMR is built with the intent of facilitating the patient-

physician relationship. EMR's focus on supporting the physician – so that the

physician can focus on treating the patient” - says Kyna Fong - ElationEMR

• Companies developing Active Patient Management in order to promote positive

Medical Outcomes include the following Digital Health Technology providers: -

– ElationEMR, GEHealthcare, Curemd and Drchrono and 5 O'Clock Records,

CareCloud between them offer a variety of web-based EMR‘s in addition to General

Practice patient administration systems and revenue cycle management solutions

– DoseSpot is an e-prescribing platform. Medopad and Practice Fusion are EMR's

which are marketed to community practitioners and doctors in primary health groups.

Digital Healthcare Technologies

Telemedicine

• With systems such as Teladoc you can obtain an on-line consultation from a consultant physician or specialist anywhere in the world via an on-line video-link. Teladoc is bringing this facility over to the 'brick and mortar' side by working on the development of walk-in patient kiosks situated in Health Centres and high-street Pharmacies .

Grid Computing World

• Community Grid for grid computing applications - Mobile Phones and other smart devices will make use of sensor and imaging technology to gather passive and active data for statistical analysis and diagnosis via Remote Healthcare Monitoring and Emergency Event Management Centres.

Care Delivery

• Delivery of care can always be improved. Some of the winners in this category are going to be: -

– One Medical, Sherpaa, Metamed (personalized medical research) and Statphone (patient transfers).

Digital Healthcare Technologies

Behavioural Health Analytics

• Patient Behaviour Analysis is the diagnostic tool of the future. Every patient has

unique genetic characteristic and environmental exposure - habits and behaviour

patterns - and any changes to those everyday habits and behaviour patterns may

be an indicator of a change in health status requiring intervention or a predictive

determinant of the future path a patient may take in terms of health and wellbeing.

Mobile Phones and other smart devices will make use of sensor and imaging

technology to gather passive and active data for statistical analysis and diagnosis.

Biomedical “Big Data” Management and Analytics

• Anapsis and EMBI, focus on Biomedical “Big Data” Management and

Analytics. This service is highly customisable for every client.

• Ginger.io is another example of a Behavioural Analytics platform. Ginger.io

examines patterns of everyday activity which are used as points of entry for

understanding larger issues such as paediatrics requirements, geriatrics needs

and mental health care for schemes such as Care in the Community and Assisted

Living at Home.

Digital Healthcare Technologies

Transitional Care • "Care transitions" is a term that describes the flow of patients from clinical

settings to settings in the community - which are socially more appropriate

relative to their needs. Every patient's needs change over time. Patients may

encounter a Primary Care Provider, a hospital physician, the nursing team

and even Social Services before they are “whisked off" to a nursing facility or

care home. Promising companies in the area of Care Transition include: -

– Care At Hand, Independa and OpenPlacement

• Companies such as these are building Smart Apps for Mobile Phones and

other smart devices which will make use of sensor and imaging technology for

streaming data to monitoring services that will bring new possibilities in the

transition from Intensive Care Units and General Hospital Wards, into a

convalescent nursing facility or care home and on into other patient care

schemes such as Care in the Community and Assisted Living at Home.

Digital Healthcare Technologies

Patient Management and Patient Administration Systems

• Integrated new clinical and back-office Patient Management and Patient Administration Systems will be in demand to manage the changing landscape of healthcare services provisioning, funding and cross-charging.

• Some of the challenges that are being addressed range from the simple capture at source of one-off chargeable consultation, medication and point medical procedures – to fully-featured clinical billing systems for managing the provision of complex multi-stage and continuous medication and clinical procedures, re-charging costs and administering payments from Primary Care budget holders and Health Insurance Companies – or patients themselves.

• Solutions from those companies listed below are of interest: -

• Medmonk, Medikly, Simplee, Cake Health, Castlight Healthcare, SwiftPayMD.

Digital Healthcare Technologies - Bioinformatics

• Healthcare is undergoing a global transformation – with Digital Healthcare

Technologies leading the way. Companies such as BT Health, Blueprint Health,

BUPA, Cisco, ElationEMR , Huawei, GE Healthcare, Microsoft, Telefonica Digital

and Rockhealth - are all developing novel and emerging Digital Healthcare

technologies - from Mobile Devices and Smart Apps to “Big Data” Analytics -

bringing new and exciting Digital Healthcare business propositions to market.

• Private Equity and Corporate Investment Funds are pouring seed-money and

Capital into Digital Health start-up ventures - in the hope of funding a “quick win”.

Applied Proteomics has just received an investment of $28 million from Genting

Berhad, Domain Associates and Vulcan Capital. The State of Essen in Germany

has recently invested 55m Euros on an SAP Digital Health Proof-of-concept.

• Telefónica Digital is sponsoring research into Smart Wards with St. Thomas's

Hospital in London. At the Institute of Digital Healthcare, part of the Science

City Research Alliance, researchers are not only looking to develop biomedical

technologies, but to base this firmly on a pragmatic understanding of both the

benefits and limitations of integrating biomedical technologies within the existing

range of commercial Digital Healthcare products and services currently on offer.

Wave-form Analytics

• • WAVE-FORM ANALYTICS • is an analytical tool based on Time-frequency Wave-

form analysis – which has been “borrowed” from spectral wave frequency analysis in

Physics. Deploying the Wigner-Gabor-Qian (WGQ) spectrogram – a method which

exploits wave frequency and time symmetry principles – demonstrates a distinct trend

forecasting and analysis capability in Wave-form Analytics. Trend-cycle wave-form

decomposition is a critical technique for testing the validity of multiple (compound)

dynamic wave-series models competing in a complex array of interacting and inter-

dependant cyclic systems - waves driven by both deterministic (human actions) and

stochastic (random, chaotic) paradigms in the study of complex cyclic phenomena.

• • WAVE-FORM ANALYTICS in “BIG DATA” • is characterised as periodic alternate

sequences of, high and low trends regularly recurring in a time-series – resulting in

cyclic phases of increased and reduced periodic activity – Wave-form Analytics

supports an integrated study of complex, compound wave forms in order to identify

hidden Cycles, Patterns and Trends in Big Data. The existence of fundamental stable

characteristic frequencies in large aggregations of time-series Economic data sets

(“Big Data”) provides us with strong evidence and valuable information about the

inherent structure of Business Cycles. The challenge found everywhere in business

cycle theory is how to interpret very large scale / long period compound-wave

(polyphonic) temporal data sets which are non-stationary (dynamic) in nature.

Wave-form Analytics

Track and Monitor

Investigate and

Analyse

Scan and Identify

Separate and Isolate

Communicate Discover

Verify and Validate Disaggregate

Background Noise

Individual Wave

Composite Waves

Wave-form Characteristics

"Big Data” Analytics – Profiling and Clustering

• "BIG DATA” ANALYTICS – PROFILING, CLUSTERING and 4D GEOSPATIAL ANALYSIS •

• The profiling and analysis of large aggregated datasets - to determine a ‘natural’ structure of data relationships or groupings - is an important starting point forming the basis of many mapping, statistical and analytic applications. Cluster analysis of implicit similarities - such as time-series demographic or geographic distribution - is a critical technique where no prior assumptions are made concerning the number or type of groups that may be found, or their relationships, hierarchies or internal data structures. Geospatial and demographic techniques are frequently used in order to profile and segment populations by ‘natural’ groupings. Shared characteristics or common factors such as Behaviour / Propensity or Epidemiology, Clinical, Morbidity and Actuarial outcomes – allows us to discover and explore previously unknown, concealed or unrecognised insights, patterns, trends or data relationships.

• "Big Data" sources include: - – Transactional Data Streams from Business Systems

– Energy Consumption Data from Smart Metering Systems

– SCADA and Environmental Control Data from Smart Buildings

– Vehicle Telemetry Data from Passenger and Transport Vehicles

– Market Data Streams – Financial, Energy and Commodities Markets

– G-Cloud – NHS Communications Spine, Local and National Systems

– Machine-generated Exploration / Production Data created in Digital Oilfields

– Cable and Satellite Home Entertainment Systems – Channel Selection Data

– Call Detail Records (CDRs) from Telco Mediation, Rating and Billing Systems

– Internet Browsers, Social Media / Search Engines – User Site Navigation and Content Data

– Biomedical Data Streaming – Smart Hospitals / Care in the Community / Assisted Living @ Home

– Other internet click-streams – Social Media, Google Analytics, RSS News Feeds / Market Data Feeds

The Temporal Wave – 4D Geospatial Analytics

• The Temporal Wave is a novel and innovative method for Visual Modelling and Exploration

of Geospatial “Big Data” - simultaneously within a Time (history) and Space (geographic)

context. The problems encountered in exploring and analysing vast volumes of spatial–

temporal information in today's data-rich landscape – are becoming increasingly difficult to

manage effectively. In order to overcome the problem of data volume and scale in a Time

(history) and Space (location) context requires not only traditional location–space and

attribute–space analysis common in GIS Mapping and Spatial Analysis - but now with the

additional dimension of time–space analysis. The Temporal Wave supports a new method

of Visual Exploration for Geospatial (location) data within a Temporal (timeline) context.

• This time-visualisation approach integrates Geospatial (location) data within a Temporal

(timeline) data along with data visualisation techniques - thus improving accessibility,

exploration and analysis of the huge amounts of geo-spatial data used to support geo-

visual “Big Data” analytics. The temporal wave combines the strengths of both linear

timeline and cyclical wave-form analysis – and is able to represent data both within a Time

(history) and Space (geographic) context simultaneously – and even at different levels of

granularity. Linear and cyclic trends in space-time data may be represented in combination

with other graphic representations typical for location–space and attribute–space data-

types. The Temporal Wave can be used in roles as a time–space data reference system,

as a time–space continuum representation tool, and as time–space interaction tool.

BIOMEDICAL DATA - CASE-BASED AND

STREAM-BASED CLASSICATION

Yang Hang Department of Computer and Information Science University of Macau, Macau henry.yh@gmail.com Simon Fong Department of Computer and Information Science University of Macau, Macau ccfong@umac.mo Andy Ip Faculty of Science and Technology University of Macau, Macau henry.yh@umac.mo Sabah Mohammed Department of Computer Science Lakehead University Thunder Bay, Canada sabah.mohammed@lakeheadu.ca

CASE-BASED AND STREAM-BASED CLASSICATION IN BIOMEDICAL DATA - University of Macau

Bioinformatics and Medical Analytics

• Digital Healthcare Technologies – Bioinformatics and Medical Analytics.

Novel and emerging high-impact Biomedical Health Technologies such as

Bioinformatics and Medical Analytics are transforming the way that Healthcare

Service Providers can deliver Digital Healthcare globally – Digital Health

Technology entrepreneurs, investors and researchers becoming increasingly

interested in and attracted to this important and rapidly growing Life Sciences

industry sector. Bioinformatics and Medical Analytics utilises Data Science to

provide actionable Clinical insights.

Bioinformatics

Bioinformatics

• Advances in “Big Data” have lead to a revolution in Chronic and Acute Patient

Monitoring and Management, Clinical Trials, Epidemiology, Morbidity, Actuarial

Science, Biomedical profiling, forecasting and outcome predictive modelling.

• There are two major families of biomedical data which are commonly to be found in

Bioinformatics – firstly, case-based Biomedical data (which consist of historical

record archival data sets), and secondly, stream-based Biomedical data (which are

dynamic signal streams captured in real-time from Medical Equipment – scanners,

sensors or monitors – or any other scientific equipment that you may care to think of..... )

• Profiling and Cluster Analysis has proven its effectiveness over traditional decision-tree

classification for revealing interesting patterns and trends in data-mining of static case-

based clinical data sets . These techniques are, however, used mainly for pattern and

trend detection in historic case-based data - rather than classification, diagnosis or

biomedical event prediction in Biomedical Metrics data which is streamed from Medical

Equipment. The application of Wave-form Analytics to the data mining of dynamic real-

time biomedical data streams has not previously been explored by other researchers -

despite biomedical signal processing techniques having existed for several decades.

CASE-BASED AND STREAM-BASED CLASSICATION IN BIOMEDICAL DATA - University of Macau

Bioinformatics

• Computer Science researchers at the University of Macau have examined the impact

of data mining techniques against static Historic biomedical datasets and dynamic,

continuous Real-time biomedical data streams. The Macau research team have

demonstrated that the two very different bio-medical workflows – consisting of static

case-based and dynamic stream-based data mining for diagnostics classification –

both require radically different Data Mining techniques. In a Simulation Programme

for conducting experiments in the analysis of these two types of biomedical data. a

comparison of the two data mining techniques (case-based and stream-based), the

researchers observed that case-based diagnostic classification data mining has a

higher accuracy – but, because it runs in batch-mode in order to support numerous

multiple database scans – it is much slower than stream-based data mining methods

• Stream-based imaging and analytics has a very low latency but achieves a relatively

lower accuracy - unless the dataset size reaches a critical very large-scale or size –

Biomedical “Big Data”. The researchers propose a new method of Data Profiling –

Cluster Analysis - to resolve the problem of needing multiple batch scanning passes

or steps using classification decision trees – in the long-running multiple database

scanning stages during data mining of dynamic, real-time Biomedical data-streams.

CASE-BASED AND STREAM-BASED CLASSICATION IN BIOMEDICAL DATA - University of Macau

Bioinformatics

• Biomedical datasets pose certain challenges to bioinformatics because of their inherent

natures of high-dimensionality, huge volume, and demand for extremely high accuracy (as

this often involves life-and-death interventions). Recent advances in biomedical sensing

and monitoring technologies further step up the challenges as datasets are generated from

real-time time-series Biomedical data streams – e.g. foetal cardiograms, where multiple

diagnostic features are automatically and continuously being measured through streaming

processing and displaying wave-form signals and images. The problem with current data

mining methods is the Medical datasets must be delimited (finite) - and the long latency to

construct or even to refresh a diagnostics model. A fundamental question for the research

project: - could traditional data mining methods effectively support the mining of dynamic,

continuous, machine-generated, large-scale and real-time biomedical data streams? No !

• Many biomedical imaging analytics and signal processing methods currently exist which

can detect anomalous patterns out of the general “noise” from the incoming data streams

– but it is deemed necessary to have additionally a decision support technique that offers

accurate diagnosis predication based on the latest updates of the incoming signal streams.

Traditional data-mining - for example, induction-based decision-tree diagnostic taxonomy

and classification, works by multiple file scanning passes – against a finite and structured

set of data – repeated many times over in order to build up a taxonomic diagnosis model.

CASE-BASED AND STREAM-BASED CLASSICATION IN BIOMEDICAL DATA - University of Macau

Bioinformatics

• The researchers from the University of Macau have generalised this method as “Historic

Case-based data mining” - which has been widely applied in the following fields of bio-

medical data for statistical analysis / prognosis of chronic and acute disease outcomes: -

– Endocrine System metric diagnoses

– Geriatric adult’s healthcare outcomes etc.

– Paediatric children’s healthcare outcomes

– Heart and Lung transplant patient monitoring

– Traditional Chinese medicine - efficacy and effectiveness

– Clinical Trials, Epidemiology, Morbidity and Actuarial Science

• Recently a new group of data mining algorithms – “Real-time data-stream mining” –

which developed from internet click-stream processing originated by Google – have been

further developed and enhanced for handling large volumes of continuous high-speed

Biomedical data-streams. Stream-based data-mining may address the challenges of

processing high-volume, real-time biomedical data or signals. The main requirement - that

of acquiring timely decisions for intervention from the data mining model – is the data

mining run-time must be significantly less than the velocity of the incoming data streams.

CASE-BASED AND STREAM-BASED CLASSICATION IN BIOMEDICAL DATA - University of Macau

Bioinformatics

• The other unique requirement is that we are no longer able to take for granted that a full and

continuous long-timeline data is always going to be available – compared with long-exposure

data collection, new and emerging data stream mining algorithms can now process relatively

short-term, small and incomplete datasets in a single pass, allowing a clinical decision can be

made instantaneously – within specific parameters of accuracy. These requirements fit in very

well with biomedical applications - especially those that involve dynamic monitoring and real-

time diagnostic analytics, and / or chronic and acute medical event and outcome prediction .

• Previous Biomedical data streaming research has evaluated the differences between traditional

Historic (batch) and real-time (dynamic) data mining applications - but only against non-medical

(financial markets data streaming) data-streams and artificially generated medical data-streams,

• To the best of the research team’s knowledge, this is the first documented attempt to exploit real-

time data stream mining techniques using dynamic bio-medical datasets. The prime objective of

the University of Macau research project was to investigate how well Biomedical data-stream

mining performs against dynamic real-time bio-medical datasets, and to evaluate their respective

diagnostic and medical event prediction accuracy – especially in the use of Wave-form and

Imaging Analytics over real-time traditional diagnostic classification methods.

CASE-BASED AND STREAM-BASED CLASSICATION IN BIOMEDICAL DATA - University of Macau

Biomedical Data Sensors and Detectors

Biomedical Data Sensors and Detectors

• Data Captured via Biomedical sensors, detectors, metering (measurement), monitoring

(looking for changes) and control (maintaining vital statistics) systems - can now be

managed in vast “Biomedical Clouds” which exploit grid computing devices in order to

capture, store and interrogate a wide spectrum of real-time Biomedical Data Types –

ranging from simple measurements of patients temperature, blood oxygen, sugar and

carbon dioxide levels – to the most complex Image Processing and Visual Rendering in

real time using data streamed from MRI, CTI, Ultra-sound and X-ray scanning machines

• There are three major areas of opportunity – these are some of the applications that

Biomedical companies are currently working on: -

1. Biomedical data collection, storage and communication - from individual patients

2. Biomedical data integration – combining multiple data sets for analysis / interpretation

3. Biomedical data aggregation and summarisation – vast clinical data sets collected and

integrated from thousands of patients – driving Geo-demographic clustering and

statistical analysis for Clinical Trials, Epidemiology, Morbidity and Actuarial Science

• Companies that have great potential in these areas include: - Sanyo Intelligence,

Apple and GEHealthimagination, Cardiio, MC10, AliveCor, AgaMatrix, Proteus.

Real-time Biomedical Data Streaming

Real-time Biomedical Data Streaming • Biomedical Scientists around the world are deeply committed to advanced Medical Programmes

which are capable of automatically generating and processing, Exobytes (millions of Petabytes)

of Biomedical Data. in real-time This data is captured via Biomedical, sensors, detectors,

measurement, monitoring and control systems - and is managed in vast “Biomedical Clouds”

which utilise grid computing devices in order to capture, store and analyse a wide spectrum of

real-time Biomedical Data Types – ranging from simple measurements of patients temperature,

blood oxygen, sugar and carbon dioxide levels – to complex Image Processing and Visual

Rendering in real time using data from MRI, CTI, Ultra-sound and X-ray scanning machines

Real-tIme bioMEdical data Streaming (RIMES)

Real-time Biomedical Data Streaming

Real-time Biomedical Data Streaming

• Most of these Biomedical datasets are huge – potentially containing Exobytes

(millions of Petabytes ) of Biomedical “Big Data”. Biomedical Data Streams are

composed of machine-generated metering, sensing and monitoring data captured by

scientific instruments deployed in support of large-scale Biomedical Research

programs. Biomedical Software features intelligent agents and alerts which can

automatically trigger alarms and interventions. Various types of biomedical data are

supported by the Biomedical Cloud environment, including .pdb and .dcd files.

• As Biomedical Data in the working repository is continuously updated, appended

image frames may be streamed to an RBNB Data-turbine Cloud by the RIMES

Synchronisation client - which ensures that data from the Biomedical Data Stream is

continuously synchronized with the Biomedical Data Cloud. User Clinicians may

deploy various extended user services over the core biomedical grid computing

features and mass storage systems – including various Biomedical Software Portals,

such as intelligent agents and alerts, visualization and analytics tools portals – which

are continuously processing incoming dynamic real time biomedical data streams.

Biomedical Data and Analytics: - Management Principles

Data Management Principles

• Driving economic value out of data is a complex task and one that requires sophisticated enterprise-

level data management software. This is apparent right now but will become even more obvious as

cloud architectural models become ever more sophisticated and ubiquitous. In the world of hybrid

cloud for example, a lot of attention has been focused on the movement of workloads from one cloud

to another. The ability to move an application from one service provider to another or from one

private cloud to a public cloud is one of the main attractions of a hybrid cloud model. What tends to be

over looked in the discussion though is the data that is associated with the workload and how that

moves through this ecosystem.

Data Management Principles

• Data Sovereignty – Data stored in a country should be subject to the data laws prevalent in that

country. This is especially acute for customer data and many countries have amended their data laws

to ensure that customer data created in-country stays in-country. This can be difficult to regulate as

workloads and their data are moved to the cloud, especially in a public cloud model. There is an

element of trust of the service provider that is required.

• Data Gravity – Moving data about from one platform to another is problematic. Data storage is

persistent and resides some physical place unlike an application that is being processed at the

compute layer or data that is transferred over a network. In essence, data has inertia and data

movement takes time.

Data Management Principles

• Data Classification – Not all data is created equal. Being able to classify data and apply suitable

policies to the treatment of that data is essential. This actually is the higher order capability, and the

basis for really deriving value out of the data, allowing data analysis technologies do their work.

• Data Privacy – This needs little explanation. Data privacy laws are continually being updated (and

usually getting tighter). Cloud service providers, whether public, private or hyperscalar need to be as

cognizant of the need for data privacy just as much as enterprises running on-prem data centers. If

anything they need to be even more vigilant given their systems are often multi-tenanted, storing data

from a large number of customers, some of whom may even be competitors.

• Data Governance, Data Ownership – All roughly the same broad topic as Data Stewardship and

Data Custody. Data, especially in the context of an enterprise, needs to be governed properly.

Auditable processes need to be established and individuals held responsible for following them. Phil

Brotherton has written eloquently about what he calls ‘the value of data control’ in the cloud and why

choosing the right partners to deliver a hybrid cloud is essential if data stewardship issues are to be

fully addressed.

• Data Replication – Allied to the movement of data question. Data needs to be replicated for a

plethora of reasons such as backup and recovery, high availability, compliance obligations etc. The

legality of where copies are data are stored is an interesting question related to the data sovereignty

issue noted above..

Data Management Principles

• Data Security – IT security as an overarching topic has been at the top of CIOs agenda for the last

several years and I doubt it will ever drop off their lists. As we start to employ more cloud based

architectural paradigms, the IT security issue will only intensify. Data protection and anti-data

leakage technologies will continue to be essential in protecting the integrity of data, whether held in

on-premise data centers or in the cloud.

• Data Escrow – What happens to your data when your cloud service provider goes belly-up? Getting

it back came be very expensive – read what happened when 2e2 shut its data center last year or

Nirvanix, a cloud storage vendor who went into administration last year giving its customers two

weeks to retrieve their data (at their own expense). The lesson here is if you outsource you data

processing provisioning to a service provider, you do not outsource the ownership of the data nor

your responsibility. As an old boss of mine used to say “there’s a fine line between delegation and

abrogation of responsibility”. After looking up the word I understood what he meant about crossing

that line.

• Data Asset Management – Deriving value out of data is a complex task and one that requires

sophisticated enterprise-level data management software. This is apparent right now but will become

even more obvious as cloud architectural models become ever more sophisticated and ubiquitous. In

the world of hybrid cloud for example, a lot of attention has been focused on the movement of

workloads from one cloud to another. The ability to move an application from one service provider to

another or from one private cloud to a public cloud is one of the main attractions of a hybrid cloud

model. What tends to be over looked in the discussion though is the data that is associated with the

workload and how that moves through this ecosystem.

Data Management Principles

• Data Storage – The storage of data is a means to an end. Why do we implement storage arrays at

all? Essentially it is to manage all the data that our stakeholders create and to do so in the most

effective way possible: - ffective from both a cost and a performance perspective. The relationship

between storage systems and data management is therefore intrinsic. Storage systems tend to have

similar non-functional requirements. The major criteria are: -

1. Performance – will it give me the throughput and the latency that my users need in order to get

access to the data they want?

2. Reliability – how often will it break down? how often will data be unavailable if at all?

3. Scalability – how many disks can I add? how much data can it store?

4. Ease of Use - how complex will it be? how can the data I store on it be tracked, backed up,

restored etc?

• Data storage and data management are intrinsically linked - these are complex storage issues which

big storage vendors have been addressing for 30 years or more. However when I think about

storage today, I am drawn much more to the latter than the former. Certainly storage hardware

vendors have differentiated technologies that provide the bedrock for data management, but it is in

the complexities of the data management layer where I believe the true action lies and differentiation

will be observed.

Data Management Principles

• In summary, Data Management is set to be an extremely critical area of IT over the next few decades.

The Internet of Things is now being flooded with the ubiquitous presence of pervasive smart devices

– in particular, in the Wearable Technology, Future Homes and Smart Cities categories. It isn’t just

about the vast volumes of data that we are now seeing with the Internet of Things and the tsunami

wave of machine-generated data from connected devices - it also about the abstraction of numerous

storage capabilities from hardware into software and the emergence of the so-called software-defined

Software Data Storage Platforms. As the future unfolds – data density can only get more intense.

Alex Osterwalder invented the Internet of Things Business Canvas in 2008

A Business Model for the Internet of Things

• Studies from Cisco, IBM, Microsoft, McKinsey, Gartner, Forrester and other

companies are now indicating a tremendous surge in growth of several

consumer categories and product areas in the Internet of Things – often referred

to as the Internet of Everything Everywhere. The Internet of Things is now being

flooded with the ubiquitous presence of pervasive smart devices – in particular,

Wearable Technology, Future Homes and Smart Cities categories. The number

of internet connected devices on our bodies, in our homes and around our cities

is only one example demonstrating how fast IOT / IEE technology is growing.

• The Internet of Things Business Canvas splits the IOT business model into

two distinct streams, the physical and the digital. Amazing new opportunities are

now being created through connecting and integrating physical devices into

digital communications – revealing fascinating social insights that we have never

appreciated before. Connecting the unconnected, the physical and the digital

streams are pivotal to the delivery of this new value proposition. Consumers are

embracing for example, Wearable Technology, Future Homes and Smart Cities

in almost every aspect of their daily life. Small start-ups funded by the crowd are

offering all kinds of services based on connected devices - on a massive scale.

A Business Model for the Internet of Things

Claropartners have developed a business model template for the Internet of Things

Digital Product Lifecycle Strategy

Digital Product Lifecycle Strategy

Digital Product Lifecycle Strategy

• Everything around us has a lifecycle. It is born, it grows, it ages, and it ultimately dies. It’s easy to spot a lifecycle in action everywhere you look. As a person is born, grows, ages, and dies – then so does a star, a tree, a bee, or a civilization – and so does a company, a product, a technology or a market - everything goes around in a lifecycle of it own.

Digital Product Lifecycle Strategy

• Everything around us has a lifecycle. It is born, it grows, it ages, and it ultimately dies.

It’s easy to spot a lifecycle in action everywhere you look. As a person is born, grows,

ages, and dies – then so does a star, a tree, a bee, or a civilization – and so does a

company, a product, a technology or a market - everything has a lifecycle of it own.

• All lifecycles exist within a dynamic tension between system development and

system stability. When an entity is born, and during it’s early its development - it

has low stability. As it grows, both its development and stability increase until it

reaches maturity. After peaking, its ability to develop diminishes over time while its

stability keeps increasing over time. Finally, it becomes so stable that it ultimately dies

and, at that moment, it loses all stability as well.

• That’s the basics of all lifecycles. We can try to optimize the path or slow the effects of

aging, but ultimately every system makes this lifecycle progression. Of course, not

all systems follow a bell curve like the picture below. Some might die a premature

death. Others are a flash in the pan. A very few live long and prosper - but from

insects to stars and everything in between, we can say that all things comes into

being, grows, matures, ages, and ultimately fades away. Such is the way of life.

Digital Product Lifecycle Strategy

• Everything has a lifecycle. It is born, it grows, it ages, and it ultimately dies. It’s easy

to spot a lifecycle in action everywhere you look. As a person is born, grows, ages,

and dies – as does a star, a tree, a bee, or a civilization – and so does a company, a

product, or a market - everything has a lifecycle of it own.

Digital Product Lifecycle Strategy

Investment

Product Lifecycle

Product Launch

Product Development

Product Planning

Death

Plateau

Product Maturity

Decline

Aging

Early Growth

Migrate Customers

to new Products

Withdraw

Innovation Prototype / Pilot / Proof-of-concept

Cash Cow Cease Investment

Digital Product Lifecycle Strategy

• What do the principles of adaptation and lifecycles have to do with your business

strategy? Everything. Just as a parent wouldn’t treat her child the same way if she’s

three or thirty years old, you must treat your strategy differently depending on the

lifecycle stage. And when it comes to your business strategy, there are actually three

lifecycles you must manage. They are the product, market, and execution lifecycles: -

– The product lifecycle refers to the assets you make available for sale.

– The market lifecycle refers to the type of customers to whom you sell.

– The execution lifecycle refers to your company’s ability to execute.

• In order to execute on a successful strategy, the stages of all three lifecycles must be in

close alignment with each other. If not, like a pyramid with one side out of balance, it will

collapse on itself and your strategy will fail. Why? Because aligning the product, market,

and execution lifecycles gives your business the greatest probability of getting new

energy from the environment now and capitalizing on emerging growth opportunities in

the future. The goal of any digital product strategy is to get new energy from the

environment, now and in the future.) As we will see, aligning all three lifecycles also

decreases your probability of making major strategic product placement mistakes.

Digital Product Lifecycle Strategy

• Each lifecycle please note that each stage blends into the next. Although every

lifecycle may have distinct stages, this is really only for convenience. There’s no

real, definitive, clean and clear break where you know when one stage has ended

and another begins. In addition, there are three basic prerequisites that you must

have before you can pursue any strategy.

• First, the strategy must be aligned with the company vision and values. Second, the

company must have or be able to get the resources – including staff, technology,

and capital – to execute the strategy. Third, the company must have or be able to

develop the core capabilities to execute the strategy. For now, I am going to assume

that you have all three prerequisites in place and that you’re currently acting on, or

about to act on, a strategy that meets these basic requirements.

Digital Product Lifecycle Strategy

Digital Product Lifecycle – End-phase

Wave-form

Analytics

• The challenge

found everywhere

in wave-form

theory is how to

interpret very large

scale / long period

compound-wave

(polyphonic) time-

series (temporal)

data sets which

are fundamentally

variable (dynamic)

in nature - waves

which are driven

by deterministic

(human actions)

and stochastic

(random, chaotic)

processes..... deterministic stochastic

deterministic stochastic

Wave-form Analytics

• The challenge found everywhere in wave-form theory is how to interpret very large scale / long period compound-wave (polyphonic) time-series (temporal) data sets which are radically non-stationary (dynamic) in nature - waves which are driven by both deterministic (human actions) and stochastic (random, chaotic) processes.....

deterministic stochastic

Wave-form Analytics in Cycles

• Wave-form Analytics is a new analytical tool “borrowed” from spectral wave

frequency analysis in Physics – and is based on Time-frequency Wave-form

analysis – a technique which exploits the wave frequency and time symmetry

principle. This is introduced here for the first time in the study of human activity

waves, and in the field of economic cycles business cycles, patterns and trends.

• Trend-cycle decomposition is a critical technique for testing the validity of multiple

(compound) dynamic wave-form models competing in a complex array of

interacting and inter-dependant cyclic systems in the study of complex cyclic

phenomena - driven by both deterministic and stochastic (probabilistic) paradigms.

• In order to study complex periodic economic phenomena there are a number of

competing analytic paradigms – which are driven by either deterministic methods

(goal-seeking - testing the validity of a range of explicit / pre-determined / pre-

selected cycle periodicity value) and stochastic (random / probabilistic / implicit -

testing every possible wave periodicity value - or by identifying actual wave

periodicity values from the “noise” – by analysing harmonic resonance and

interference patterns in order to discover the fundamental original frequencies).

Wave-form Analytics in Cycles

• The existence of fundamental stable characteristic frequencies in large aggregations

of time-series economic data sets (“Big Data”) provides us with strong evidence and

valuable information about the inherent structure of Business Cycles. The challenge

found everywhere in business cycle theory is how to interpret very large scale / long

period compound-wave (polyphonic) time series data sets which are in nature

dynamic (non-stationary) waves. Fundamental constraints for Friedman's rational

arbitrageurs are the selection of valid reference points and a preferred time-scale

from large data sets containing economic observations – this will be re-examined

later from the viewpoint of source data ambiguity and dynamic cycle instability.

• Wave-form Analytics is a new analytical too based on Time-frequency analysis – a

technique which exploits the wave frequency and time symmetry principle. A variety

of competing deterministic and stochastic methods, including the first difference

(FD) and Hodrick-Prescott (HP) filter - may be deployed with the mixed case of

multiple-frequency overlaid cycles and background system noise, using repetitive

estimation and elimination techniques. The FD filter does not produce any clear

picture of multiple business cycles – however, the HP filter provides us with strong

empiric evidence for pattern recognition of multiple co-impacting business cycles.

Wave-form Analytics

Track and Monitor

Investigate and

Analyse

Scan and Identify

Separate and Isolate

Communicate Discover

Verify and Validate Disaggregate

Background Noise

Individual Wave

Composite Waves

Wave-form Characteristics

Wave-form Analytics in Cycles

• Biological, Sociological, Economic and Political systems all tend to demonstrate

Complex Adaptive System (CAS) behaviour - which appears to be more similar

in nature to biological behaviour in a living organism than to Disorderly, Chaotic,

Stochastic Systems (“Random” Systems). For example, the remarkable

adaptability, stability and resilience of market economies may be demonstrated by

the impact of Black Swan Events causing stock market crashes - such as oil price

shocks (1970-72) and credit supply shocks (1927- 1929 and 2008 onwards).

Unexpected and surprising Cycle Pattern changes have historically occurred

during regional and global conflicts being fuelled by technology innovation-driven

arms races - and also during US Republican administrations (Reagan and Bush -

why?). Just as advances in electron microscopy have revolutionised biology -

non-stationary time series wave-form analysis has opened up a new space for

Biological, Sociological, Economic and Political system studies and diagnostics.

• The Wigner-Gabor-Qian (WGQ) spectrogram method demonstrates a distinct

capability for identifying revealing multiple and complex superimposed cycles or

waves within dynamic, noisy and chaotic time-series data sets – without the need

for using repetitive individual wave-form estimation and elimination techniques.

The Temporal Wave – 4D Geospatial Analytics

• The Temporal Wave is a novel and innovative method for Visual Modelling and Exploration

of Geospatial “Big Data” - simultaneously within a Time (history) and Space (geographic)

context. The problems encountered in exploring and analysing vast volumes of spatial–

temporal information in today's data-rich landscape – are becoming increasingly difficult to

manage effectively. In order to overcome the problem of data volume and scale in a Time

(history) and Space (location) context requires not only traditional location–space and

attribute–space analysis common in GIS Mapping and Spatial Analysis - but now with the

additional dimension of time–space analysis. The Temporal Wave supports a new method

of Visual Exploration for Geospatial (location) data within a Temporal (timeline) context.

• This time-visualisation approach integrates Geospatial (location) data within a Temporal

(timeline) data along with data visualisation techniques - thus improving accessibility,

exploration and analysis of the huge amounts of geo-spatial data used to support geo-

visual “Big Data” analytics. The temporal wave combines the strengths of both linear

timeline and cyclical wave-form analysis – and is able to represent data both within a Time

(history) and Space (geographic) context simultaneously – and even at different levels of

granularity. Linear and cyclic trends in space-time data may be represented in combination

with other graphic representations typical for location–space and attribute–space data-

types. The Temporal Wave can be used in roles as a time–space data reference system,

as a time–space continuum representation tool, and as time–space interaction tool.

Wave-form Analytics

Track and Monitor

Investigate and

Analyse

Scan and Identify

Separate and Isolate

Communicate Discover

Verify and Validate Disaggregate

Background Noise

Individual Wave

Composite Waves

Wave-form Characteristics

Wave-form Analytics

• • WAVE-FORM ANALYTICS • is an analytical tool based on Time-frequency Wave-

form analysis – which has been “borrowed” from spectral wave frequency analysis in

Physics. Deploying the Wigner-Gabor-Qian (WGQ) spectrogram – a method which

exploits wave frequency and time symmetry principles – demonstrates a distinct trend

forecasting and analysis capability in Wave-form Analytics. Trend-cycle wave-form

decomposition is a critical technique for testing the validity of multiple (compound)

dynamic wave-series models competing in a complex array of interacting and inter-

dependant cyclic systems - waves driven by both deterministic (human actions) and

stochastic (random, chaotic) paradigms in the study of complex cyclic phenomena.

• • WAVE-FORM ANALYTICS in “BIG DATA” • is characterised as periodic alternate

sequences of, high and low trends regularly recurring in a time-series – resulting in

cyclic phases of increased and reduced periodic activity – Wave-form Analytics

supports an integrated study of complex, compound wave forms in order to identify

hidden Cycles, Patterns and Trends in Big Data. The existence of fundamental stable

characteristic frequencies in large aggregations of time-series Economic data sets

(“Big Data”) provides us with strong evidence and valuable information about the

inherent structure of Business Cycles. The challenge found everywhere in business

cycle theory is how to interpret very large scale / long period compound-wave

(polyphonic) temporal data sets which are non-stationary (dynamic) in nature.

Wave-form Analytics in Big Data

• Wave-form Analytics is a new analytical tool “borrowed” from spectral wave

frequency analysis in Physics – and is based on Time-frequency Wave-form

analysis – a technique which exploits the wave frequency and time symmetry

principle. This is introduced here for the first time in the study of human activity

waves, and in the field of morbidity and epidemiology cycles, patterns and trends.

• Trend-cycle decomposition is a critical technique for testing the validity of multiple

(compound) dynamic wave-form models competing in a complex array of

interacting and inter-dependant cyclic systems in the study of complex cyclic

phenomena - driven by both deterministic and stochastic (probabilistic) paradigms.

• In order to study complex periodic morbidity and epidemiology phenomena there

are a number of competing analytic paradigms – which are driven by either

deterministic methods (goal-seeking - testing the validity of a range of explicit / pre-

determined / pre-selected cycle periodicity value) and stochastic (random /

probabilistic / implicit - testing every possible wave periodicity value - or by

identifying actual wave periodicity values from the “noise” – by analysing harmonic

resonance and interference patterns to find the fundamental frequencies).

Wave-form Analytics in Big Data

• The strong evidence of stable characteristic frequencies in large biomedical data set

aggregations (“Big Data”) provides us with some insights and valuable information

into the structure of natural pandemic and famine cycles. A fundamental challenge

found everywhere in morbidity and epidemiology cycle theory is how to interpret the

very large scale / long period compound-wave (polyphonic) time series data sets

which are dynamic (non-stationary) in nature. The role of time scale and preferred

reference from clinical and statistical observation are fundamental constraints for

Friedman's rational arbitrageurs – which will be re-examined later from the viewpoint

of source data ambiguity and dynamic cycle instability.

• Wave-form Analytics is a new analytical too based on Time-frequency analysis – a

technique which exploits the wave frequency and time symmetry principle. A variety

of competing deterministic and stochastic methods, including the first difference

(FD) and Hodrick-Prescott (HP) filter - may be deployed with the mixed case of

multiple-frequency overlaid cycles and background system noise, using repetitive

estimation and elimination techniques. The FD filter does not produce any clear

picture of multiple business cycles – however, the HP filter provides us with strong

empiric evidence for pattern recognition of multiple co-impacting morbidity cycles.

Wave-form Analytics

Track and Monitor

Investigate and

Analyse

Scan and Identify

Separate and Isolate

Communicate Discover

Verify and Validate Disaggregate

Background Noise

Individual Wave

Composite Waves

Wave-form Characteristics

Wave-form Analytics in Big Data

• Biological, Sociological, Economic and Political systems all tend to demonstrate

Complex Adaptive System (CAS) behaviour - which appears to be more similar

in nature to biological behaviour in an organism than to Disorderly, Chaotic,

Stochastic Systems (“Random” Systems). For example, the remarkable

adaptability, stability and resilience of market economies may be demonstrated by

the impact of Black Swan Events causing stock market crashes - such as oil price

shocks (1970-72) and credit supply shocks (1927- 1929 and 2008 onwards).

Unexpected and surprising Cycle Pattern changes have historically occurred

during regional and global conflicts being fuelled by technology innovation-driven

arms races - and also during US Republican administrations (Reagan and Bush -

why?). Just as advances in electron microscopy have revolutionised biology -

non-stationary time series wave-form analysis has opened up a new space for

Biological, Sociological, Economic and Political system studies and diagnostics.

• The Wigner-Gabor-Qian (WGQ) spectrogram method demonstrates a distinct

capability for identifying revealing multiple and complex superimposed cycles or

waves within dynamic, noisy and chaotic time-series data sets – without the need

for using repetitive individual wave-form estimation and elimination techniques.

The Temporal Wave

• The Temporal Wave is a novel and innovative method for Visual Modelling and Exploration

of Geospatial “Big Data” - simultaneously within a Time (history) and Space (geographic)

context. The problems encountered in exploring and analysing vast volumes of spatial–

temporal information in today's data-rich landscape – are becoming increasingly difficult to

manage effectively. In order to overcome the problem of data volume and scale in a Time

(history) and Space (location) context requires not only traditional location–space and

attribute–space analysis common in GIS Mapping and Spatial Analysis - but now with the

additional dimension of time–space analysis. The Temporal Wave supports a new method

of Visual Exploration for Geospatial (location) data within a Temporal (timeline) context.

• This time-visualisation approach integrates Geospatial (location) data within a Temporal

(timeline) data along with data visualisation techniques - thus improving accessibility,

exploration and analysis of the huge amounts of geo-spatial data used to support geo-

visual “Big Data” analytics. The temporal wave combines the strengths of both linear

timeline and cyclical wave-form analysis – and is able to represent data both within a Time

(history) and Space (geographic) context simultaneously – and even at different levels of

granularity. Linear and cyclic trends in space-time data may be represented in combination

with other graphic representations typical for location–space and attribute–space data-

types. The Temporal Wave can be used in roles as a time–space data reference system,

as a time–space continuum representation tool, and as time–space interaction tool.

Digital Healthcare – Technical Appendices

The Digital Enterprise

Digital Technology • The term Digital Technologies is used to describe the exploitation of digital resources in order to

discover, analyse, create, exploit, communicate and consume useful information within a digital context. This encompasses the use of various Smart Devices and Smart Apps, Next Generation Network (NGN) Digital Communication Architectures, web 2.0 and mobile programming tools and utilities, mobile and digital media e-business / e-commerce platforms, and mobile and digital media software applications: -

• Cloud Services

– Secure Mobile Payments / On-line Gaming / Digital Marketing / Automatic Trading

– Automatic Data – Machine-generated Data for Remote Sensing, Monitoring and Control

• Mobile – Smart Devices, Smart Apps, Apps Shops and the Smart Grid

• Social Media Applications – FaceBook, LinkedIn, MySpace, Spotify, Twitter, U-Tube, WhatsApp

• Digital and Social Customer Relationship Management – eCRM and sCRM

• Multi-channel Retail – Home Shopping, e-commerce and e-business platforms

• Next Generation Network (NGN) Digital Communication Architectures – 4G, Wifi

• Next Generation Enterprise (NGE) – Digital Enterprise Target Operating Models (eTOM)

• Big Data – Discovery of hidden relationships between data items in vast aggregated data sets

• Fast Data – Data Warehouse Engines, Data Marts, Data Mining, Real-time / Predictive Analytics

• Smart Buildings – Security, Environment Control, Smart Energy, Multimedia/Entertainment Automation

SMAC – Social, Mobile, Analytics, Cloud

OVERVIEW

• While Social, Mobile, Analytics and Cloud technologies add a new dimension

to the Telco 2.0 business operating model and technology landscape, to fully

maximize their value - consider the whole to be greater than sum of its parts.....

• The formula for the Future of Work is centred around SMAC - Social, Mobile,

Analytics and Cloud – integrated on a single technology stack, where every

function enables all of the others to maximize their cumulative impact. This is the

foundation of a new Enterprise Architecture model delivering Digital Technology

that supports an organization that is fully integrated in real-time – and is thus

more lean, agile, connected, collaborative productive and customer-focussed.

SMAC – Social, Mobile, Analytics, Cloud

• Social Media, Virtual Communities, Digital Ecosystems

• Mobile Communication Platforms / Smart Devices / Smart Apps

• Analytics / Data Science / Big Data / Hadoop / SSDs / GPUs

• Cloud Services Platforms

MOBILE ENTERPRISE (MEAP’s) - Vendors & Technologies

SMAC – Smart, Mobile, Analytics, Cloud

• Today’s SMAC Stack™ - ‘the fifth wave’ of IT architecture - is happening faster

than anything that has ever come before. By 2020, as many as 30 billion fixed

devices will be connected to the internet and 70 billion mobile computing devices

will be connected to the Cloud. Enterprises will be managing 50 times the amount

of data than they do currently. So SMAC will have a multiplying effect on

businesses and increase productivity across the organization – whilst placing a

massive burden on Service Providers of future Digital Communications

Technology Stacks, Platforms and Architectures.

THE SMAC EFFECT

• In all Industries across the business landscape, the SMAC Stack™ is eroding the

century-old blueprint of value chains and spawning new, highly distributed, digital

business models, social networks, virtual communities and digital ecosystems.

The power of SMAC technology platforms is released by treating SMAC as an

integrated digital stack – as core components combine to create a massive

multiplying effect when they are integrated and deployed together.

Chart showing the growth of Smart-phones as compared to PCs. This remarkable trend has got all of the PC

manufacturers worried - they are all looking into transitioning into the manufacture of Smart-phones, PDAs and

Tablets. Now is the time to enter the Digital Enterprise and Mobile Platform marketplace - before its too late,,,,,

The Mobile Enterprise – Outlook for 2015

• CONVERTING DATA STREAMS INTO REVENUE STREAMS • SMAC Digital Technologies • describes the use of digital resources in order to discover, analyse, create, exploit, communicate and consume useful information within a digital context. This encompasses the deployment of Enterprise 2.0 Target Operating Model (eTOM) and development of Smart Devices and Smart Apps, Next Generation Network (NGN) Mobile Communication Architectures (4G / LTE), Analytics, Data Science and Big Data supported by Cloud Computing and integrated with Network API Services for access by OTT Business Partners, Value-added Service Providers (VARs) and other 3rd Party consumer platforms. Data sources include the following: - • Transactional Data Streams from Business Systems • Energy Consumption Data from Smart Metering Systems • SCADA and Environmental Control Data from Smart Buildings • Vehicle Telemetry Data from Passenger and Transport Vehicles • Market Data Streams – Financial, Energy and Commodities Markets • G-Cloud – NHS Communications Spine, Local and National Systems • Cable and Satellite Home Entertainment Systems – Channel Selection Data • Call Detail Records (CDRs) from Telco Mediation, Rating and Billing Systems • Machine-generated data from Computer-aided Design and Manufacturing Systems • Internet Browsers, Social Media and Search Engines – User Site Navigation and Content Data • Biomedical Data Streaming – Smart Hospitals / Care in the Community / Assisted Living @ Home • Other internet click-streams – Social Media, Google Analytics, RSS News / Market Data Feeds

• Geo-demographic techniques are frequently used in order to profile and segment population segments or clusters by ‘natural’ groupings - common behavioural traits, Epidemiology, Clinical Trial, Morbidity or Actuarial outcomes, along with many other shared characteristics and common factors – in order to discover and explore previously unknown, concealed or unrecognised patterns, trends and data relationships.

SMAC – Smart, Mobile, Analytics, Cloud

From sports to scientific research, a surprising range of industries will begin to find value in big data.....

Hadoop Clustering and Managing Data.....

Managing Data Transfers in Networked Computer Clusters using Orchestra

To illustrate I/O Bottlenecks, we studied Data Transfer impact in two clustered computing systems: -

Hadoop - using trace from a 3000-node cluster at Facebook

Spark a MapReduce-like framework with iterative machine learning + graph algorithms.

Mosharaf Chowdhury, Matei Zaharia, Justin Ma, Michael I. Jordan, Ion Stoica

University of California, Berkeley

{mosharaf, matei, jtma, jordan, istoica}@cs.berkeley.edu

Hadoop Framework

• The workhorse relational database has been the tool of choice for businesses for well over 20 years now. Challengers have come and gone but the trusty RDBMS is the foundation of almost all enterprise systems today. This includes almost all transactional and data warehousing systems. The RDBMS has earned its place as a proven model that, despite some quirks, is fundamental to the very integrity and operational success of IT systems around the world.

• The relational database is finally showing some signs of age as data volumes and network speeds grow faster than the computer industry's present compliance with Moore's Law can keep pace with. The Web in particular is driving innovation in new ways of processing information as the data footprints of Internet-scale applications become prohibitive using traditional SQL database engines.

• When it comes to database processing today, change is being driven by (at least) four factors:

– Speed. The seek times of physical storage is not keeping pace with improvements in network speeds.

– Scale. The difficulty of scaling the RDBMS out efficiently (i.e. clustering beyond a handful of servers is notoriously hard.)

– Integration. Today's data processing tasks increasingly have to access and combine data from many different non-relational sources, often over a network.

– Volume. Data volumes have grown from tens of gigabytes in the 1990s to hundreds of terabytes and often petabytes in recent years.

RDBMS and Hadoop: Apples and Oranges?

• Below is Figure 1 - a comparison of the overall differences between

Database RDBMS and MapReduce-based systems such as Hadoop

• From this it's clear that the MapReduce model cannot replace the

traditional enterprise RDBMS. However, it can be a key enabler of a

number of interesting scenarios that can considerably increase

flexibility, turn-around times, and the ability to tackle problems that

weren't possible before.

• With Database RDBMS platforms, SQL-based processing of data sets

tends to fall away and not scale linearly after a specific volume ceiling,

usually just a handful of nodes in a cluster. With MapReduce, you can

consistently obtain performance gains by increasing the size of the

cluster. In other words, double the size of Hadoop cluster and a job will

run twice as fast - quadruple it will rub four times faster - its the same

linear relationship, irrespective of data volume and throughput.

Comparing Data in DWH, Appliances, Hadoop Clusters and Analytics Engines

RDBMS DWH DWH Appliance Hadoop Cluster Analytics Appliance

Data size Gigabytes Terabytes Petabytes Petabytes

Access Interactive and

batch

Interactive and batch Batch Interactive

Structure Fixed schema Fixed schema Flexible schema Flexible schema

Language SQL SQL Non-procedural

Languages (Java, C++,

Ruby, “R” etc)

Non-procedural

Languages (Java, C++,

Ruby, “R” etc)

Data Integrity High High Low Very High

Architecture Shared memory -

SMP

Shared nothing - MPP Hadoop DFS In-memory Processing

– GPGPUs / SSDs

Virtualisation Partitions / Regions MPP / Nodal MPP / Clustered MPP / Clustered

Scaling Non-linear Nodal / Linear Clustered / Linear Clustered / Linear

Updates Read and write Write once, read many Write once, read many Write once, read many

Selects Row-based Set-based Column-based Array-based

Latency Low – Real-time Low – Near Real-time High – Historic

Reporting

Very Low – Real-time

Analytics

Figure 1: Comparing RDBMS to MapReduce

Hadoop Framework

• These datasets would previously have been very challenging and expensive to take on with a traditional RDBMS using standard bulk load and ETL approaches. Never mind trying to efficiently combining multiple data sources simultaneously or dealing with volumes of data that simply can't reside on any single machine (or often even dozens). Hadoop deals with this by using a distributed file system (HDFS) that's designed to deal coherently with datasets that can only reside across distributed server farms. HDFS is also fault resilient and so doesn't impose the overhead of RAID drives and mirroring on individual nodes in a Hadoop compute cluster, allowing the use of truly low cost commodity hardware.

• So what does this specifically mean to enterprise users that would like to improve their data processing capabilities? Well, first there are some catches to be aware of. Despite enormous strengths in distributed data processing and analysis, MapReduce is not good in some key areas that the RDMS is extremely strong in (and vice versa). The MapReduce approach tends to have high latency (i.e. not suitable for real-time transactions) compared to relational databases and is strongest at processing large volumes of write-once data where most of the dataset needs to be processed at one time. The RDBMS excels at point queries and updates, while MapReduce is best when data is written once and read many times.

• The story is the same with structured data, where the RDBMS and the rules of database normalization identified precise laws for preserving the integrity of structured data and which have stood the test of time. MapReduce is designed for a less structured, more federated world where schemas may be used but data formats can be much looser and freeform.

The Emerging “Big Data” Stack

Targeting – Map / Reduce

Consume – End-User Data

Data Acquisition – High-Volume Data Flows

– Mobile Enterprise Platforms (MEAP’s)

Apache Hadoop Framework HDFS, MapReduce, Metlab “R” Autonomy, Vertica

Smart Devices Smart Apps Smart Grid

Clinical Trial, Morbidity and Actuarial Outcomes Market Sentiment and Price Curve Forecasting Horizon Scanning,, Tracking and Monitoring Weak Signal, Wild Card and Black Swan Event Forecasting

– Data Delivery and Consumption

News Feeds and Digital Media Global Internet Content Social Mapping Social Media Social CRM

– Data Discovery and Collection

– Analytics Engines - Hadoop

– Data Presentation and Display

Excel Web Mobile

– Data Management Processes Data Audit Data Profile Data Quality Reporting Data Quality Improvement Data Extract, Transform, Load

– Performance Acceleration GPU’s – massive parallelism SSD’s – in-memory processing DBMS – ultra-fast database replication

– Data Management Tools DataFlux Embarcadero Informatica Talend

– Info. Management Tools Business Objects Cognos Hyperion Microstrategy

Biolap Jedox Sagent Polaris

Teradata SAP HANA Netezza (now IBM) Greenplum (now EMC2) Extreme Data xdg Zybert Gridbox

– Data Warehouse Appliances

Ab Initio Ascential Genio Orchestra

Hadoop Framework

• Each of these factors is presently driving interest in alternatives that are significantly better at dealing with these requirements. I'll be clear here: The relational database has proven to be incredibly versatile and is the right tool for the majority of business needs today. However, the edge cases for many large-scale business applications are moving out into areas where the RDBMS is often not the strongest option. One of the most discussed new alternatives at the moment is Hadoop, a popular open source implementation of MapReduce. MapReduce is a simple yet very powerful method for processing and analyzing extremely large data sets, even up to the multi-petabyte level. At its most basic, MapReduce is a process for combining data from multiple inputs (creating the "map"), and then reducing it using a supplied function that will distill and extract the desired results. It was originally invented by engineers at Google to deal with the building of production search indexes. The MapReduce technique has since spilled over into other disciplines that process vast quantities of information including science, industry, and systems management. For its part, Hadoop has become the leading implementation of MapReduce.

• While there are many non-relational database approaches out there today (see my emerging IT and business topics post for a list), nothing currently matches Hadoop for the amount of attention it's receiving or the concrete results that are being reported in recent case studies. A quick look at thelist of organizations that have applications powered by Hadoop includes Yahoo! with over 25,000 nodes (including a single, massive 4,000 node cluster), Quantcast which says it has over 3,000 cores running Hadoop and currently processes over 1PB of data per day, and Adknowledge who uses Hadoop to process over 500 million clickstream events daily using up to 200 nodes

HP HAVEn Big Data Platform

Informatica / Hortonworks Vibe

Big Data – Products

The MapReduce technique has spilled over into many other disciplines that process vast

quantities of information including science, industry, and systems management. The Apache

Hadoop Library has become the most popular implementation of MapReduce – with

framework implementations from Cloudera, Hortonworks and MAPR

Split-Map-Shuffle-Reduce Process

Big Data Consumers

Split Map Shuffle Reduce

Key / Value Pairs Actionable Insights Data Provisioning Raw Data

Apache Hadoop Component Stack

HDFS

MapReduce

Pig

Zookeeper

Hive

HBase

Oozie

Mahoot

Hadoop Distributed File System (HDFS)

Scalable Data Applications Framework

Procedural Language – abstracts low-level MapReduce operators

High-reliability distributed cluster co-ordination

Structured Data Access Management

Hadoop Database Management System

Job Management and Data Flow Co-ordination

Scalable Knowledge-base Framework

Data Management Component Stack

Informatica

Drill

Millwheel

Informatica Big Data Edition / Vibe Data Stream

Data Analysis Framework

Data Analytics on-the-fly + Extract – Transform – Load Framework

Flume

Sqoop

Scribe

Extract – Transform - Load

Extract – Transform - Load

Extract – Transform - Load

Talend Extract – Transform - Load

Pentaho Extract – Transform – Load Framework + Data Reporting on-the-fly

Big Data Storage Platforms

Autonomy

Vertica

MongoDB

HP Unstructured Data DBMS

HP Columnar DBMS

High-availability DBMS

CouchDB Couchbase Database Server for Big Data with NoSQL / Hadoop

Integration

Pivotal Pivotal Big Data Suite – GreenPlum, GemFire, SQLFire, HAWQ

Cassandra Cassandra Distributed Database for Big Data with NoSQL and

Hadoop Integration

NoSQL NoSQL Database for Oracle, SQL/Server, Couchbase etc.

Riak Basho Technologies Riak Big Data DBMS with NoSQL / Hadoop

Integration

Big Data Analytics Engines and Appliances

Alpine

Karmasphere

Kognito

Alpine Data Studio - Advanced Big Data Analytics

Karmasphere Studio and Analyst – Hadoop Customer Analytics

Kognito In-memory Big Data Analytics MPP Platform

Skytree

Redis

Skytree Server Artificial Intelligence / Machine Learning Platform

Redis is an open source key-value database for AWS, Pivotal etc.

Teradata Teradata Appliance for Hadoop

Neo4j Crunchbase Neo4j - Graphical Database for Big Data

InfiniDB Columnar MPP open-source DB version hosted on GitHub

Big Data Analytics Engines / Appliances

Big Data Analytics and Visualisation Platforms

Tableaux Tableaux - Big Data Visualisation Engine

Eclipse Symentec Eclipse - Big Data Visualisation

Mathematica Mathematical Expressions and Algorithms

StatGraphics Statistical Expressions and Algorithms

FastStats Numerical computation, visualization and programming toolset

MatLab

R

Data Acquisition and Analysis Application Development Toolkit

“R” Statistical Programming / Algorithm Language

Revolution Revolution Analytics Framework and Library for “R”

Hadoop / Big Data Extended Infrastructure Stack

SSD Solid State Drive (SSD) – configured as cached memory / fast HDD

CUDA CUDA (Compute Unified Device Architecture)

GPGPU GPGPU (General Purpose Graphical Processing Unit Architecture)

IMDG IMDG (In-memory Data Grid – extended cached memory)

Vibe

Splunk

High Velocity / High Volume Machine / Automatic Data Streaming

High Velocity / High Volume Machine / Automatic Data Streaming

Ambari High-availability distributed cluster co-ordination

YARN Hadoop Resource Scheduling

Big Data Extended Architecture Stack

Cloud-based Big-Data-as-a-Service and Analytics

AWS Amazon Web Services (AWS) – Big Data-as-a-Service (BDaaS)

Elastic Compute Cloud (ECC) and Simple Storage Service (S3)

1010 Data Big Data Discovery, Visualisation and Sharing Cloud Platform

SAP HANA SAP HANA Cloud - In-memory Big Data Analytics Appliance

Azure Microsoft Azure Data-as-a-Service (DaaS) and Analytics

Anomaly 42 Anomaly 42 Smart-Data-as-a-Service (SDaaS) and Analytics

Workday Workday Big-Data-as-a-Service (BDaaS) and Analytics

Google Cloud Google Cloud Platform – Cloud Storage, Compute Platform,

Firebrand API Resource Framework

Apigee Apigee API Resource Framework

Gartner Magic Quadrant for BI and Analytics Platforms

Hadoop Framework Distributions

FEATURE Hortonworks Cloudera MAPR Pivotal

Open Source Hadoop Library Yes Yes Yes Pivotal HD

Support Yes Yes Yes Yes

Professional Services Yes Yes Yes Yes

Catalogue Extensions Yes Yes Yes Yes

Management Extensions Yes Yes Yes

Architecture Extensions Yes Yes

Infrastructure Extensions Yes Yes

Library

Support

Services

Catalogue

Job Management

Library

Support

Services

Catalogue

Hortonworks Cloudera MAPR

Library

Support

Services

Catalogue

Job Management

Resilience

High Availability

Performance

Pivotal

Library

Support

Services

Catalogue

Job Management

Resilience

High Availability

Performance

Gartner Magic Quadrant for BI

Data Warehouse Appliance / Real-time Analytics Engine Price Comparison

Manufacturer Server

Configuration Cached Memory

Server

Type

Software

Platform Cost (est.)

SAP HANA

(BO BW)

32-node (4

Channels x 8 CPU)

1.3 Terabytes

SMP Proprietary $ 6,000,,000

Teradata 20-node (2

Channels x 10 CPU)

1 Terabyte

MPP Proprietary $ 1,000,000

Netezza

(now IBM)

20-node (2

Channels x 10 CPU)

1 Terabyte

MPP Proprietary $ 180,000

IBM ex5 (non-HANA

configuration)

32-node (4

Channels x 8 CPU)

1.3 Terabytes

SMP Proprietary $ 120,000

Greenplum (now

Pivotal)

20-node (2

Channels x 10 CPU)

1 Terabyte

MPP Open Source $ 20,000

XtremeData xdb 20-node (2

Channels x 10 CPU)

1 Terabyte

MPP Open Source $ 18,000

Zybert Gridbox 48-node (4

Channels x 12 CPU)

20 Terabytes

SMP Open Source $ 60,000

Risk Research Philosophies and

Investigative Methods • This section aims to discuss Risk Research Philosophies in detail, in order to develop

a general awareness and understanding of the options - and to describe a rigorous

approach to Research Methods and Scope as a mandatory precursor to the full

Research Design. Denzin and Lincoln (2003) and Kvale (1996) highlight how

different Research Philosophies can result in much tension amongst stakeholders.

• When undertaking any research of either a Scientific or Humanistic nature, it is most

important to consider, compare and contrast all of the varied and diverse Research

Philosophies and Paradigms that are available to the researcher and supervisor -

along with their respective treatments of ontology and epistemology issues.

• Since Research Philosophies and paradigms often describe dogma, perceptions,

beliefs and assumptions about the nature of reality and truth (and knowledge of that

reality) - they can radically influence the way in which the research is undertaken,

from design through to outcomes and conclusions. It is important to understand and

discuss these contrasting aspects in order that approaches congruent to the nature

and aims of the particular study or inquiry in question, are adopted - and to ensure

that researcher and supervisor biases are understood, exposed, and mitigated.

Risk Research Methods

• When undertaking any research of either a Scientific or Humanistic nature, it is most important for the researcher and supervisor to consider, compare and contrast all of the varied and diverse Research Philosophies and Paradigms, Data Analysis Methods and Techniques available - along with the express implications of their treatment of ontology and epistemology issues....,

Risk Research

• Traditional approaches to risk studies and risk management are based upon the

paradigm of risk as an event adequately characterised by a single feature. This

simplistic conceptualisation of risk leads to the use of analysis tools and models

which do not reliably integrate qualitative and quantitative information or model the

interconnectivity of the dynamic behaviour of risks. For complex systems, like an

economy or financial organisations, a new paradigm or philosophy is required to

understand how the constituent parts interact to create behaviours not predictable

from the ‘sum of the parts’. Systems theory provides a more robust conceptual

framework which views risk as an emerging property arising from the complex and

adaptive interactions which occur within companies, sectors and economies.

• Risk appetite is a concept that many practitioners find confusing and hard to

implement. The fundamental problem is that there is no common measure for all

risks, and it is not always clear how different risk factors should be limited in order to

remain within an overall “appetite”. Attempts are generally made to force everything

into an impact on profit or capital but this is problematic when businesses and risk

decisions become more complex. There is a lack of real understanding about how

they would propagate, or indeed how the appetite may shift or evolve to have a

preference for specific risks.

Risk Research

• By thinking holistically, risk appetite can be viewed as “our comfort and preference for

accepting a series of interconnected uncertainties related to achieving our strategic

goals”. By making those uncertainties and the connectivity of the underlying drivers

explicit, it is possible for decision makers to define their risk appetite and monitor

performance against it more effectively. The ability to link multiple factors back to

financial outcomes also makes the challenge of expressing risk appetite in those

terms more tractable.

• Similarly, the identification and assessment of emerging risks can become more

robust by using a systems approach that enables a clearer understanding of the

underlying dynamics that exist between the key factors of the risks themselves. It is

possible to identify interactions in a system that may propagate hitherto unseen risks.

Emerging risks can be viewed as evolving risks from a complex system. It is also

known that such systems exhibit signals in advance of an observable change in

overall performance. Knowing how to spot and interpret those signs is the key to

building a scientific and robust emerging risk process. Also it is becoming increasingly

clear that risk appetite and emerging risks are interconnected in numerous complex

relationships over many layers.

Risk Research

• Assuming that strategic goals are already identified, establishing a risk appetite framework

comprises two distinct parts, one top down and the other bottom up. First, it is necessary

to describe how much uncertainty about the achievement of specific business goals is

acceptable, and what the key sources of that uncertainty are. Second, it is necessary to

identify the key operational activities or actions which contribute towards each source of

uncertainty and then apply the necessary limits to those activities to maintain performance

within the desired risk appetite.

• Systems techniques used in the case study proved extremely effective at helping

businesses to explain their understanding of how uncertainty arises around their business

goals. Cognitive mapping was used to elicit a robust understanding of the business

dynamics creating uncertainty in business goals. This process was useful for engaging the

business and capturing their collective knowledge of the risk appetite problem.

• By carrying out a mathematically based analysis on the cognitive maps it is possible to

quickly and objectively identify which parts of the description are most important in driving

explaining the uncertainties we are attempting to constrain. It also highlights areas which

have not been particularly well described or understood, prompting further discussion and

analysis. This provides a hypothesis for our risk appetite, and associated limit, framework.

Risk Research

Bayesian Networks

• Bayesian Networks are proposed as a mechanism to provide a dynamic model of how

various risk factors connect and interact. This links the behaviour of the operational

activities to the levels of risk they produce and can be parameterised through a

combination of qualitative and quantitative data. Bayesian Networks permit evidence

to propagate up and down the model, providing the business with a robust method for

determining risk limits by setting the level of risk to be at the risk appetite point and

observing what level the limits should be to ensure compliance with this level of risk.

• Alternatively, the observed indicator values can be entered and the implied level of

risk is computed. Making this linkage explicit provides a mechanism for companies to

understand more immediately where their risk exposure is coming from and how to

control it.

Risk = Impact x Probability

Risk Complexity Map

Enterprise Risk Management

Avoid / Mitigate

Discover

Prioritise

Evaluate

Scan and Identify

Track and Monitor Investigate and Research

Publish and Socialise

Risk Register and Balance Sheet Provisioning

Threat Categories and Risk Analysis

Risk Avoidance and Mitigation

Risk Scenarios and Impact Analysis

Strategy and Foresight Process

Communicate

Discover

Understand

Evaluate

Scan and Identify

Track and Monitor Investigate and Research

Publish and Socialise

Desired Outcomes, Goals and Objectives

Vision and Mission

Strategy / Foresight Epics and Stories,

Scenarios and Use Cases

Strategy / Foresight Themes and Categories

Horizon Scanning

Publish and

Socialise

Investigate and

Research

Scan and Identify

Track and Monitor

Communicate Discover

Understand Evaluate

Horizon Scanning – Human Activity

Environment Scanning – Natural Phenomena

Horizon Scanning, Tracking and Monitoring

Disease / Pandemics

Horizon Scanning

Geo-political Shock Wave

Socio-Demographic Shock Wave

Economic Shock Wave

Technology Shock Wave

Ecological Shock Wave

Biomedical Shock Wave

Environment Shock Wave

Climate Shock Wave

Culture Change

Climate Change

Innovation

Money Supply / Commodity Price / Sovereign Default

War, Terrorism, Revolution

Population Curves / Extinction Human Activity / Natural Disasters

Horizon Scanning

Environment Scanning

Human Activity

Natural Phenomena

Environment Scanning, Tracking and Monitoring – Extinction Level Scenarios

Event Type Force Random Event Weak Signal Strong

Signal

Wild card Black Swan

1 Hyperspace

Event

String

Theory

The “Big Bang”

- the creation of

the Universe

Gravity Waves

- evidence of

early inflation

CMB - the

clustering

of matter

Expansion

- Clusters of

mass rip apart

Slow heat-

death of the

Universe

2 Hyperspace

Event

String

Theory

Membranes

collide in

Hyperspace

(none – event

unfolds at the

speed of light)

(none –

speed of

light event)

(none – event

unfolds at the

speed of light)

The abrupt

end of the

Universe

3 Singularity

Event

Quantum

Dynamics

Black Hole

appears in the

Solar System

(none – event

unfolds at the

speed of light)

(none –

speed of

light event)

(none – event

unfolds at the

speed of light)

The end of

the Solar

System

4 Alien

Contact

Event

Biological

Disease

Contact with the

foreign bio-cloud

of an Alien host

People start

collapsing in the

street

Global

Pandemic

declared

Hospitals and

Mortuaries

inundated by

disease

victims

Disease –

90-95 % of the

total Human

Population lost

5 Alien

Contact

Event

Biological

Predation

Contact with an

Alien invasion and

exposure to WMD

People are being

predated in the

street

Global

Conflict

event

declared

Hospitals and

Mortuaries

inundated by

attack victims

Attack –

90-95 % of the

total Human

Population lost

6 Global

Warfare

Human

Conflict /

WMD

Exposure to

Weapons of Mass

Destruction

People are being

predated in the

street

Global

Conflict

event

declared

Hospitals and

Mortuaries

inundated by

attack victims

Attack –

90-95 % of the

total Human

Population lost

Weak Signals and Wild Cards

Publish and

Socialise

Investigate and

Research

Scan and Identify

Track and Monitor

Communicate Discover

Understand Evaluate

Random Event

Strong Signal

Weak Signal

Wild Card

Scenario Planning and Impact Analysis

• Scenario Planning and Impact Analysis is the archetypical method for futures studies

because it embodies the central principles of the discipline:

– The future is uncertain - so we must prepare for a wide range of possible, probable

and alternative futures, not just the future that we desire (or hope) will happen.....

– It is vitally important that we think deeply and creatively about the future, else we run

the risk of being either unprepared for, or surprised by events – or even both.....

• Scenarios contain the stories of these multiple futures - from the Utopian to the Dystopian,

from the preferred to the expected, from the Wild Card to the Black Swan - in forms which

are analytically coherent and imaginatively engaging. A good scenario grabs our attention

and says, ‘‘Take a good look at this future. This could be your future - are you prepared ?’’

• As consultants and organizations have come to recognize the value of scenarios, they

have also latched onto one scenario technique – a very good one in fact – as the default

for all their scenario work. That technique is the Royal Dutch Shell / Global Business

Network (GBN) matrix approach, created by Pierre Wack in the 1970s and popularized by

Schwartz (1991) in the Art of the Long View and Van der Heijden (1996) in Scenarios: The

Art of Strategic Conversations. In fact, Millett (2003, p. 18) calls it the ‘‘gold standard of

corporate scenario generation.’’

Scenario Planning and Impact Analysis

Published Scenarios

Evaluated Scenarios

Monte Carlo

Simulation

Discovered Scenarios

Communicate Discover

Understand Evaluate

Non-linear Models

Cluster Analysis

Profile Analysis

Impact Analysis

SCENARIO

From sports to scientific research, a surprising range of industries will begin to find value in big data.....

4D Geospatial Analytics • The profiling and analysis of

large aggregated datasets in

order to determine a ‘natural’

structure of groupings provides

an important technique for many

statistical and analytic

applications. Cluster analysis

on the basis of profile similarities

or geographic distribution is a

method where no prior

assumptions are made

concerning the number of

groups or group hierarchies and

internal structure. Geo-

demographic techniques are

frequently used in order to

profile and segment populations

by ‘natural’ groupings - such as

common behavioural traits,

Clinical Trial, Morbidity or

Actuarial outcomes - along with

many other shared

characteristics and common

factors.....

4D Geospatial Analytics – The Temporal Wave

• The Temporal Wave is a novel and innovative method for Visual Modelling and Exploration

of Geospatial “Big Data” - simultaneously within a Time (history) and Space (geographic)

context. The problems encountered in exploring and analysing vast volumes of spatial–

temporal information in today's data-rich landscape – are becoming increasingly difficult to

manage effectively. In order to overcome the problem of data volume and scale in a Time

(history) and Space (location) context requires not only traditional location–space and

attribute–space analysis common in GIS Mapping and Spatial Analysis - but now with the

additional dimension of time–space analysis. The Temporal Wave supports a new method

of Visual Exploration for Geospatial (location) data within a Temporal (timeline) context.

• This time-visualisation approach integrates Geospatial (location) data within a Temporal

(timeline) data along with data visualisation techniques - thus improving accessibility,

exploration and analysis of the huge amounts of geo-spatial data used to support geo-

visual “Big Data” analytics. The temporal wave combines the strengths of both linear

timeline and cyclical wave-form analysis – and is able to represent data both within a Time

(history) and Space (geographic) context simultaneously – and even at different levels of

granularity. Linear and cyclic trends in space-time data may be represented in combination

with other graphic representations typical for location–space and attribute–space data-

types. The Temporal Wave can be used in roles as a time–space data reference system,

as a time–space continuum representation tool, and as time–space interaction tool.

4D Geospatial Analytics – London Timeline

4D Geospatial Analytics – London Timeline

• How did London evolve from its creation as a Roman city in 43AD into the crowded, chaotic cosmopolitan megacity we see today? The London Evolution Animation takes a holistic view of what has been constructed in the capital over different historical periods – what has been lost, what saved and what protected.

• Greater London covers 600 square miles. Up until the 17th century, however, the capital city was crammed largely into a single square mile which today is marked by the skyscrapers which are a feature of the financial district of the City.

• This visualisation, originally created for the Almost Lost exhibition by the Bartlett Centre for Advanced Spatial Analysis (CASA), explores the historic evolution of the city by plotting a timeline of the development of the road network - along with documented buildings and other features – through 4D geospatial analysis of a vast number of diverse geographic, archaeological and historic data sets.

• Unlike other historical cities such as Athens or Rome, with an obvious patchwork of districts from different periods, London's individual structures scheduled sites and listed buildings are in many cases constructed gradually by parts assembled during different periods. Researchers who have tried previously to locate and document archaeological structures and research historic references will know that these features, when plotted, appear scrambled up like pieces of different jigsaw puzzles – all scattered across the contemporary London cityscape.

History of Digital Epidemiology

• Doctor John Snow (15 March 1813 – 16

June 1858) was an English physician and a

leading figure in the adoption of anaesthesia

and medical hygiene. John Snow is largely

credited with sparking and pursuing a total

transformation in Public Health and epidemic

disease management and is considered one

of the fathers of modern epidemiology in part

because of his work in tracing the source of

a cholera outbreak in Soho, London, in 1854.

• John Snows’ investigation and findings into

the Broad Street cholera outbreak - which

occurred in 1854 near Broad Street in the

London district of Soho in England - inspired

fundamental changes in both the clean and

waste water systems of London, which led to

further similar changes in other cities, and a

significant improvement in understanding of

Public Health around the whole of the world.

History of Digital Epidemiology

• The Broad Street cholera outbreak of

1854 was a major cholera epidemic or

severe outbreak of cholera which

occurred in 1854 near Broad Street in

the London district of Soho in England .

• This cholera outbreak is best known for

statistical analysis and study of the

epidemic by the physician John Snow

and his discovery that cholera is spread

by contaminated water. This knowledge

drove improvement in Public Health with

mass construction of sanitation facilities

from the middle of the19th century.

• Later, the term "focus of infection" would

be used to describe factors such as the

Broad Street pump – where Social and

Environmental conditions may result in the outbreak of local infectious diseases.

History of Digital Epidemiology • It was the study of

cholera epidemics, particularly in Victorian England during the middle of the 19th century, which laid the foundation for epidemiology - the applied observation and surveillance of epidemics and the statistical analysis of public health data.

• This discovery came at a time when the miasma theory of disease transmission by noxious “foul air” prevailed in the medical community.

History of Digital Epidemiology

Modern epidemiology has its origin with the study of Cholera

Broad Street cholera outbreak of 1854

History of Digital Epidemiology

Modern epidemiology has its origin with the study of Cholera.

• It was the study of cholera epidemics, particularly in Victorian England

during the middle of the 19th century, that laid the foundation for the science

of epidemiology - the applied observation and surveillance of epidemics and

the statistical analysis of public health data. It was during a time when the

miasma theory of disease transmission prevailed in the medical community.

• John Snow is largely credited with sparking and pursuing a transformation in

Public Health and epidemic disease management from the extant paradigm

in which communicable illnesses were thought to have been carried by

bad, malodorous airs, or "miasmas“ - towards a new paradigm which would

begin to recognize that virulent contagious and infectious diseases are

communicated by various other means – such as water being polluted by

human sewage. This new approach to disease management recognised that

contagious diseases were either directly communicable through contact with

infected individuals - or via vectors of infection (water, in the case of cholera)

which are susceptible to contamination by viral and bacterial agents.

History of Digital Epidemiology • This map is John Snow’s

famous plot of the 1854 Broad Street Cholera Outbreak in London. By plotting epidemic data on a map like this, John Snow was able to identify that the outbreak was centred on a specific water pump.

• Interviews confirmed that outlying cases were from people who would regularly walk past the pump and take a drink. He removed the handle off the water pump and the outbreak ended almost overnight.

• The cause of cholera (bacteria Vibria cholerae) was unknown at the time, and Snow’s important work with cholera in London during the 1850s is considered the beginning of modern epidemiology. Some have even gone so far as to describe Snow’s Broad Street Map as the world’s first GIS.

History of Digital Epidemiology

Broad Street cholera outbreak of 1854

Clinical Risk Types

Clinical Risk Types

Clinical Risk Group

Employee

Patient

B

A

Human Risk Process

Risk

D

Morbidity Risk Types

Morbidity Risk Group

C

Legal Risk

F

3rd Party Risk

G

C

Technology Risk

Trauma Risk

E

Morbidity Risk

H E

J

G

A

I D

Immunological System Risk

Sponsorship

Stakeholders Disease

Risk

Shock Risk

Cardiovascular

System Risk

Pulmonary System Risk

Toxicity Risk

Organ Failure Risk

- Airways

- Conscious

- Bleeding

Triage Risk

- Performance

- Finance

- Standards

Compliance Risk

H

Patient Risk

Neurological

System Risk F

B

Predation Risk

Risk Complexity Map

• Case Study • Pandemics

• Case Study • Pandemics

• Pandemics - during a pandemic episode, such as the recent Ebola outbreak, current

policies emphasise the need to ground decision-making on empiric evidence. This section

studies the tension that remains in decision-making processes when their is a sudden and

unpredictable change of course in an outbreak – or when key evidence is weak or ‘silent’.

• The current focus in epidemiology is on the ‘known unknowns’ - factors with which we are

familiar in the pandemic risk assessment processes. These risk processes cover, for

example, monitoring the course of the pandemic, estimating the most affected age groups,

and assessing population-level clinical and pharmaceutical interventions. This section

looks for the ‘unknown unknowns’ - factors with a silence or lack of evidence, factors which

we have only limited or weak understanding in the pandemic risk assessment processes.

• Pandemic risk assessment shows that any developing, new and emerging or sudden and

unpredictable change in the pandemic situation does not accumulate a robust body of

evidence for decision making. These uncertainties may be conceptualised as ‘unknown

unknowns’, or “silent evidence”. Historical and archaeological pandemic studies indicate

that there may well have been evidence that was not discovered, known or recognised.

This section looks at a new method to discover “silent evidence” - unknown factors - that

affect pandemic risk assessment - by focusing on the tension under pressure that impacts

upon the actions of key decision-makers in the pandemic risk decision-making process.

Antonine Plague (Smallpox ) AD 165-180

Pandemic Black Swan Events Black Swan Pandemic Type / Location Impact Date

Malaria For the entirety of human history,

Malaria has been a pathogen

The Malaria pathogen kills more

humans than any other disease 20 kya – present

Smallpox (Antonine Plague) Smallpox Roman Empire / Italy Smallpox is the 2nd worst killer 165-180

Black Death (Plague of Justinian) Bubonic Plague – Roman Empire 50 million people died 6th century

Black Death (Late Middle Ages) Bubonic Plague – Europe 75 to 200 million people died 1340–1400

Smallpox Amazonian Basin Indians 90% Amazonian Indians died 16th century

Tuberculosis Western Europe, 18th - 19th c 900 deaths per 100,000 pop. 18th - 19th c

Syphilis Global pandemic – invariably fatal 10% of Victorian men carriers 19th century

1st Cholera Pandemic Global pandemic Started in the Bay of Bengal 1817-1823

2nd Cholera Pandemic Global pandemic (arrived in London in 1832) 1826-1837

Spanish Flu Global pandemic 50 million people died 1918

Smallpox Global pandemic 300 million people died in 20th c Eliminated 20th c

Poliomyelitis Global pandemic Contracted by up to 500,000

persons per year 1950’s/1960’s 1950’s -1960’s

AIDS Global pandemic – mostly fatal 10% Sub-Saharans are carriers Late 20th century

Ebola West African epidemic – 50% fatal Sub-Saharan Africa epicentre Late 20th century

For the entirety of human history, Malaria has been the most lethal pathogen to attack man

Pandemic Black Swan Event Types

Type Force Epidemiology Black Swan Event

1 Malaria Parasitic

Biological

Disease

The Malaria pathogen has killed more humans than any other disease. Human

malaria most likely originated in Africa and has coevolved along with its hosts,

mosquitoes and non-human primates. The first evidence of malaria parasites

was found in mosquitoes preserved in amber from the Palaeogene period that

are approximately 30 million years old. Malaria may have been a human

pathogen for the entire history of the species. Humans may have originally

caught Plasmodium falciparum from gorillas. About 10,000 years ago, a period

which coincides with the development of agriculture (Neolithic revolution) -

malaria started having a major impact on human survival. A consequence was

natural selection for sickle-cell disease, thalassaemias, glucose-6-phosphate

dehydrogenase deficiency, ovalocytosis, elliptocytosis and loss of the Gerbich

antigen (glycophorin C) and the Duffy antigen on erythrocytes because such

blood disorders confer a selective advantage against malaria infection (balancing

selection). The first known description of malaria dates back 4000 years to 2700

B.C. China where ancient writings refer to symptoms now commonly associated

with malaria. Early malaria treatments were first developed in China from

Quinghao plant, which contains the active ingredient artemisinin, re-discovered

and still used in anti-malaria drugs today. Largely overlooked by researchers is

the role of disease and epidemics in the fall of Rome. Three major types of

inherited genetic resistance to malaria (sickle-cell disease, thalassaemias, and

glucose-6-phosphate dehydrogenase deficiency) were all present in the

Mediterranean world 2,000 years ago, at the time of the Roman Empire.

Pandemic Black Swan Event Types

Type Force Epidemiology Black Swan Event

2 Smallpox Viral

Biological

Disease

The history of smallpox holds a unique place in medical history. One of the

deadliest viral diseases known to man, it is the first disease to be treated by

vaccination - and also the only disease to have been eradicated from the

face of the earth by vaccination. Smallpox plagued human populations for

thousands of years. Researchers who examined the mummy of Egyptian

pharaoh Ramses V (died 1157 BCE) observed scarring similar to that from

smallpox on his remains. Ancient Sanskrit medical texts, dating from about

1500 BCE, describe a smallpox-like illness. Smallpox was most likely

present in Europe by about 300 CE. – although there are no unequivocal

records of smallpox in Europe before the 6th century CE. It has been

suggested that it was a major component of the Plague of Athens that

occurred in 430 BCE, during the Peloponnesian Wars, and was described

by Thucydides. A recent analysis of the description of clinical features

provided by Galen during the Antonine Plague that swept through the

Roman Empire and Italy in 165–180, indicates that the probable cause was

smallpox. In 1796, after noting Smallpox immunity amongst milkmaids –

Edward Jenner carried out his now famous experiment on eight-year-old

James Phipps, using Cow Pox as a vaccine to confer immunity to Smallpox.

Some estimates indicate that 20th century worldwide deaths from smallpox

numbered more than 300 million. The last known case of wild smallpox

occurred in Somalia in 1977 – until recent outbreaks in Pakistan and Syria.

Pandemic Black Swan Event Types

Type Force Epidemiology Black Swan Event

3 Bubonic

Plague

Bacterial

Biological

Disease

The Bubonic Plague – or Black Death – was one of the most devastating

pandemics in human history, killing an estimated 75 to 200 million people

and peaking in Europe in the years 1348–50 CE. The Bubonic Plague is a

bacterial disease – spread by fleas carried by Asian Black Rats - which

originated in or near China and then travelled to Italy, overland along the Silk

Road, or by sea along the Silk Route. From Italy the Black Death spread

onwards through other European countries. Research published in 2002

suggests that the Black Death began in the spring of 1346 in the Russian

steppe region, where a plague reservoir stretched from the north-western

shore of the Caspian Sea into southern Russia. Although there were

several competing theories as to the etiology of the Black Death, analysis of

DNA from victims in northern and southern Europe published in 2010 and

2011 indicates that the pathogen responsible was the Yersinia pestis

bacterium, possibly causing several forms of plague. The first recorded

epidemic ravaged the Byzantine Empire during the sixth century, and was

named the Plague of Justinian after emperor Justinian I, who was infected

but survived through extensive treatment. The epidemic is estimated to have

killed approximately 50 million people in the Roman Empire alone. During

the Late Middle Ages (1340–1400) Europe experienced the most deadly

disease outbreak in history when the Black Death, the infamous pandemic

of bubonic plague, peaked in 1347, killing one third of the human population.

Pandemic Black Swan Event Types

Type Force Epidemiology Black Swan Event

4 Syphilis Bacterial

Biological

Disease

Syphilis - the exact origin of syphilis is unknown. There are two primary

hypotheses: one proposes that syphilis was carried from the Americas to

Europe by the crew of Christopher Columbus, the other proposes that

syphilis previously existed in Europe but went unrecognized. These are

referred to as the "Columbian" and "pre-Columbian" hypotheses. In late 2011

newly published evidence suggested that the Columbian hypothesis is valid.

The appearance of syphilis in Europe at the end of the 1400s heralded

decades of death as the disease raged across the continent. The first

evidence of an outbreak of syphilis in Europe were recorded in 1494/1495

in Naples, Italy, during a French invasion. First spread by returning French

troops, the disease was known as “French disease”, and it was not until

1530 that the term "syphilis" was first applied by the Italian physician and

poet Girolamo Fracastoro. By the 1800s it had become endemic, carried by

as many as 10% of men in some areas - in late Victorian London this may

have been as high as 20%. Invariably fatal, associated with extramarital sex

and prostitution, syphilis was accompanied by enormous social stigma. The

secretive nature of syphilis helped it spread - disgrace was such that many

sufferers hid their symptoms, while others carrying the latent form of the

disease were unaware they even had it. Treponema pallidum, the syphilis

causal organism, was first identified by Fritz Schaudinn and Erich Hoffmann

in 1905. The first effective treatment (Salvarsan) was developed in 1910

by Paul Ehrlich which was followed by the introduction of penicillin in 1943.

Pandemic Black Swan Event Types

Type Force Epidemiology Black Swan Event

5 Tuberculosis Bacterial

Biological

Disease

Tuberculosis - the evolutionary origins of the Mycobacterium tuberculosis

indicates that the most recent common ancestor was a human-specific

pathogen, which encountered an evolutionary bottleneck leading to

diversification. Analysis of mycobacterial interspersed repetitive units has

allowed dating of this evolutionary bottleneck to approximately 40,000 years

ago, which corresponds to the period subsequent to the expansion of Homo

sapiens out of Africa. This analysis of mycobacterial interspersed repetitive

units also dated the Mycobacterium bovis lineage as dispersing some 6,000

years ago. Tuberculosis existed 15,000 to 20,000 years ago, and has been

found in human remains from ancient Egypt, India, and China. Human

bones from the Neolithic show the presence of the bacteria, which may be

linked to early farming and animal domestication. Evidence of tubercular

decay has been found in the spines of Egyptian mummies, and TB was

common both in ancient Greece and Imperial Rome. Tuberculosis reached

its peak the 18th century in Western Europe with a prevalence as high as

900 deaths per 100,000 - due to malnutrition and overcrowded housing with

poor ventilation and sanitation. Although relatively little is known about its

frequency before the 19th century, the incidence of Scrofula (consumption)

“the captain of all men of death” is thought to have peaked between the end

of the 18th century and the end of the 19th century. With advent of HIV there

has been a dramatic resurgence of tuberculosis with more than 8 million

new cases reported each year worldwide and more than 2 million deaths.

Pandemic Black Swan Event Types

Type Force Epidemiology Black Swan Event

6 Cholera Bacterial

Biological

Disease

Cholera is a severe infection in the small intestine caused by the bacterium

vibrio cholerae, contracted by drinking water or eating food contaminated

with the bacterium. Cholera symptoms include profuse watery diarrhoea and

vomiting. The primary danger posed by cholera is severe dehydration, which

can lead to rapid death. Cholera can now be treated with re-hydration and

prevented by vaccination. Cholera outbreaks in recorded history have

indeed been explosive and the global proliferation of the disease is seen by

most scholars to have occurred in six separate pandemics, with the seventh

pandemic still rampant in many developing countries around the world. The

first recorded instance of cholera was described in 1563 in an Indian medical

report. In modern times, the story of the disease begins in 1817 when it

spread from its ancient homeland of the Ganges Delta in the bay of Bengal

in North East India - to the rest of the world. The first cholera pandemic

raged from 1817-1823, the second from 1826-1837 The disease reached

Britain during October 1831 - and finally arrived in London in 1832 (13,000

deaths) with subsequent major outbreaks in 1841, 1848 (21,000 deaths)

1854 (15,000 deaths) and 1866. Surgeon John Snow – by studying the

outbreak cantered around the Broad Street well in 1854 – traced the source

of cholera to drinking water which was contaminated by infected human

faeces – ending the “miasma” or “bad air” theory of cholera transmission.

Pandemic Black Swan Event Types

Type Force Epidemiology Black Swan Event

7 Poliomyelitis Viral

Biological

Disease

The history of poliomyelitis (polio) infections extends into prehistory.

Ancient Egyptian paintings and carvings depict otherwise healthy people

with withered limbs, and children walking with canes at a young age.[3] It is

theorized that the Roman Emperor Claudius was stricken as a child, and this

caused him to walk with a limp for the rest of his life. Perhaps the earliest

recorded case of poliomyelitis is that of Sir Walter Scott. At the time, polio

was not known to medicine. In 1773 Scott was said to have developed "a

severe teething fever which deprived him of the power of his right leg." The

symptoms of poliomyelitis have been described as: Dental Paralysis,

Infantile Spinal Paralysis, Essential Paralysis of Children, Regressive

Paralysis, Myelitis of the Anterior Horns and Paralysis of the Morning.

In 1789 the first clinical description of poliomyelitis was provided by the

British physician Michael Underwood as "a debility of the lower extremities”.

Although major polio epidemics were unknown before the 20th century, the

disease has caused paralysis and death for much of human history. Over

millennia, polio survived quietly as an endemic pathogen until the 1880s

when major epidemics began to occur in Europe; soon after, widespread

epidemics appeared in the United States. By 1910, frequent epidemics

became regular events throughout the developed world, primarily in cities

during the summer months. At its peak in the 1940s and 1950s, polio would

maim, paralyse or kill over half a million people worldwide every year

Pandemic Black Swan Event Types

Type Force Epidemiology Black Swan Event

8 Typhus Bacterial

Biological

Disease

Typhoid fever (jail fever) is an acute illness associated with a high fever that

is most often caused by the Salmonella typhi bacteria. Typhoid may also be

caused by Salmonella paratyphi, a related bacterium that usually leads to a

less severe illness. The bacteria are spread via deposition in water or food

by a human carrier. An estimated 16–33 million cases of typhoid fever occur

annually. Its incidence is highest in children and young adults between 5 and

19 years old. These cases as of 2010 caused about 190,000 deaths up from

137,000 in 1990. Historically, in the pre-antibiotic era, the case fatality rate of

typhoid fever was 10-20%. Today, with prompt treatment, it is less than 1%.

9 Dysentery Bacterial /

Parasitic

Biological

Disease

Dysentery (the Flux or the bloody flux) is a form of gastroenteritis – a type

inflammatory disorder of the intestine, especially of the colon, resulting in

severe diarrhea containing blood and mucus in the feces accompanied by

fever, abdominal pain and rectal tenesmus (feeling incomplete defecation),

caused by any kind of gastric infection. Conservative estimates suggest

that 90 million cases of Bacterial Dysentery (Shigellosis) are contracted

annually, killing at least 100,000. Amoebic Dysentery (Amebiasis) infects

some 50 million people each year, with over 50,000 cases resulting in death.

Pandemic Black Swan Event Types

Type Force Epidemiology Black Swan Event

10 Spanish

Flu

Viral

Biological

Disease

In the United States, the Spanish Flu was first observed in Haskell County,

Kansas, in January 1918, prompting a local doctor, Loring Miner to warn the

U.S. Public Health Service's academic journal. On 4th March 1918, army cook

Albert Gitchell reported sick at Fort Riley, Kansas. A week later on 11th March

1918, over 100 soldiers were in hospital and the Spanish Flu virus had now

reached Queens New York. Within days, 522 men had reported sick at the

army camp. In August 1918, a more virulent strain appeared simultaneously

in Brest, Brittany-France, in Freetown, Sierra Leone, and in the U.S, in Boston,

Massachusetts. It is estimated that in 1918, between 20-40% of the worlds

population became infected by Spanish Flu - with 50 million deaths globally.

11 HIV / AIDS Viral

Biological

Disease

AIDS was first reported in America in 1981 – and provoked reactions which

echoed those associated with syphilis for so long. Many of the earliest cases

were among homosexual men - creating a climate of prejudice and moral

panic. Fear of catching this new and terrifying disease was also widespread

among the public. The observed time-lag between contracting HIV and the

onset of AIDS, coupled with new drug treatments, changed perceptions.

Increasingly it was seen as a chronic but manageable disease. The global

story was very different - by the mid-1980s it became clear that the virus had

spread, largely unnoticed, throughout the rest of the world. The nature of this

global pandemic varies from region to region, with poorer areas hit hardest. In

parts of sub-Saharan Africa nearly 1 in 10 adults carries the virus - a statistic

which is reminiscent of the spread of syphilis in parts of Europe in the 1800s.

Pandemic Black Swan Event Types

Type Force Epidemiology Black Swan Event

12 Ebola Haemorrhagic

Viral

Biological

Disease

Ebola is a highly lethal Haemorrhagic Viral Biological Disease, which has

caused at least 16 confirmed outbreaks in Africa between 1976 and 2015.

Ebola Virus Disease (EVD) is found in wild great apes and kills 50% to 90% of

humans infected - making it one of the deadliest diseases known to man. It is

so dangerous that it is considered to be a potential Grade A bioterrorism agent

– on a par with anthrax, smallpox, and bubonic plague. The current outbreak

of EVD has seen confirmed cases in Guinea, Liberia and Sierra Leone,

countries in an area of West Africa where the disease has not previously

occurred. There were also a handful of suspected cases in neighbouring Mali,

but these patients were found to have contracted other diseases

For each epidemic, transmission was quantified in different settings (illness in

the community, hospitalization, and traditional burial) and predictive analytics

simulated various epidemic scenarios to explore the impact of medical control

interventions on an emerging epidemic. A key medical parameter was the

rapid institution of control measures. For both epidemic profiles identified,

increasing the rate of hospitalization reduced the predicted epidemic size.

Over 4000 suspected cases of EVD have been recorded, with the majority of

them in Guinea. The current outbreak has currently resulted in over 2000

deaths. These figures will continue to rise as more patients die and as test

results confirm that they were infected with Ebola.

Pandemic Black Swan Event Types

Ebola is a highly lethal Haemorrhagic Viral Biological Disease, which has

caused at least 16 confirmed outbreaks in Africa between 1976 and 2015.

Pandemic Black Swan Event Types

Type Force Epidemiology Black Swan Event

13 Future

Bacterial

Pandemic

Infections

Bacterial

Biological

Disease

Bacteria were most likely the real killers in the 1918 Flu Pandemic - the vast

majority of deaths in the 1918–1919 influenza pandemic resulted as a result of

secondary bacterial pneumonia, caused by common upper respiratory-tract

bacteria. Less substantial data from the subsequent 1957 and 1968 Flu

pandemics are consistent with these findings. If severe pandemic influenza is

largely a problem of viral-bacterial co-pathogenesis, pandemic planning needs

to go beyond addressing the viral cause alone (influenza vaccines and

antiviral drugs). The diagnosis, prophylaxis, treatment and prevention of

secondary bacterial pneumonia - as well as stockpiling of antibiotics and

bacterial vaccines – should be high priorities for future pandemic planning.

14 Future

Viral

Pandemic

infections

Viral

Biological

Disease

What was Learned from Reconstructing the 1918 Spanish Flu Virus

Comparing pandemic H1N1 influenza viruses at the molecular level yields key

insights into pathogenesis – the way animal viruses mutate to cross species.

The availability of these two H1N1 virus genomes separated by over 90 years,

provided an unparalleled opportunity to study and recognise genetic properties

associated with virulent pandemic viruses - allowing for a comprehensive

assessment of emerging influenza viruses with human pandemic potential.

There are only four to six mutations required within the first three days of viral

infection in a new human host, to change an animal virus to become highly

virulent and infectious to human beings. Candidate viral gene pools for future

possible Human Pandemics include Anthrax, Lassa Fever, Rift Valley Fever,

EVD, SARS, MIRS, H1N1 Swine Flu (2009) and H7N9 Avian / Bat Flu (2013).

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