Smart Data and Clinical decision support: Strategic ... · Smart Data and Clinical decision support: Strategic aspects of a big healthcare provider Werner Leodolter, KAGES, University

Smart Data and Clinical decision support:

Strategic aspects of a big healthcare provider

Werner Leodolter, KAGES, University of Graz

Agenda

• The provider and its staff …. as user of CDS • ICT infrastructure and operational data as foundation ……. for smart data • The decision making process …… human, machine, hybrid • CDS Clinical decision making ……. Principles, Trust as foundation • Examples

• Process mining …….. RPA • Quickview • Diagnosis finder • prediction

• Future healthcare - opportunities and benefits • Staff • Patient – engagement, prevention

• A model of thinking

27.05.2019 © Werner Leodolter 2

Agenda

• The provider and its staff …. as user of CDS • ICT infrastructure and operational data as foundation ……. for smart data • The decision making process …… human, machine, hybrid • Clinical decision making ……. Principles, Trust as foundation • Examples



• A model of thinking 27.05.2019 © Werner Leodolter 3

Werner Leodolter 4 5.10.18

Steiermärk ische Krankenansta ltengesellschaft m.b.H. esKAG

KAGes and its role in patient-centered healthcare provision in the province

of styria KAGes on its journey through the digital

transformation



Company presentation, last update: 01.01.2018

KAGes – regions, associations, regional hospitals/nursing centres, locations 12 hospitals (incl. LKH-Univ.Klinikum Graz) at 22 locations and 4 regional nursing centres (last amended January 2018)



Short Company Profile:

• Full scope regional healthcare/hospital provider with 90% market share in hospital beds for population of app. 1,2 million

• Including university hospital in close cooperation with medical university of Graz (MUG)

• Additionally 4 nursing homes (market share >10%) and beginning “healthcare centers” (ambulatory care centers)

Basic data

www.kages.at

− 22 hospital locations (incl. Univ. hospital Graz) − 4 nursing homes with app. 350 residents − app. 5.700 hospital beds − app. 260.000 inpatients/yr − app. 1 mio outpatient visits/yr − app. 1.750.000 hospital days /yr − app. 5,7 days – average length of stay

− app.17.500 employees − App. 1,6 billion operating costs, 150 mio investments

Steiermärkische Krankenanstaltengesellschaft m.b.H. (KAGes)





Some key challenges………….

• Integrated care for chronically ill patients • Patient engagement in an active role (condition, process,

feedback, patient reported outcome) • Innovative use of available data to…….

• Reduce administrative workload • Better decisions with decision support and prediction • Knowledge discovery – clinical research



Provision of care and ICT – quo vadis? • Specialization and local basic care can be recombined by ICT (teleconsultation,

telemedicine) • High organizational requirements for HC-prociders and their ability to cooperate for

networked patient-centered care with patient involvement and disease management (email, telephone, web, AAL, home devices, self-service, etc.) Laboratory diagnostics will change (POCT, Lab on a chip, home devices, wearables)

• Medication safety and medication adherence can be increased • Innovative data use (Big/Smart Data) for clinical decision support (CDS), knowledge

acquisition, personalized medicine, discharge management, training, simulation, etc. requires a central approach

• improvement of hospital information systems in functionality, visualization and with numerous mobile terminals of various forms

• Integration of bioinformatics, medical device technology and information management

• Higher investment levels and higher ICT expenses are to be expected

Agenda








FI/CO

HR PACS

SAP

Cerner SER

CompuGroup

Geb- Reg

PAS V3 Subsy

IS-H LIS

Integration Concept:

Subsy PDMS

Digital Archive

i.s.h.med

openMEDOCS

ComServ

Σ Subsystems

Siemens

Overview IT-Landscape

MM



Special-IT-Systems "Subsystems" ("Best-of-Breed-Solutions")

ONE leading and patient-centered Hospital-Information-System for all 26 hospital locations and nursing homes (IS-H und i.s.h.med)"

Pat.-Administration, Wards, Outpatient-Clinics, OR, Radiology, Nursing Process, Medical Documentation, Scheduling, EDI Med.

Reports, EDI Insurance Comp., Billing&Acounting, ...

ICU

Laboratory

Anesthesia Pat.Logistics Blood bank … etc.…

Pathology Endoscopy Obstetrics Dialysis

Main System Architecture



Experience with eHealth and eHealth supported collaboration in Healthcare

• Virtual EBA („emergency room“) • Communication of discharge letters etc. • Medical Portal for Health Professionals (Collaboration) • Medical Archive Styria (long-term archiving and image exchange for

radiologists – KAGes daughter company marc) • Health Portal Styria (Information) • Patient portal (integration of patients) • Telemonitoring for various diseases and care settings • ELGA Electronic health record Austria – styrian domain



www.medizin-portal.kages.at

For Health-Professionals

www.patienten-portal.kages.at

For Patients

Web-Portals to optimize processes

http://www.medizin-portal.kages.at/

http://www.medizin-portal.kages.at/



now in rollout or pilot phase:

• Telemonitoring of chronically ill patients in Cooperation with general practitioners in a pilot-region • Chronic heart failure – heart insufficiency (rollout) • Diabetes • Hypertension • To come: COPD

Agenda






The cognitive process – how do we take decisions?

• the individual person • in the organization • in the process chain of my

business model

perceive

Recognize, evaluate

decide

act

© Werner Leodolter 18

learning

The cognitive process hybrid – analog+digital

IoT, VR, AR, Drones, speech analysis, affective computing, chatbots, virtual assistents etc


perceive

Recognize, evaluate

decide

act

image and pattern recognition, Speech analysis, affective computing, Decision support Systems chatbots, virtual assistents etc

20

perceive

Recognize, evaluate

decide

act


© Werner Leodolter

Methods: Rule based (Expert Systems e.g. with fuzzy Logic) Supervised and unsupervised machine learning, etc.

21

perceive

Recognize, evaluate

decide

act


© Werner Leodolter

• In the organization • In the business

process automated

22

perceive

Recognize, evaluate

decide

act

The cognitive process automated

© Werner Leodolter

Machine learning

DATA, Algorithms,

The basis:

23


© Werner Leodolter

perceive

Recognize, evaluate

decide

act

The psychology of decision making (of humans and organization(al units))

• Analogy and Intuition (Hofstaedter, Gigerenzer) • Thinking fast and slow (Kahneman) - System 1 und 2 • Instinctive linking of experience and perception/sensations and

the formation of mental models for the future (Gilbert)

• The pitfalls of psychology are also valid for organisation(al units) - bias, priming, self-deception etc.

• Those pitfalls even accumulate in organizations with more persons with the same biases etc. (due to culture, structure, same information sources, following the leaders - „leadership“ etc.)

June 2016 © Werner Leodolter 24

Why do we need trust?

Uncertainty Unknown New

Certainty Known Old

Risk

benefit

curiosity

© Werner Leodolter

Why should we talk about Trust?

• Trust has a major impact on decisions • Decisions concerning health are complex and they are changing

• enormous complexity of the human organism and its psyche, • incredible amounts of available knowledge • personal preferences of patients and the medical and nursing staff. • possibilities of personalized medicine

• Decisions taken by algorithms? Trust – but whom? - and what?

Trust and decision Trust generally helps us to manage complexity.

Trust - and ultimately faith - helps us to make the decisions - be it as a doctor, pharmacist, caregiver or be it as a patient

© Werner Leodolter

Trust is a manifold concept – a „cloud of words“ Trust is something very personal – Trust grows and develops

Self-confidence – Trust in yourself

Values

Trust in god

Trust your family Trust your partners

Trust your friend Trust your (digital) assistant

Trust your organization Trust a brand

Trust the airline

Trust technology

Trust in …..???? etc.

Trust your teacher

Trust facebook?

Trust in experts

Trust your boss

Trust in data and information

Laws and regulations

certificates

Rankings

Trust in media

Trust in political processes Trust in relationships

Trust your peers

Trust and truth

Trust in algorithms

Personal Trust Institutional trust Technology trust

Trust in institutions

© Werner Leodolter

Fundamental to Trust: What is reality? – what is truth?

• Deep Fakes: audio-visual imitations of people, generated by increasingly powerful neural networks, that will soon be indistinguishable from the real thing.

• What would that do to politics? • Democracy, and our ability to counteract threats, is already

threatened by a lack of agreement on the facts. • Once you can’t trust the evidence of your senses anymore, we’re in

serious trouble. • Algorithms can be fooled in ways we didn’t anticipate.

following Thomas Hornigold SU Mar 06, 2018

https://singularityhub.com/author/thornigold/


….the infrastructure beneath…….. changes – is it an infrastructure of trust?

• Everything seems to become…… • Digital – digital twins are only proxies! • Interconnected – reasonable decoupling! • Automated – can/should technologies „trust each other? • Scalable - ..becoming „too big to fail“?

• ……and monopolized ……. Trust in…..

• Facebook? • Google? • Amazon? • Etc.

© Werner Leodolter

Novel threats and risks from increasing dependence on AI

• E.g. autonomous driving: Imagine that a hacker fools a computer into thinking that a stop sign isn’t there, or that the back of someone’s car is really a nice open stretch of road……….

• These algorithms may be smart in some ways, but they are lacking in common sense; they can be fooled.

Common sense has to stay in control

following Thomas Hornigold SU Mar 06, 2018


Trust in decision support systems? e.g: criteria for doctors

• Provable? Evidence based? (scientific) knowledge base? • Understandable? • Responsibility and liability – who?

• What if doctor has liability, but the doctor has no idea how the decision proposal has been derived e.g. a prediction?

• Is his expectation (from his experience) met? • Is the depicted rationale for the proposal compatible to his clinical reasoning?

• If yes Trust and adoption

© Werner Leodolter

DATA, Algorithms,

The basis:

32


© Werner Leodolter

perceive

Recognize, evaluate

decide

act

Data, Algorithms, Learning

Embedded in a „cloud of trust“

33

Trust emerges partly from our subconscious mind

© Werner Leodolter

perceive

Recognize, evaluate

decide

act


…network man and machine intelligently

…Hybrid Intelligence

Hybrid Intelligencies • “The future belongs to human and computer

collaboration. Human creativity and increasingly intelligent machines come together. We will use AI more and more as a support for our own thinking and decision-making. We need to consider what kind of cognitive functions we can outsource to machines. How will we organise this co-operation to make business more efficient and create a social environment which is more productive?”

Garry Kasparow


Agenda






Waste in healthcare following an analysis by Intermountain Healthcare, Chris Wood, MD

20% inappropriate care driven by false incentives 10% avoidable care 2% complications 23% variation in care delivered – no standards of

behaviour - stylistic difference of every physician 2% excess profits of insurance industry 2%+ unnecessary overhead 8% operational ineffectiveness

32% makes sense - the rest is waste

Dimensions of progress in Medicine

prediction in medicine – can we trust? • doctors routinely overestimate patient life expectancy by a factor

of 3, and deliver care of widely varied intensity in the last 6 months of life

• predictive algorithms cannot eliminate medical uncertainty, but • they already improve allocation of scarce health care resources, helping to avert hospitalization for patients • fairly prioritizing patients e.g. for liver transplantation by means of MELD scores. • Early-warning systems that once would have taken years to create can now be rapidly developed and optimized

from realworld data, • deep-learning neural networks routinely yield state-of-the-art image-recognition capabilities previously thought to

be impossible.

• Combining machine-learning software with the best human clinician “hardware” - Hybrid Intelligence - will permit delivery of care that outperforms what either can do alone.

Following: Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations Jonathan H. Chen, M.D., Ph.D., and Steven M. Asch, M.D., M.P.H. NEJM 376;26 nejm.org June 29, 2017

Trust in prediction from ML and Big Data? • practice of medicine is constantly evolving in response to new technology,

epidemiology, and social phenomena • we will always be chasing a moving target.

• the future will not necessarily resemble the past, • simply accumulating mass data over time has diminishing returns. • the relevance of clinical data decays differently in different medical disciplines

• e.g. clinical data alone have relatively limited predictive power for hospital readmissions

• they may have more to do with social determinants of health.

• The last mile of clinical implementation is the far more critical task of prediction

Following: Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations Jonathan H. Chen, M.D., Ph.D., and Steven M. Asch, M.D., M.P.H. NEJM 376;26 nejm.org June 29, 2017

Build trust in data! Consider…………..

• What are Data used for? – for prediction?, decision proposal or (automated) decision?, (automated) action?

• What are your raw data? • provenance? source?, bias?, quality (accuracy)?, plausibility?, reliability?, timeliness?, security?, data

privacy compliant?, compliant to established standards (e.g. for interoperability)?, blockchain-mechanisms involved?

• Apply data cleansing, data aggregation, data imputation, data augmentation carefully • Text analytics (NLP), Image recognition generating additional relevant data, features and context • relevance, simplicity, standardisation, harmonisation, credibility, completeness, consistency, volume

• Be careful with data interpretation! - what are the data for? what is their context? • Data enrichment – add new observations a/o sources to data sets

© Werner Leodolter

Clinical decision support (CDS) • CDS is a health information technology component that

• provides clinicians, staff, patients or other individuals with knowledge and person-specific information,

• intelligently filtered or presented at appropriate times, • to enhance health and health care.

• CDS encompasses a variety of tools to enhance decision making in the clinical workflow. (HealthIT.gov, 2014) These tools

• include computerized alerts and • reminders to care providers and patients; • clinical guidelines; • condition-specific order sets; • focused patient data reports and summaries; • Documentation templates; • diagnostic support; and • contextually relevant reference information, among other tools

Source: Improving Diagnosis in Health Care Erin P. Balogh, Bryan T. Miller, and John R. Ball, Editors; Committee on Diagnostic Error in Health Care; Board on Health Care Services;Institute of Medicine; The National Academies of Sciences,Engineering, and Medicine

CDS – Clinical Decision Support basic considerations

Transparency of data and algorithms (rough understanding of logic behind and – in best case - the decisive parameters)

plus

Medical competence plus

enough time available to reason about the decision proposals preserves

ability for medical judgement and certain independence from support systems – this enables continous improvement

2016 Werner Leodolter / Diether Kramer 43

following Prof. Adlassnig

Agenda






Experimental prototypes for innovative use of data at styrian hospital group KAGes (Austria)

• Basis: KAGes is rich in Data und - thus – rich in Information and knowledge (90% market share in hospital beds for 1.2 million inhabitants)

• For the first preliminary models we used – with much room for further development and sophistication:

1. …all patients with a stationary admission between 2006 and 2015 at KAGes app. 2 mio longitudinal patient records

2. …all coded diagnoses are used (with every ICD 10 Code once per patient)

3. ….age and gender of patients …..there is potential for much more……..



Quelle: Werner Eberhardt, SAP




• The statistical model incorporates more than 300 variables (and more to come….)

• A view of the „heatmap“ gives • an impression of complexity and • promises many interesting hypotheses

to be evaluated


Preparing statistic modeling and knowledge detection

Process Management and Mining enable realtime perception, deep analysis and self-regulation

• Classical BPM: modeling and documenting ideal processes • BPA (automation): leveraging information - operating processes • Today´s requirements:

• Flexible and immediate change of processes to improve the value chain • Visualize life process • Analyze and detect trends, bottlenecks, compliance problems and fraud as well as

inefficiency immediately • DTO - Digital twins of organizations (Will van der Alst) – IoT and AI enabling

• The organisation´s memory • Plan, simulate, measure and control (RPA – robotic process automation) • Predictive and prescriptive analytics • On the long run: supervised self-regualtion


Process mining concerning Colon Cancer schematic view:



Oncolyzer – in dialogue with processes and data

2016 Diether Kramer, Werner Leodolter 50

Survival curves sho the clinical outcome of different clinical processes


* Für diese Stichprobe!

Knowledge from data: Diagnose Finder find patients possible co-diagnoses


Contextsensitive view on extensive EMRs

Qui

ckVi

ew


for example – now in operation:

Prevention of Delir – predictive analysis

•Warning for patients who are likely to encounter delir, so that preventive measures such as close observations can be taken in order to prevent delir (40 % are preventable)

•Parameters derived from available parameters •Part of the general innovative approach – predicting readmission in order to prevent it



Random Forest

1 - Specificity

Sen

sitiv

ity

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

Area under the curve: 0.9307

Accuracy : 0.8888 Sensitivity : 0.6233 Specificity : 0.9876

Pred

ictio

n 1

Referenz Kein Delir Delir

Prog

nose

Kein Delir 796 113

Delir 10 187

Preparing statistic modeling and knowledge detection – example Delir


Steiermärk ische Krankenansta ltengesellschaft m.b.H. esKAGPredicting Health – Predicting Delir

56 5.10.18

To summarize: Our ICT topics & challenges for the next years: • Stay efficient (curr.) 2,55 % ICT expenditure of total costs) and safe • Innovate electronic charting and medication (Process innovation)

and support medical staff with intuitive, context sensitive UI • Integrate the patient, enable him to understand his “EPR” (context-

related) – patient engagement • Integration of all relevant data and Innovative data use (analytics,

predictive, NLP) • Enable clinical decision support - context sensitive



Agenda






Digital-medical Convergence generating digital twins and models

• Omics, Bioinformatics • Imaging • Medical device integration • New (partly implantable) biosensors • Induce ordinary skin or blood cells to become pluripotent stem cells – coaxed to grow any tissue

of interest to test which drugs might be effective to prevent a genetically predisposed disease • Augmented EMR, EHR and EPR

• ……bringing together clinical informatics and bioinformatics allowing new biomarkers • …..enabling closed loop sensing and dosing models (e.g. diabetes) • ……enabling less false positive results with therapeutic consequence

Improving diagnostics

Enabling precision medicine (via e.g. medication, therapy, transplant rejection, prevention)


Prediction enables precision medicine and personalized care processes

• Prediction changes clinical pathways • Prediction changes diagnostic processes

• e.g. breast cancer risk assessments – prevent biopsies • Prediction changes therapy

• e.g. medication - possibly with pharmacogenetics

• Prediction changes care processes • e.g. post hospital care processes

• Rehab • Home care • Telemonitoring • „eMail-visits“ for follow-up checks

• Care processes without hospital involvement • Prediction changes behaviour of patients – preventive ?

© Werner Leodolter

Patient Engagement

PRO, Telemon

„myELGA“ (myEHR)

Quality -

what is the outcome?

© Werner Leodolter 27.05.2019 61

The challenge: augment „patient quality experience“

• Scheduling for diagnostic and therapeutic pathways (cross-provider) • Telemonitoring offers better feeling of security for the patient • patient outcome reporting • Proactive information und patient engagement – to foster prevention

and change behaviour

© Werner Leodolter 27.05.2019 62

Continously develop the trust of your staff as part of the subconscious mind of your organization

• Support them with • assistive systems like CDS that provide and support clinical reasoning (as context-

sensitive as possible) • Training and simulations • Team experiences and peer support (like tumor board, tele-consultation etc.) • Enough case load to build up professional experience – „practice makes perfect“ • Future: good collaboration with AI

• Provide them good tools and a comprehensive infrastructure • Provide them good orientation, guidance and leadership • Help them build their self-confidence (Case-load, Training, Simulators)


Agenda






The subconscious mind of your organization – cascaded hybrid intelligences


Shape the subconscious mind

deliberately

Let emerge Hybrid Intelligencies

in a targeted way

Agencies other HC providers

Assistents, Chatbots Patients

The subconscious mind of organizations …….a socio-technical construct, consisting of

• technical infrastructure (which allow and support subconscious and conscious action for the organization) and

• structures and processes of an organization (formal and informal rules) and • values, attitudes and strategies

Werner Leodolter Springer Nature: Digital Transformation Shaping the Subconscious Minds of Organizations - Innovative Organizations and Hybrid Intelligences

Shape the subconscious mind of your organization – embedded in a cloud of trusted relationships


Shape the subconscious mind

deliberately

Mind the trust and the changing roles of the people involved –

as trusters and trustees

Social Media other HC providers

Assistents, Chatbots Patients

As patients, employees or partners we tend to trust…….. • ……our own experience

• …..humans, where we have or can develop a relationship • ……technology (devices, cars, airplanes, ICT systems, chatbots, AI etc.) • ……an organization and its organizational units, an institution and an

institutional system…… • that exploits technology…….with trained humans and common sense in the „drivers

seat“, …… with well-shaped„hybrid intelligences“ • that regularly evaluates its decision making processes, its business processes and its

quality • that supports patient engagement and provides an excellent patient quality experience • that is trustworthy and provides us with a „good feeling“

Let us shape our organizations well


„Hybrid Intelligencies“ shaping Organisations The more you automate decision making – the more you have to consider

the subconscious mind of the organization

We shape our tools and then our tools shape us. Marshall McLuhan: Understanding new media

Illustration aus DIE ZEIT 13.2.2014

© Werner Leodolter

Build Trust and Confidence by

shaping the Subconscious Mind of your Organization

It is easy to use this model of thinking – this metaphor… …………just think of yourself Do it deliberately – don´t let it just happen Thus enable your organization for more value-based care

© Werner Leodolter

https://youtu.be/wB9hRIm75ow contact: [email protected] Redneragentur Speaker´s agency: Topspeaker german book: Springer-Verlag

Since July 2017:

http://www.springer.com/in/book/978331953617

…..further reading, links and video

http://cbmed.org

www.kages.at

Improving Diagnosis in Healthcare Erin P. Balogh, Bryan T. Miller, and John R. Ball, Editors; Committee on Diagnostic Error in Health Care; Board on Health Care Services; Institute of Medicine; The National Academies of Sciences,Engineering, and Medicine

http://ebooks.iospress.nl/volumearticle/46457 Development and Validation of a Multivariable Prediction Model for the Occurrence of Delirium in Hospitalized Gerontopsychiatry and Internal Medicine Patients Diether Kramer, Sai Veeranki, Dieter Hayn, Franz Quehenberger, Werner Leodolter, Christian Jagsch, Günter Schreier

https://youtu.be/wB9hRIm75ow

mailto:[email protected]

http://www.topspeaker.at/liste.php?r=133

http://www.springer.com/de/book/9783662444580

http://www.springer.com/in/book/9783319536170

http://cbmed.org/

http://www.kages.at/

http://ebooks.iospress.nl/volumearticle/46457

Documents

Smart Data and Clinical decision support: Strategic ... · Smart Data and Clinical decision support: Strategic aspects of a big healthcare provider Werner Leodolter, KAGES, University