1. BUILDING THE FUTURE OF PERSONALIZED
HEALTHCARE
IN A DATA DRIVEN SOCIETY
University of Twente
University Medical Center Groningen
The Netherlands
Building the future of Health
June, 3-2016; Groningen
Lisette van Gemert-Pijnen

2. The University of Twente is noted for:
• Excellent education & research
• New technology as a catalyst for change, innovation and progress
• Combination of technology & social sciences
• Entrepreneurial attitude
Themes: ICT, Nano-, Bio-, Geo-Engineering, Management, Behavioral Science
26/08/2016
OUR PROFILE: HIGH TECH HUMAN TOUCH

3. the
technology
sciences
the
social
sciences

4. • What are the paradigm shifts in Healthcare?
• What are the challenges in a data driven society?
• What are we doing? In the domain of safety
• Food for Thoughts
THIS TALK
BUILDING THE FUTURE OF PERSONALIZED HEALTHCARE
www.cewr.nl (persuasive technology lab)

5. 1. People-centered versus disease-centered
• Health as the ability to adapt and to self-manage, (Huber, 2011)
• Services are focused on individual needs and preferences
• No one size fits all
2. Medicine digitized, unplugged, democratized
• Bottom up
• From hospitals to self-organizing communities (resilience)
PERSONALIZED HEALTHCARE
PARADIGM SHIFTS

6. BOTTOM UP MEDICINE
ENABLED VIA TECHNOLOGIES

7. Amount of data is growing explosively
3. Breaking the wall of knowledge
4. Health Industry blurs medicine
DATA DRIVEN SOCIETY
PARADIGM SHIFTS

8. 5. Pervasive tech: breaking wall of connectivity
• A fusion of Technologies (mobile health environments; IoT)
• Cloud based Healthcare Information Technology
6. New Science: Tech & Health & Behaviors
• Data Stewardship: Safety, Security (e.g. Personal Health Train)
• Data Analytics: Algorithms to understand behaviors
• Data Wisdom: to add value to health &wellbeing
DATA DRIVEN HEALTHCARE
PARADIGM SHIFTS

9. The datification of our world gives us
boundless data in terms of Volume,
Velocity, Variety and Veracitiy
Advanced analytics allows us to
leverage all types of data to gain
insights and add Value
Marr 2015
CHALLENGE: BIG DATA; NOT ALL DATA IS BIG

10. BIG DATA-BIG INSIGHTS?
DATA WISDOM

11. WHAT ARE THE CHALLENGES?
BUILDING THE FUTURE OF HEALTH
Crossing boundaries
Crossing borders

12. STRUCTURAL HYGIENE
CROSSING BOUNDARIES: HUMAN & MEDICINE & TECH

13. 13
CROSSING BOUNDARIES: CEHRES ROADMAP (VAN GEMERT, 2011)
Why IT? How does IT work? Does IT help? What is ITs impact?
needs & values usability and persuasiveness is anyone getting better? implementation
facilitators & barriers compliance to IT physical & mental & social maintenance
Needs assessments Persuasive Designs Health Behaviour Theories Business models

14. Crossing borders, tech to develop digital surveillance systems to improve health &wellbeing
Crossing boundaries, tech as a method to better measure, aggregate and make sense of behavioral,
clinical and environmental data
STRUCTURAL HYGIENE
BIG DATA TO SUPPORT HEALTH & WELLBEING

15. Safety and Superbugs; a wicked problem
 Lack of cooperation across countries, across continents
 Lack of regulations, guidelines, laws
 Profit-driven instead of needs-driven supply (farming)
 Knowledge gap; Inadequate education, information
 Inadequate surveillance, Insufficient diagnosis
 Inadequate infection control practices, lack of resources,
compliance
 Lack of research, development AB slow and no priority
15

16. SAFETY & SUPERBUGS: BAD HYGIENE
Acinetobacter Baumanni
Mobile IC Enschede

17. Early warning systems: eSurveillance of HCWs

18. COMPLIANCE: DEMAND FOR TAILORED INSTRUCTIONS
BOTTOM UP DEVELOPMENT ANTIBIOTIC STEWARDSHIP INFORMATION SYSTEMS

19.  Highly Resistant Micro Organism, e.g. MRSA; Zoonotics
(Animal>humans)
 Digital surveillance to track, trace infections and to develop an
EWS and predictive model to detect and prevent outbreaks
19
BIG DATA IN INFECTION PREVENTION
Demand for EWS & predictive modelling

20. • Integrating geospatial data with epidemiological and clinical data
• to develop a smart Early Warning System
• Path of movements (sensors data analytics inside/outside hospital)
• Pathogens and HRMO are monitored real-time (over 5 years)
• Predictive modelling; new computational methods for analysing geospatial and
laboratory data
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EARLY WARNING SYSTEM
CROSSING BORDERS & BOUNDARIES

21. Development of Predictive model to analyse geospatial, laboratory,
epidemiological and clinical data
1. Exploratory spatial analysis of historical data to investigate emerging
patterns
2. Regression model will be developed to estimate the posterior distributions
of outbreaks (integrating clinical, epidemiological & geospatial data)
3. Predictive modelling will be used at the level of (sub)units in a hospital
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PROACTIVE DECISION MAKING
COOPERATION HEALTH-BUSINESS-SCIENCE

22. User centred methods to design a data dashboard
- To tailor the predictive decision model to end users (A-Team
members)
- CeHRes- roadmap for eHealth design will be used
- Persuasive design and usability principles to optimize the system
and comprehensible visualization of data
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PERSUASIVE DESIGNS
COOPERATION WITH HCWS

23. Shared Decision making support during outbreaks
Dealing with dilemmas (infections impact society)
Dealing with pressure, emotions, unclear regulations etc.
Dealing with stakeholders
Target users:
professionals from human, veterinary & public health domains dealing with infection prevention &
control (zoonotic case)
Agile development (Avian Influenza & MRSA) with stakeholders, professionals
Coop with T-Xchange (Science-Business-Health)
DILEMMA GAMES TO MANAGE OUTBREAKS
EDUCATION PROFESSIONALS MED-VET

24. 24
E-LEARNING ASP
GAME BASED TRAINING ENVIRONMENT MED-VET (GAMES FOR HEALTH)
How to design and develop performance assessment methods and
techniques, such that the player is unaware of it? Including feedback and
dynamics regarding risk communication and game models.
How to evaluate the serious games and its effectiveness in the virtual and real
world?

25. Implementation: Stakeholder Business model
Maturity Scale ASP; implementation advice
ASP: no one size fits all
Tailoring interventions, to local resources, guidelines
Support for persuasive Auditing
Benchmarking hospitals

26. 26
Future is Promising!
BIG Data BIG Health

27. Big Data Safe Health: STRUCTURAL STEWARDSHIP
food for thoughts
 Create Data Wisdom
 those who generate data are not the ones that have the
knowledge to analyse, those who analyse lack domain insight
 Big data a supplement no substitute of traditional data collection
 Ethical considerations (over-under prediction; data management)
 Accountability; Who has to prove what and how it is regulated?
 Interpretation; Data versus a Clinical eye
26-8-2016 27

28.  Search for patterns rather than testing hypotheses
 Critical volume, variety, veracity of data
 Beyond RCTs; Life Logging
 Power of Analytics (Machine learning)
 Bottom up evidence (reverse epidemiology)
FOOD FOR THOUGHTS
CRITICAL EVIDENCE

29. SAFETY: CREATIVE BUILDING, NO ROOM FOR SAFE CHILDBIRTH
COCREATION WITH HCWS A MUST!
Simon et al, nederlands tijdschrift voor anesthesiologie, april 2016
Delivery Room
Operating room
Prone to complications:
300 meters distance and 2 floors up!

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MOOC eHealth
https://www.futurelearn.com/courses/ehealth
j.vangemert-pijnen@utwente.nl

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Super surveillance…Smart Blood to track 24h, everywhere

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Big data hubris
missing swine flu (2009),
the peak of flu season (2013)
VALUE: Need for Prediction models

35. SMART & SAFE HOMES
VALUE: DIGITAL EMPOWERED CITIZENS

36. Patient
Mobility &
Infections
eSurveillance
for just in time
Interventions

37. Update with Geospatial data & behavioral data

38. COMPUTERS WITH ATTITUDE NOT ON THE HORIZON
It Was A Bad Idea For Watson The Supercomputer To Learn The Urban
Dictionary…
Value: Sense making Communication (NLP, contextualized)

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www.healthbytech.com www.cewr.nl (persuasive technology lab)

40. BIG DATA
WEARABLES @WORK; @ HOME
 Data analytics: Does IT work? Help?
 Engagement: persuasive feedback
 Awareness of risks
 Prevention of complications
40
Twente-Thales ImEdisense
Telemonitoring in stAble Chronic Heart
Failure
(Twente TEACH)

41. BIG DATA IN PSYCHIATRY
JUST IN TIME COACHING
Data analytics * Design Persuasive Coaching strategies (using virtual reality)
…to better measure, aggregate and make sense of behavioral, psychosocial,
biometric and geodata to develop personalized coaching programs,
….to make predictions about how a given individual will proceed

43. PREDICTIVE MODELING
RISK FACTORS USING DATA FROM SMART-COPD INHALERS

44. EBOLA SPREAD
eSURVEILLANCE SYSTEMS

45. 45
http://www.cgdev.org/section/initiatives/_active/drugresistanceglobalhealth/drug_resistance_by_disease
• Urbanization
• Pollution
• Demographic Change
• Migration
• Lifestyle patterns
• Food & Agriculture
• Trade (low prizes, easy access)
• Inappropriate and irrational use of
medicines (hospitals, community,
agriculture)
Digital Surveillance to monitor infections/AMR
AMR, A wicked problem