Big Data A Hype or a Chance in Managing Health Risks?

Big Data A Hype or a Chance in Managing Health Risks? Achim 11 th Geneva Association Health and Ageing Conference Emerging Health Risks and Insurance 6-7 November 2014, Madrid www.genevaassociation.org info@genevaassociation.org

Image: used under license from Shutterstock.com Big Data a hype or a chance in managing health risks? Geneva Association Emerging Health Risks & Insurance Madrid 2014 Achim

Image: used under license from Shutterstock.com Big Data a hype or a chance in managing health risks? Geneva Association Emerging Health Risks & Insurance Madrid 2014 Achim

Is There a Doctor on the Plane? Image: used under license from Shutterstock.com Source: Universal Medical Data, LLC Image: used under license from Shutterstock.com Image: Mauritius Images Image: dpa Picture Alliance / Daniel Kalker Future 2017? 3

1. Data Quelle: Verwendung unter Lizenz von Shutterstock.com

Big Data data created annually Exabytes 10000 9000 Health: e.g. Documentation, e.g. Gene sequencing, e.g. individualised Medicine e.g. Mobile Data 8000 7000 6000 5000 4000 3000 2000 2008 2009 2010 2011 2012 2013 2014 5

The Champions League among data: Health Data 70 % of clinical data in the U.S. currently electronically. Health care insurers discard 90% of the data Four problems: 1. Data are in different silos within fragmentated healthcare systems 2. Shortage of primary care clinicians in the US and D 3. Increasing prevalence of chronic diseases 4. Exploding costs!! "Governments should ensure that health data is gathered in a standard way, anonymised and made freely available to anyone that can add value to it." ehealth Task Force Report EU commission 2012 6

2. Health goes digital Photo: Shutterstock

Disruptive power # 1: smartphones Global growth in smartphones (Millions) Scope of smartphones Features 2011 2014 Alarm Clocks X X GPS X X Digital Camera (x) X Game Devices X X Radio (x) X TV 0 X Audio Players X X Electronic Wallet 0 X Geographic Location 0 X Health Apps 0 X Health Sensors 0 X Source: Mc Kinsey Global Institute 2010 8

Health Apps in one slide Categories Soft facts Hard facts Health Apps Fitness/Sport ca. 30% Lifestyle/Weight ca. 10% Wellness ca. 15% Healthcare ca. 7% Medicine ca. 17% Quantities D: > 15.000 Health Apps (Aug.. 2012 Dt. Ärzteblatt) USA: > 97.000 Health Apps (Oct. 2014 AMA American Medical Association) Growth 1.000/Month?? Reasons for growth Digitalisation Reduction of complexity Gamification & Number crunching Health as commodity Applications (Examples) Wristwatches Smartphones/i Watches Medical devices Consumer Patient Physician Disease Management Telemedicine 9

Turning smartphones into medical monitors Wello $200 Consumer Smartphone + Sensor Blood pressure, Oxygen, Heart rate, Temperature, Respiration Fever thermometer ibg Star 59,90 Patient Blood sugar in real time Share data with GP https://www.youtube.com/watch?v=3c6qdnhy1aw Quelle: dpa Picture Alliance / David Parry AliveCor $ 10,00 Physician ECG + Storage + Evaluation+ Transfer Cleared by the FDA for use by medical professionals Quelle: Verwendung unter Lizenz von Shutterstock.com MelApp $4.99 Consumer Melanom risk assessment via ABCDE Criteria (Asymmetry, Border, Color, Diameter and Evolution) FDA approval pending Quelle: MelApp risk assessment tool for skin cancer 10

Health Apps Analysis as November 2014 - Four challenges - Quality Easy to understand, to operate Standardisation regulation USA-IMS Institut 2013: 90% der Apps poor performance Fragmentation One individual many different devices Synchronisation? Compliance Inefficient utilization for persistent change to behavior USA: 2/3 applying > 1 year Evaluation Vision Up to now only descriptions Insight, outcome, conclusions? Impact on mortality and morbidity? Holistic analysis of correct data not quantities but even more quality! 11

3. Big Data Quelle: Verwendung unter Lizenz von Shutterstock.com

Data as crude oil of 21. century Velocity Seconds/Real-time Data volume Linking all data to information profiles & enriching & evaluating & interpreting Big Data Technologies : Automated decisions Correlations, Pattern Data variety External data Unstructured data Hidden data Predictions (laborious) IT-Technology: available Hardware: affordable 13

Anybody with everybody or anything with everything? Real-time access to Information Data : e.g. Internet Each other : e.g. Social networks Objects: Internet of Things Web 3.0 Quelle: Technology Review, Cambridge 14

Anybody with everybody or anything with everything? Real-time access to Information Data : e.g. Internet Each other : e.g. Social networks Objects: Internet of Things Web 3.0 Glasses Digestible Sensors Lenses Quelle: Google/Handou/Corbis Quelle: dpa Picture Alliance / Jerome Fouquet Quelle: http://www.ac24.cz/ 15

Wireless biometric data* = Wearables (e.g. Fitness trackers) EEG Vision Hearing Sleep cycles Personal health behaviour Doctors' basis for diagnosis/therapy Image: PantherMedia / Detlef Krieger Electrocardiography Heart rate Pacemaker Emergency action Food intake Blood pressure Blood glucose, lipids Higher pricing granularity Hip implant Social activity Urine Product designing? Walking distances Individualized offerings? * > 300.000 Health Care Apps in Apple's Store (August 2012 Gartner). 16

Next disruption # 2? Apple (Health Kit) Goggle (Goggle Fit) Samsung (S Health) http://www.forbes.com/sites/quora/2014/06/09/wha t-do-doctors-think-of-healthkit/ Single platform for all health data (central hub) Health profile Exchange between health apps much easier i-tunes (music)? Quelle: Apple health Kit 17

Big Data in Medicine Marriage medical innovation with the Internet Health Apps + Electronic medical records + Genome data + Public databases Real time analysis ( Big data ) Transfer ( Cloud ) Translation: short reports to clinicians Impact on health care costs? 18

4. Relevance to Business Quelle: Verwendung unter Lizenz von Shutterstock.com

Risks: Information asymmetry as to health data Data protection Legislative environment in flux?! Insurance Empowered Budget cuts Individ. medicine Discrimination Consumer Regulation of medical devices Industry Safety of diagnosis? Liability? Induced demand 20

Opportunities: Turning (Big) Data into Information - Possible areas of application - Volume: growing quantity Varitey: internal and external, structured and unstructured data Velocity: quickening speed until almost real-time Value:??? 1 Risk management centric 2 Customer centric Underwriting Claims Product development Pricing Reserving Competitors analysis Trends detection Processes optimization 3 Big Data Finance centric Investmentportfolio optimisation Asset/Liability matching Consumer with insurance affinity Segmentation Campaign analysis Cross/Up-selling Purchase behaviour 21

Opportunites: Proactive risk management? Medicalisation of everyday life Quantified self Need for orientation Contributions to healthcare costs (prevention) http://www.forbes.com/sites/quora/2014/06/09/wha t-do-doctors-think-of-healthkit/ Risk protection (sanctioning) Risk minimisation (caretaker) Support Transparency Image Quelle: Apple health Kit 22

IT may change the insurance business model but who will offer the new business models? Disruptive force of technology: e.g. hard- and software industry Mobile, social and Internet-centric Analogy for the insurance industry? Focus: Cost savings, process efficiency, profitability Digitization, Internet, social networks, digital natives Regional / Niche Insurers Retailers Big Pharma Social Media Companies 23

Conclusions 1 In one sentence Future-oriented booming market Many problems remain unsolved 2 Insurance (Health) 3 Acceleration by Big Data Emerging health risks? Only experimental application by now Impact on Underwriting and Pricing likely in the medium-term i-tunisation of health data Internet of Things 24