Big Data in Healthcare Zürich,

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Randell Harris
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1 Big Data in Healthcare Zürich, Fraunhofer Institut für intelligente

2 Fraunhofer Institute for intelligent Analysis and Information Systems Fraunhofer IAIS: Do more with data! 200 Employees - Researchers, Project Engineers, etc. Applied solutions from the areas of Machine Learning, Visual Analytics, Business Intelligence, Risk Analysis, Multimedia Pattern Recognition, Process Intelligence Coordinator of the Fraunhofer Big Data Alliance Business Field Big Data Analytics Strategy and Consulting Research and Development Application in Companies Training Program Data Scientist Joint research groups and cooperations with

3 Big Data and Healthcare Chances and Challenges Medicine is a clear driver of big data but healthcare industry is lagging behind Challenges: (McKinsey, 2011)*, (Fraunhofer, 2012)** 1. shortage of the analytical and managerial talent 2. need to ensure that the right infrastructure is in place 3. safeguards are in place to address public concerns about big data * * **

4 Classical Defintion of Big Data: the three V s [Gartner/MarkLogic, 2011] Volume Variety Velocity

5 Big Data Drivers: Three Trends Electronic Processes Transparent and meaningful decisions Consumers as producers Social media Digitalisation of everyday life Privacy by design 5

6 What is Data Analytics? Data Analytics is: Data Analytics is not:

7 The Privacy Challenge Legal and technical solutions Data protection is key building block for a big data strategy in healthcare Solutions: Informed Consent and Contracts: Principle of de-facto anonymisation* Privacy by Design: Integration of Privacy-Constraints in architecture. E.g.: only export number of steps rounded to 1.000, then analyse Privacy-preserving Data Mining: Integration of Privacy-Constraints in analysis E.g.: analyse complete data, find best predictive pattern that fulfills privacy criterion * Forgó, Kollek, Arning, Krügel, Petersen: Ethical and Legal Requirements for Transnational Genetic Research, München: Beck 2010

8 The p-medicine Architecture Workflow for Clincal Decision Support Trial Hospital p-medicine Portal Test Hospital Pseudonymization Ontology Annotation CDS Model Repository Hospital Database Data Warehouse Hospital Database Data Mining CDS Tool Security

9 Big Data and Medical Devices Three theses: 1. A Big Data approach can generate significant value Integration with additional data sets increases practical impact 2. Technical aspects (data size, volume, velocity, quality) are manageable 3. Legal apects are to be addressed Privacy law Medical device law!

10 Developing a Big Data Strategy Step 1: Creative Process Creative Workshop Excite- Seminar McKinsey, 2013: Although the health-care industry has lagged behind ( ) in the use of big data ( ) it could soon catch up. First movers ( ) are already achieving positive results, which is prompting other stakeholders to take action, lest they be left behind. Data Map Process Analysis Proof of Concept Step 2: Analysis Step 3: Evaluation Data protection and Privacy Business Cases Big Data Architecture Big Data Analytics Training and Quali-fikation Step 4: Implementation

11 Contact Fraunhofer IAIS Schloss Birlinghoven D St. Augustin

Legal perspectives in EU projects. Prof. Dr. Nikolaus Forgó Leibniz University Hannover nikolaus.forgo@iri.unihannover.de

Legal perspectives in EU projects Prof. Dr. Nikolaus Forgó Leibniz University Hannover nikolaus.forgo@iri.unihannover.de Why a lawyer? A Confession 3 4 5 Background ACGT (Advancing Clinico Genomic Trials