Towards privacy preserving comparative effectiveness research
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1 Towards privacy preserving comparative effectiveness research Kassaye Y. Yigzaw Johan Gustav Bellika Anders Andersen Gunnar Hartvigsen HelseIT 2013, Trondheim
2 Overview Motivation Comparative Barriers Identifiable data Deidentified data Secure multi-party computation Discussion 2
3 Motivation Demography change (i.e. aging population, multiple chronic conditions) Infectious diseases Health care system is under serious challenges 3
4 Motivation (2) An increased use of electronic health records (EHRs) Detail and diversity of healthcare and biomedical data is collected Health care systems effectiveness and efficiencies Patient outcomes and safety 4
5 Knowledge generation and use in medicine Alerts Quality Registries Clinical Decision Support Clinical Trials Order sets Compliance Checking Knowledge Management Computerized Guidelines Source: Rong Chen, MedInfo2013 tutorial on OpenEHR Guideline Definition Language 5
6 Comparative Effectiveness Research Generate evidence on the effectiveness, benefits, and harms of different treatment options in real life Study designs: systematic reviews of existing studies, RCTs, and observational data analyses Observational studies use existing data sources 6
7 Comparative Effectiveness Research (2) Lab test result, treatment and outcome, outpatient visits, hospitalization, primary care visits, pharmacy, and/or other information Patients receive care from multiple institutions Strong statistical power Population heterogeneity Horizontally and vertically partitioned dataset Link data distributed across multiple institutions 7
8 What is the problem? 8
9 What is the problem? 9
10 Objective Enjoy the benefits of both the privacy and research worlds! 10
11 Identifiable Data Use of identifiable data requires individuals consent Except under limited circumstances Difficult to obtain consent from some patients, such as severely ill, demented and pediatric patients Often, it is not practical to collect consent (i.e. large study size) 11
12 Identifiable Data (2) Consenter Vs. non-consenter difference Demographic and Socio-economic characteristics Biased samples 12
13 De-identified Data De-identified data can be used for research Health data can be deidentified: Removing identifiers (e.g. Safe harbor and limited dataset) Statistical methods The HIPAA safe harbor method involves removal of 18 identifiers including biometric or genetic data Limited dataset removes 16 identifiers (except date and zip code) and obtain data use agreement 13
14 De-identified Data (2) De-identification data usefulness re-identification Causal relationship between events Link data from multiple source to individual record Sub-populations level study 14
15 Secure multi-party computation (SMC) Health institution 1 Trusted Third Party Secure multi-party computation emulate the trusted party Health institution 2 Bogdanov D. Sharemind-Easily programmable secure multi-party computation on integers, strings and floating point numbers. Health institution 3 15
16 Secure multi-party computation (2) A set of two or more parties with private inputs, x 1,..,x n wish to jointly compute a function, f(x 1,..,x n ), of their inputs Parties wish to preserve some security properties. E.g. privacy and correctness. Even in the face of adversarial behavior by some of the participants, or by an external party. Yehuda Lindell. Presentation Tutorial on Secure Multi-Party Computation. IBM T.J.Watson 16
17 History Introduced by Yao in 1982 (two-party computation) Goldreich et al. in 1987 (Multi-party computation) No practical implementation until the last decade 17
18 SMC techniques Generic techniques (i.e. Garbled circuit, Homomorphic encryption, Secret sharing) Specialized techniques (i.e. secure sum, scalar product) 18
19 SMC protocols All to all communication Representative based approach Considered not efficient and not scalable to hundreds and thousands of distributed data sources 19
20 Distributed SMC Decompose the computation problem in a way that can be computed by neighbor peers in parallel A peer only jointly compute with neighbors ONLY combined statistics of neighbor peers private data will be learned Reasonable to hide private data in combined statistics of neighbor peers 20
21 Distributed SMC (2) Constant communication complexity Enable parallel computations Execute asynchronous algorithms Hypothesis: Distributed SMC enables more efficient and scalable solutions. 21
22 Discussion Data sources maintain autonomy over their record No new information can be discovered after a computation Preserve patients and data owners privacy Increased data owners motivation to participate 22
23 Reference Towards Privacy-Preserving Computing on Distributed Electronic Health Record Data Middleware 2013 (submitted) 23
24 Acknowledgement Gro Berntsen, Norwegian Center for Integrated care and Telemedicine, University Hospital North Norway Tromsø Telemedicine Laboratory (TTL) University of Tromsø Norwegian center for integrated care and telemedicine (NST) 24
25 Contact Information Kassaye Y. Yigzaw PhD student University of Tromsø
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