1. Enabling Data Sharing in Biomedical Research Integrating Data for analysis, Anonymization, and Sharing (iDASH) Aziz A. Boxwala, MD, PhD Division of Biomedical Informatics UCSD 1U54GM095327 10/25/2010

2. Sharing Biomedical Data –Today Public repositories (mostly non-clinical) Limited DUAs, public fear Data ‘transmitted’ by FedEx –Tomorrow Annotated public databases Certified trust network Consented sharing and use Sharing Computational Resources –Today Computer scientists looking for data, biomedical and behavioral scientists looking for analytics Processed data not shared Massive storage and high performance computing limited to a few institutions –Tomorrow Teams working to solve a problem (e.g., human genome project) Processed anonymized data shared for verification and algorithmic improvement Secure biomedical/behavioral cloud available to all

3. Challenges Data integration Maintenance of research subject’s privacy Respect for research subject’s autonomy Data analysis due to novel science Lack of infrastructure

4. Challenges Data integration Maintenance of research subject’s privacy Respect for research subject’s autonomy Data analysis due to novel science Lack of infrastructure

5. labsregistries genometranscriptomeproteome Integrating Data (from different biological levels) GenotypeRNA Biomarkers transcriptiontranslation Population Protein Phenotype clinical data

6. UCSD (Epic) Data matching function: Map D onto data dictionaries MRN 23212 MRN 43244 MRN 6554MRN 4433 Researcher is authorized to get data D about I for reason R Return data D Request about individual I Request for data D ID matching function Remote Monitor DB MRN 234512 UC Irvine (Eclipsys) UC Davis (Epic) UCSF (GE) Community Partners Integrating Data (from different institutions)

9. Challenges Data integration Maintenance of research subject’s privacy Respect for research subject’s autonomy Data analysis due to novel science Lack of infrastructure

10. The HIPAA Identifiers 1.Names 2.All geographical subdivisions smaller than a State, except for the initial three digits of a zip code 3.Dates (except year) directly related to an individual, including birth date, admission date, discharge date, date of death and all ages over 89 and all elements of dates (including year) indicative of such age, except that such ages and elements may be aggregated into a single category of age 90 or older 4.Phone numbers 5.Fax numbers 6.Electronic mail addresses 7.Social Security numbers 8.Medical record numbers 9.Health plan beneficiary numbers 10.Account numbers 11.Certificate/license numbers 12.Vehicle identifiers and serial numbers, including license plate numbers 13.Device identifiers and serial numbers 14.Web Universal Resource Locators (URLs) 15.Internet Protocol (IP) address numbers 16.Biometric identifiers, including finger and voice prints 17.Full face photographic images and any comparable images 18.Any other unique identifying number, characteristic, or code

11. HIPAA data sets De-identified data set –Does not include 18 identifiers Limited data set –can include the following identifiers: Geographic data: town, city, State and zip code, but no street address. Dates: A limited data set can include dates relating to an individual (e.g., birth date, admission and discharge date). Other unique identifiers: A limited data set can include any unique identifying number, characteristic or code other than those specified in the list of 16 identifiers that are expressly disallowed Fully identified data set –All identifiers allowed

12. IRB concerns

14. Limiting results to counts No inherent privacy: Original Reconstructed

15. Serving result counts Allows: –Cohort finding –Exploration Need: –Perturbation Q Estimated Count + Count returned noise

17. Truly privacy preserving data Yields information about distribution independent of any individual data point How: Sampling from robust representation of joint probability distribution learn Sample Privacy preservingOriginalRobust distribution

18. Source Anonymization Multiple participating data sources (PDSs) contribute data to a central processing unit (CPU) –Cyptographic anonymization cloud:

19. Challenges Data integration Maintenance of research subject’s privacy Respect for research subject’s autonomy Data analysis due to novel science Lack of infrastructure

20. Informed Consent

21. Biospecimen and data repositories are creating archives for future, possibly unforeseen types of research Does this create challenges in adhering to the autonomy (right to self-determination) principle of biomedical ethics? We want to enable subjects to have better control on their participation in research Different consents within the same repository will create a challenge for investigators in selecting subjects –Matching research aims to consented uses –Selection biases

22. Electronic Informed Consent Management Create an informed consent ontology that can represent various dimensions of subject’s consent for research Develop an electronic informed consent registry that documents the subjects’ consents –Enables subjects to update consent Create a mediator that can resolve an investigator’s request for samples, data, or subject participation against the consented uses

23. Challenges Data integration Maintenance of research subject’s privacy Respect for research subject’s autonomy Data analysis due to novel science Lack of infrastructure

24. Data Analysis Library Genome Data –Compression –Genome query language Pattern recognition Computing with streams Rare events

25. Challenges Data integration Maintenance of research subject’s privacy Respect for research subject’s autonomy Data analysis due to novel science Lack of infrastructure

26. Data Publishing and Computational Resources Mismatches –Data availability –Computational resources and expertise iDASH services –Data acquisition, annotation, storage, dissemination –Scientific workflow execution –Governance and policy framework for data access control –Accessible via web portal and API

27. Biomedical CyberInfrastructure Architecture Rich Services developed by Ingolf Krueger and colleagues

29. Driving Biological Projects Kawasaki Disease Research Anticoagulant Medication Safety Remote Monitoring of Behavior

30. Kawasaki Disease (PI: Jane Burns) Aim 1: To sequence size-selected cDNA from whole blood from KD patients and age-similar children with acute adenovirus infection to identify miRNA abundance patterns and to relate these patterns to disease state and to KD clinical outcome Aim 2: To selectively sequence genomic DNA regions in the pathway genes of interest to identify rare genetic variants that may play a functional role in disease susceptibility and outcome Aim 3: To create a KD data warehouse and web- based data analysis system aimed at facilitating discoveries using clinical and molecular data

31. Anticoagualant Medication Monitoring (PI: Fred Resnic) Aim 1: To determine baseline expectations for bleeding events for prasugrel and dabigatran, clopidogrel, and warfarin in eligible patients Aim 2: To evaluate the usefulness of aggregating information from 3 healthcare centers in an automated risk-adjusted medication safety monitoring tool that alerts for unsafe use of medications in particular cohorts of patients

32. Monitoring Sedentary Behavior (PI: Greg Norman) Phase 1 –physical activity behavior pattern recognition and feedback device and test for Device Limiting Failures (DLFs) with 12 adults for two week cycles using a Phase I clinical trial approach. Phase 2 –efficacy testing of the prototype with iterative improvement/ retesting in 30 sedentary adults with outcomes of accelerometer measured activity and sedentary time evaluated against controls for a 6 week intervention period. Phase 3 –pilot randomized trial with 48 sedentary adults receiving either the intervention device or assessments only for a 3 month period evaluated with accelerometer-measured activity and sedentary time.

33. New science: new computational needs DBP1 –Genetic data compression –Pattern recognition –Data integration from different biological levels DBP2 –Data integration from different institutions aggregated results from three medical centers that serve different types of patients (BWH, VA TN, UCSD) –Rare event detection DBP3 – –Pattern recognition from streaming data from personal monitoring –Integration of spatial, temporal, physiological, and behavioral data

34. PI (Ohno-Machado) Core 1 R&D (Bafna, Vinterbo) Algorithms (Varghese) Software Engineering (Krueger) Statistical Methods (Messer) Core 2 Driving Projects (Ohno-Machado) DBP 1 Kawasaki Genomics (Burns) DBP 2 Pharmacosurveillance (Resnic) DBP 3 Activity Patterns (Norman) Core 3 Infrastructure (Thornton) High Performance Computing System Administration Helpdesk Core 4 Training (Pevzner) San Diego State University Master’s (Valafar) UCSD Doctoral Program UCSD Medical Center Rotation Core 5 Dissemination (Patrick) Annual Workshop User Group Technical Support Core 6 Administration (Boxwala, Balac) Evaluation DBP Selection Committee NCBC consortium Advisory Council Steering Committee Executive Committee Operations Committee iDASH Team

35. Thank you aboxwala@ucsd.edu