The analyses upon which this publication is based were performed under Contract Number HHSM C sponsored by the Center for Medicare and Medicaid Services, Department of Health and Human Services. Current issues and challenges in sharing biomedical human subjects data OASIS 2014 Lucila Ohno-Machado, MD, PhD Division of Biomedical Informatics University of California San Diego Oasis 2014

Personalized Healthcare Which therapies work best for individual patients?

Person-Centered Outcomes Research Genome –Sequencing data Phenotype –Personal monitoring Blood pressure, glucose –Personal health records –Behavior monitoring Adherence to medication, exercise Environment –Air sensors, food quality –Location Source: DOE

Where does knowledge come from? Controlled studies with strict eligibility criteria Does this apply to me? Hopefully, but we need a lot of data to answer this question: We need to build infrastructure to access large data repositories –Lower the barriers to share data We need to share tools to analyze the data –Algorithms and computational facilities

Big Data, Medium Data, and Small Data Data integration across biological scales Data annotation and harmonization Data ‘anonymization’ and privacy preservation

Data for Personalized Medicine Prevention, Diagnosis and Therapy –Genetic predisposition –Biomarkers –Pharmacogenomics –Health records –Sensors Handling Protected Health Information – Secure Electronic Environment Electronic Health Records Genetic Data

Sharing Data Sharing data today – Data sharing plans required Little incentive to actually share – One model: users download data – Yes/No decision on sharing Data use agreements across institutions – Pairwise, limited and complicated – Specific to a particular study – Resources for sharing are limited – Security/privacy constraints are hard for small institutions to follow

Mission “A national center for biomedical computing that develops new algorithms, open-source tools, computational infrastructure, and services that will enable biomedical and behavioral researchers nationwide to integrate Data for Analysis, ‘anonymization,’ and Sharing”

Vision

Models for Data Sharing Cloud Storage: data exported for computation elsewhere – Users download data from the cloud Cloud Compute and Virtualization: computation goes to the data – Users analyze data in the cloud – Users download virtual machines

Three Different Models for Data Sharing 1.Users download data 2.Users compute in a central facility 3.Users install software that operates on their data and transmits results of operations (e.g., queries, analyses)

Model 1: Users download data “De-identification” may be necessary Encrypted transmission Data Use Agreement Central Lawyers from the University of California helped write – Data Contributor Agreement Who can have access for what purpose – Data User Agreement Terms of use iDASH serves as ‘agent’ for the data

Model 2: Users compute in central facility Securing the privacy of human subjects data including biometrics such as genomes There are known security issues with commercial clouds (business associate liability agreement mitigates some risks) A protected cloud compute environment is capable of operating on genomes and clinical data We have built this cloud environment in iDASH

Infrastructure Security for Human Subjects Data HIPAA (Health Insurance Portability and Accountability Act) compliant computing environment Segmentation (Zones) of projects & functionality Physical and environmental protection of compute hardware Access control with Two Factor Authentication Secure (encrypted tunnel) system access and upload capability Centralized logging, intrusion detection Proxies and filters Hardened (secured) system configurations

Model 3: Computation goes to the data Some health systems cannot host data outside their facilities (e.g., VA) Software can be sent to those facilities in order to build an overall model (e.g., regression)

University of California Research eXchange UC-ReX 1.UC Davis 2.UC Irvine 3.UC Los Angeles 4.UC San Diego 5.UC San Francisco Funded by the UC Office of the President to the NIH-funded CTSAs Integration of Clinical Data Warehouses from 5 University of California Medical Centers and affiliated institutions (>10 million patients) – Aggregate and individual-level patient data will be accessible according to data use agreements and IRB approval – Distributed models to adjust for confounders Objectives – Monitor patient safety – Improve outcomes – Promote research

Acknowledgements Slides contributed by the iDASH team Division of Biomedical Informatics Funding by NIH AHRQ PCORI UCOP UCSD