Enabling Data Sharing in Biomedical Research Integrating Data for analysis, Anonymization, and Sharing (iDASH) Aziz A. Boxwala, MD, PhD Division of Biomedical.

Slides:



Advertisements
Similar presentations
HIPAA Privacy Rule “Standards for Privacy of Individually Identifiable Health Information” 45 CFR 160 and 164* *
Advertisements

UC BRAID: Co-creating and evaluating performance in a regional laboratory for conducting translational science UC BRAID Executive Committee: Steven Dubinett.
The Veterans Affairs Central Biorepository and MVP Highlights Mary T
HIPAA – Privacy Rule and Research USCRF Research Educational Series March 19, 2003.
Increasing public concern about loss of privacy Broad availability of information stored and exchanged in electronic format Concerns about genetic information.
Informed Consent.
Health Insurance Portability & Accountability Act “HIPAA” To every patient, every time, we will provide the care that we would want for our own loved ones.
PERMISSION ONTOLOGY FOR INFORMED CONSENT AND HIPAA COMPLIANCE Maria Adela Grando PhD Division Biomedical Informatics, University California.
HIPAA Training Presentation for New Employees How did we get here? HIPAA Police 1.
Disclosure I, Peter T. Katzmarzyk, PhD, FACSM, have no relationships with commercial interests to disclose. A commercial interest is any entity producing,
Nora B. McCann Privacy Manager Corporate Compliance Fox Chase Cancer Center
What does this form mean? HIPAA Authorization means prior written permission for use and disclosure of protected health information (PHI) from the information’s.
Bioinformatics Training for Dental Researchers Lynn Johnson, Ph.D. University of Michigan.
Overview of Biomedical Informatics Rakesh Nagarajan.
1 HIPAA, Researchers and the IRB: Part Two Alan Homans, IRB Chair and Nancy Stalnaker, IRB Administrator.
SPECIAL DIABETES PROGRAM FOR INDIANS Competitive Grant Program Special Diabetes Program for Indians Competitive Grant Program SPECIAL DIABETES PROGRAM.
HIPAA, Researchers and the IRB Alan Homans, IRB Chair and Nancy Stalnaker, IRB Administrator.
Registry 201 Excel Registry Training. Registry 201 Excel Registry Training Outline ► Important Information about PHI ► Getting to know you ► Excel Training.
BTRIS: The NIH Biomedical Translational Research Information System James J. Cimino Chief, Laboratory for Informatics Development NIH Clinical Center.
BTRIS: The NIH Biomedical Translational Research Information System James J. Cimino Chief, Laboratory for Informatics Development NIH Clinical Center.
Health Insurance Portability and Accountability Act (HIPAA)
Adam Wilcox, PhD Associate Professor of Biomedical Informatics.
Kathryn Camp, M.S., R.D., CSP Consultant to the Office of Dietary Supplements National Institutes of Health Secretary’s Advisory Committee on Heritable.
2012 VA Human Research Protection Program Patricia L. Christensen, MS, RHIA, CIPP/G, CHPS, CHPC VHA Privacy Office Common Privacy Findings in Research.
Data Security and Research 101 Completing Required Forms Kimberly Summers, PharmD Assistant Chief for Clinical Research South Texas Veterans Health Care.
MIRC Clinical Trials Software Medical Imaging Resource Center.
Cornell Evaluation Network The Use of Human Participants in Research Office of Research Integrity and Assurance ~ May 14, 2007.
Human Research Protection Programs 1a: How to Navigate Human Subject Protection Regulations Sponsored by the American Society for Investigative Pathology.
Paula Peyrani, MD Medical/Project Director, HIV Program at the 550 Clinic Assistant Director, Research Design and Development Clinical and Translational.
S New Security Developments in DICOM Lawrence Tarbox, Ph.D Chair, DICOM WG 14 (Security) Siemens Corporate Research.
Yesterday, today, and tomorrow
HIPAA Business Associates Leadership Group Meeting June 28, 2001.
1 Research & Accounting for Disclosures March 12, 2008 Leslie J. Pfeffer, BS, CHP Office of the Vice President for Research Administration Office of Compliance.
Revised February 4, Health Insurance Portability and Accountability Act (HIPAA) HIPAA Privacy Rule: UCSF Education Module for Researchers, Research.
1 HIPAA OVERVIEW ETSU. 2 What is HIPAA? Health Insurance Portability and Accountability Act.
14 May Privacy Requirements Phoenix Ambulatory Blood Pressure Monitoring System © 2006 Christopher J. Adams Copying and distribution of this document.
HIPAA Privacy and Research August 21, 2015
De-identifying Pathology Reports for Pathology Informatics
Standards & Vocabulary
Health information that does not identify an individual and with respect to which there is no reasonable basis to believe that the information can be.
Future Use of Stored Samples & Data and the NIH Policy on GWAS and dbGaP NIAID/DAIDS Dione Washington, M.S. -- ProPEP Sudha Srinivasan, Ph.D.-- TRP Tanisha.
The analyses upon which this publication is based were performed under Contract Number HHSM C sponsored by the Center for Medicare and Medicaid.
PwC Tissue Banking and Repositories – Human Subject Protections Privacy Protections Medical Research Summit Tom Puglisi, Ph.D. Friday March 7 – 9:15 am.
HIPAA – How Will the Regulations Impact Research?.
NIH Data Sharing Dr. Belinda Seto, Deputy Director National Institute of Biomedical Imaging and Biomedical Engineering (NBIB) June 23, 2004 Collaborative.
Patient Data Security and Privacy Lecture # 7 PHCL 498 Amar Hijazi, Majed Alameel, Mona AlMehaid.
Facilitate Scientific Data Sharing by Sharing Informatics Tools and Standards Belinda Seto and James Luo National Institute of Biomedical Imaging and Bioengineering.
1 Ethical issues in genomics research Bernard Lo, M.D. March 3, 2009.
Group Science J. Marc Overhage MD, PhD Regenstrief Institute Indiana University School of Medicine.
Data Sharing in Nursing: What Researchers Need to Know November 9, 2015 Caitlin Bakker, Research Services Librarian |
Office of Human Research (OHR) Quality Improvement Program Patrick Herbison Heather Krupinski.
EHR & BIG DATA – RISKS AND ADVANTAGES OF AMASSING MEDICAL DATABASES Sandra Gardiner Technology Law Section October 24, 2014.
Navigating IRBs as a Suicide Researcher Peter M. Gutierrez, Ph.D. VISN 19 MIRECC American Association of Suicidology Annual Conference, April 19, 2012.
NIH and the Clinical Research Enterprise Third Annual Medical Research Summit March 6, 2003 Mary S. McCabe National Institute of Health.
Configuring Electronic Health Records Privacy and Security in the US Lecture b This material (Comp11_Unit7b) was developed by Oregon Health & Science University.
PwC Issues in HIPAA Research Compliance William R. Braithwaite, MD, PhD “Dr. HIPAA” HIPAA Summit 6 Washington, DC 27 March 2003.
Office of Human Research (OHR) Quality Improvement Program Patrick Herbison Heather Krupinski.
Teaching & POEMs and DOEs in an Online Classroom Jacob Reider, MD David C Ross Albany Medical College.
Final HIPAA Privacy Rule: The Research Provisions Julie Kaneshiro DHHS Office for Human Research Protections Phone: Fax:
Privacy: HIPAA Emerson Murphy-Hill. Rosie Callender, RHIA, web.msm.edu/hipaa/An%20Introduction%20to%20HIPAA.ppt What is HIPAA? A Federal Law Created in.
De-Identified Data: Ethics and Regulation Translational Research Ethics – Applied Topics (TREATs) Bioethics and Subjects Advocacy Program Indiana Clinical.
Semantic Web - caBIG Abstract: 21st century biomedical research is driven by massive amounts of data: automated technologies generate hundreds of.
Solutions to Clinical Data Visualization and Analysis
Unit 5 Systems Integration and Interoperability
No No, Yes Yes: Simple Privacy & Information Security Tips Krista Barnes, J.D. Senior Legal Officer and Director, Privacy & Information Security, Institutional.
Transfer of Materials, Confidential Information, and Data
Issues in HIPAA Research Compliance
DSHS, Environmental & Injury Epidemiology and Toxicology
Case Study Template Kerecis Aurora Awards
The Health Insurance Portability and Accountability Act
Presentation transcript:

Enabling Data Sharing in Biomedical Research Integrating Data for analysis, Anonymization, and Sharing (iDASH) Aziz A. Boxwala, MD, PhD Division of Biomedical Informatics UCSD 1U54GM /25/2010

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

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

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

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

UCSD (Epic) Data matching function: Map D onto data dictionaries MRN MRN 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 UC Irvine (Eclipsys) UC Davis (Epic) UCSF (GE) Community Partners Integrating Data (from different institutions)

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

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

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

IRB concerns

Limiting results to counts No inherent privacy: Original Reconstructed

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

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

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

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

Informed Consent

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

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

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

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

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

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

Biomedical CyberInfrastructure Architecture Rich Services developed by Ingolf Krueger and colleagues

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

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

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

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.

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

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

Thank you