BTRIS: The NIH Biomedical Translational Research Information System James J. Cimino Chief, Laboratory for Informatics Development NIH Clinical Center

National Institutes of Health Clinical Center In-patient beds Day hospital and out-patient facilities Active protocols Terminated protocols Clinical researchers All patients are on a protocol

Clinical Data at NIH EHR Institute System Lab System Personal “System”

Clinical Data at NIH EHR Institute System Lab System Personal System

Clinical Data at NIH EHR Institute System Lab System Personal System BTRIS

Biomedical Translational Research Information System (BTRIS) Database Data Standards (RED) Data Access Security Preferences

Architecture Overview Generic data model in SQL-Server (Microsoft) Data acquisition: HL7, ODBC, batch Standard extraction-translation-loading Encoding with Research Entities Dictionary (RED) Terminology Development Editor (Apelon) National Cancer Institute extensions to TDE Cognos (IBM) business intelligence tool Javascript extensions to Cognos Home-grown system for user data entry

CRIS, MIS 33 NIAID NIAAA

BTRIS – Two Applications

BTRIS Data Access

What is in BTRIS? Clinical Center MIS ( ) and CRIS (2004-) Demographics Vital signs Laboratory results Medications (orders and administration) Problems and diagnoses Reports (admission, progress, discharge, radiology, cardiology, PFTs) National Institute of Allergy and Infectious Disease Medication lists Laboratory results Problems National Institute of Alcohol Abuse and Alcoholism Clinical assessments

BTRIS Data Growth MillionsofRowsMillionsofRows

BTRIS Data Access Reports IRB Inclusion CBC Panel Chem 20 Microbiology Demographics Individual Lab Lab Panels Medications Vital Signs Diagnoses/Problems Lists Individual Lab Test Lab Panels Medications Subjects Vital Signs

33 years of Data

BTRIS Reports per Week

BTRIS Users and Subjects 115 BTRIS Users thru March Non- BTRIS PIs += 245 BTRIS Beneficiaries 619 Unique Protocols 80,073 Attributed Subjects (of 395,005 attributions, or 20.27%)

Subject-Protocol Attributions 395,005 total attributions 126,533 verified by Medical Records 44,142 verified by IC systems 1,966 verified by users 363 unverified subjects “not on protocol” 236 verified subjects “not on protocol”

Re-using Data in De-Identified Form Look for unexpected correlations Pose hypothetical research questions Determine potential subject sample sizes Find potential collaborators

Access to De-identified Data De-identified data available to NIH intramural research community NIH researchers wanted access policy to ensure protection of intellectual property and first rights to publication Resolved through three means: –Association of data with an NIH PI –Status of protocol –Age of data

d) Active Protocol Access to De-identified (Coded) Data a) Data Outside Any Protocol Period b) Terminated Protocol – PI Gone c) Terminated Protocol – PI at NIH d) Active Protocol

Data Available for De-Identified Reports Total Subjects: 430,196 Not attributed to any protocol: 249,128 Attributed to Protocol: 181,068 Terminated > 5 yrs: 36,467

Data Available for De-Identified Reports Available Subjects – 285,595 (66.4%)

OHSR Exemption Process Required for all de-identified data queries Automated process replaces OHSR “Form 1” paper process for exemption

Serum Albumin Trends

Using BTRIS For Clinical Research Identify Potential Subjects Identify Potential Controls Include Cases with Pathology Specimens Subject Cases Control Cases Assign Case Numbers Potential Subject Cases Potential Control Cases Obtain Clinical Data Deidentified Subject Cases with Phenomic and Genomic Data Deidentified Subject Cases with Phenomic and Genomic Data Specimens Obtained from Pathology Department Send Case Numbers and MRNs to Pathology SNPs Sequenced Deidentify Cases

De-identified Text Reports and Other Data Merging Records Manual Scrubbing De-identified Text Reports Obtain Clinical Data Deidentified Subject Data Identified Text Reports Perform Query in Identified Form Trusted Broker Re-using BTRIS For Clinical Research Office of Human Subjects Research Develop Deidentified Query Investigator

Capabilities of Data Model Storage of like data in meaningful model Preservation of original data details Can “promote” commonalities to main table Preservation of original meanings Queries based on users’ aggregations

Informatics Challenges Understanding data sources Finding the right balance for unified data model Modeling in the Research Entities Dictionary Organizing the Research Entities Dictionary Understanding researchers’ information needs User interface (including Cognos customization) Keeping up with report requests Integration into multiple research workflows Access to deidentified data New policies on contribution and use

So What? Easier access to protocol data from EHR Easier access to archived data Protocol data integrated from multiple sources User empowerment Concept-based queries Data feeds to institute systems Data model flexible but not too flexible Rapid development timeline (under budget) User adoption can be considered good High user satisfaction Success with NIH policy Success with data sharing

Future Directions Finish historical data Add more institutes and centers

CRIS, MIS Radiology Images Other CC Sources 33 NIAID NIAAA NINDS NID DK NINRNINR N HG RI NHL BI NCINCI

Future Directions Images “-omic” data Specimen identification and location New reports and analytic tools Clinical Trials.gov reporting Beyond NIH Finish historical data Add more institutes and centers

btris.nih.gov