BTRIS: The NIH Biomedical Translational Research Information System

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

2. National Institutes of Health Clinical Center In-patient beds - 234 Day hospital and out-patient facilities Active protocols - 1800 Terminated protocols - 7100 Clinical researchers - 4700 All patients are on a protocol

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

5. Clinical Data at NIH Institute System Personal System EHR Lab System

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

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

9. 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

10. NIAAA NIAID 33 CRIS, MIS

11. BTRIS – Two Applications

15. BTRIS – Two Applications

16. BTRIS – Two Applications BTRIS Data Access

17. What is in BTRIS? • Clinical Center MIS (1976-2004) 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

18. BTRIS Data Growth M i l l i o n s o f R o w s

19. 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

30. 33 years of Data

31. BTRIS Reports per Week

32. BTRIS Users and Subjects 130 Non- BTRIS PIs 245 BTRIS Beneficiaries + = 80,073 Attributed Subjects 619 Unique Protocols 115 BTRIS Users thru March 2010 (of 395,005 attributions, or 20.27%)

33. 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”

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

38. 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

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

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

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

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

43. Serum Albumin Trends

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

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

46. 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

47. 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

48. 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

49. Future Directions Finish historical data Add more institutes and centers

50. N I N R NIAAA NIAID 33 Other CC Sources Radiology Images CRIS, MIS N C I NINDS NHL BI NID DK N HG RI