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 EHR Institute System Lab System Personal “System”

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

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

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

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. CRIS, MIS 33 NIAID NIAAA

11. BTRIS – Two Applications

16. 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 MillionsofRowsMillionsofRows

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 115 BTRIS Users thru March 2010 130 Non- BTRIS PIs += 245 BTRIS Beneficiaries 619 Unique Protocols 80,073 Attributed Subjects (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. 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

40. 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

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 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

45. 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

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

51. 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

52. btris.nih.gov