New Informatics Capabilities for Scholarly Projects & Disease Registries

1. New Informatics Capabilities for Scholarly Projects & Disease Registries Russ Waitman, PhD (plus KahliaFord, Dongsheng Zhu, and Dan Connolly) Associate Professor, Director Medical Informatics Department of Biostatistics December 8, 2010

2. Outline • New tools: Redcap (easy); HERON (hard) • Existing Velos capability in CRIS • Redcap Demo • HERON/i2b2 discussion • CTSA • Oversight Process • Demo • Milestones • General Discussion

3. Clinical Research Information Systems • KUMC has purchased VeloseResearch and calls it “CRIS” • Define Studies, Assign Patients to Studies • Design and Capture data on electronic Case Report Forms (CRFs) – ideally in real time. • Capture Adverse Events, Reports, Export Data for analysis. • Options: Samples, Financials, Regulatory IRB • Other Approaches • OnCore by Forte Research Systems – more expensive, highly customized for Cancer Centers…. Ferrari to Velos’ Audi. • RedCap by Paul Harris at Vanderbilt University – “free but not open source”, capabilities growing. Think Hyundai

4. CRIS Intro Screen

5. CRIS: sample e Case Report Form

6. CRIS: Document Adverse Events

7. Redcap process • For all needs: Register your project with us so we can make sure we don't screw up and drop the ball. • Our first customers: OSARM and Urology. Urology had the opportunity to re-use data dictionaries developed by Vanderbilt Urology for Prostatectomy and Cystectomy • Check out the cool training materials under videos at  http://www.project-redcap.org/ • Check out what other people have done that you can modify/steal in the library. • After you register your project, a CRIS team member, likely Kahlia Ford will get in touch with you. • We'll set you up and give you access to production. We also have a test environment which I will use for this demo.  http://bmidev1.kumc.edu/redcap/ • It uses the same username and password as everyone's email. • There's a survey module which we haven't played with yet but will be supporting if there's interest.

8. Redcap Disclaimer • Works if PI takes responsibility for data • Scalability: informatics provides consultation and responsibility for technical integrity; not your dictionary. • Existing for clinical trials: CRIS/Velos • Multiple years of experience • CRIS team builds for you with biostats review • Ideal for defined trials/grants • Budget for CRIS team and biostatsexplicity

9. CTSA Background: NIH Goal to Reduce Barriers to Research • Administrative bottlenecks • Poor integration of translational resources • Delay in the completion of clinical studies • Difficulties in human subject recruitment • Little investment in methodologic research • Insufficient bi-directional information flow • Increasingly complex resources needed • Inadequate models of human disease • Reduced financial margins • Difficulty recruiting, training, mentoring scientists

10. NIH CTSAs: Home for Clinical and Translational Science NIH Other Institutions Clinical Research Ethics Trial Design Gap! Advanced Degree-Granting Programs Biomedical Informatics CTSA HOME Industry Participant & Community Involvement Clinical Resources Biostatistics Regulatory Support Dan Masys: http://courses.mbl.edu/mi/2009/presentations_fall/masys.ppt

11. KUMC CTSA Specific Aims • Provide a HICTR portal for investigators to access clinical and translational research resources, track usage and outcomes, and provide informatics consultative services. • Create a platform, HERON (Healthcare Enterprise Repository for Ontological Narration), to integrate clinical and biomedical data for translational research. • Advance medical innovation by linking biological tissues to clinical phenotype and the pharmacokinetic and pharmacodynamicdata generated by research cores in phase I and II clinical trials (addressing T1 translational research). • Leverage an active, engaged statewide telemedicine and Health Information Exchange (HIE) effort to enable community based translational research (addressing T2 translational research).

13. Aim #2: Create a data “fishing” platform • Develop business agreements, policies, data use agreements and oversight. • Implement open source NIH funded (i.e. i2b2) initiatives for accessing data. • Transform data into information using the NLM UMLS Metathesaurus as our vocabulary source. • Link clinical data sources to enhance their research utility.

14. Develop business agreements, policies, data use agreements and oversight. • September 6, 2010 the hospital, clinics and university signed a master data sharing agreement to create the repository. Four Uses: • After signing a system access agreement, cohort identification queries and view-only access is allowed but logged and audited • Requests for de-identified patient data, while not deemed human subjects research, are reviewed. • Identified data requests require approval by the Institutional Review Board prior to data request review. Medical informatics will generate the data set for the investigator. • Contact information from the HICTR Participant Registry have their study request and contact letters reviewed by the Participant and Clinical Interactions Resources Program

15. Constructing a Research Repository: Ethical and Regulatory Concerns • Who “owns” the data? Doctor, Clinic/Hospital, Insurer, State, Researcher… perhaps the Patient? • Perception/reality is often the organization that paid for the system owns the data. • My opinion: we are custodians of the data, each role has rights and responsibilities • Regulatory Sources: • Health Insurance Portability and Accountability Act (HIPAA) • Human Subjects Research • Research depends on Trust which depends on Ethical Behavior and Competence • Goals: Protect Patient Privacy (preserve Anonymity), • Growing Topic: Quanitifying Re-identification risk.

16. Re-identification Risk Example Will the released columns in combination with publicly available data re-identify individuals? What if the released columns were combined with other items which “may be known”? Sensitive columns, diagnoses or very unique individuals? New measures to quantify re-identification risk. Reference: Benitez K, Malin B. Evaluating re-identification risks with respect to the HIPAA privacy rule. J Am Med Inform Assoc. 2010 Mar-Apr;17(2):169-77.

17. WizOrder Client Lab DB Generic Interface Engine (GIE) WizOrder Server Pharmacy System Rx DB Print SubSystem Mainframe DB2 Constructing a Repository: Understanding Source Systems, Example CPOE Most Clinical Systems focus on transaction processing for workflow automation Repackages and Routes SQL Laboratory System Internal Format HL7 HL7 SQL Temporary Data queue (TDQ) SQL document Knowledge Base, Files SQL SQL Orderables, Orderset DB Drug DB SQL

18. Constructing a Repository: Understanding Differing Data Models used by Systems Hierarchical databases (MUMPS), still very common in Clinical systems (VA VISTA, Epic, Meditech) http://www.cs.pitt.edu/~chang/156/14hier.html Murphy SN, Weber G, Mendis M, Gainer V, Chueh HC, Churchill S, Kohane I. Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2). J Am Med Inform Assoc. 2010 Mar-Apr;17(2):124-30. Star Schemas: Data Warehouses http://www.ibm.com/developerworks/library/x-matters8/index.html Relational databases (Oracle, Access), dominant in business and clinical systems (Cerner, McKesson)

19. HERON: Repository Architecture

20. Workflow: System Access

21. Workflow & Oversight: Request Data

22. Implement NIH funded (i.e. i2b2) initiatives for accessing data.

23. i2b2: Count Cohorts

24. i2b2: Patient Count in Lower Left

25. i2b2: Ask for Patient Sets

26. i2b2: Analyze Demographics Plugin

27. i2b2: Demographics Plugin Result

28. i2b2: View Timeline

29. i2b2: Timeline Results

30. Transform data into information using standard vocabularies and ontologies

31. What we’ve done and current plan • 4 milestones: Statistics, Alpha, Beta, 1.0 • NightHeronStats: data obtained in October added crude statistics to CTSA submission • Alpha: complete as of Tuesday • Full production environment process in place. • Get data, store, transform, deidentify, i2b2 access. • Epic only: • Demographics, Diagnoses (pat_enc), • Meds (dispenses; using Epic hierarchy), • Labs (final, top 500 mapped to LOINC) • Validation is a work in progress • Uncovering i2b2 “bugs” in web client

32. Current Plan continued • Beta: target January • Implement System Access Agreement Process • Add Problem List data (diagnoses) • Add vital signs, nursing observations for informatics research • Understand “age” • Auditing, logging to central service • Start validation with Hospital • Prototype billing data ICD9/CPT from IDX clinic billing system

33. Current and Future Plan • HERON 1.0: target Spring • Implement Data Use Agreement, download data mechanism • Ready for widespread use • Add data: IDX integration with Epic, other “key” results • Improve medication representation • Provider/service representation and search? • HERON 1.X and 2.0: Summer? • Monthly updates • More data and features (Path, Rad, Micro) • Wire up EMR intervention data for informatics research