Michael G. Kahn MD, PhD Biomedical Informatics Core Director

1. Merging Clinical Care & Clinical Research in the EMR: Implementation IssuesNarrowing the Research-Practice Divide in Evidence-Based Medicine with Adoption of Electronic Health Record Systems: Present and Future DirectionsHosted by: National Institute on Drug Abuse13-14 July 2009 Michael G. Kahn MD, PhD Biomedical Informatics Core Director Colorado Clinical and Translational Sciences Institute Associate Professor, Department of Pediatrics University of Colorado Director, Clinical Informatics The Children’s Hospital, Denver Michael.Kahn@ucdenver.edu

2. Presentation Outline • Promises • Challenges • Warnings • Solutions • Kahn MG, Kaplan D, Sokol RJ, DiLaura RP. Configuration Challenges: Implementing Translational Research Policies in Electronic Medical Records. Academic Medicine, 2007; 82(7) 661-9. • A presentation based on article @ http://www2.amia.org/meetings/s07/docs/pdf/s28panel_kahn_tri.pdf

3. EMR versus EHR • From NAHIT (National Alliance for Health Information Technology) • EMR: The electronic record of health-related information on an individual that is created, gathered, managed, and consulted by licensed clinicians and staff from a single organization who are involved in the individual’s health and care. • EHR: The aggregate electronic record of health-related information on an individual that is created and gathered cumulatively across more than one health care organization and is managed and consulted by licensed clinicians and staff involved in the individual’s health and care. This talk focuses exclusively on E**M**R and clinical research (despite the title of this symposium!)

4. The Promise of the Electronic Medical Record • Merging prospective clinical research & evidence-based clinical care • A “front-end” focus • Improving care one patient at a time (decision support) • Merging clinical care and clinical research data collection • Clinically rich database for retrospective clinical research • A “back-end” focus • Making discoveries across populations of patients • Improving care at the population / policy level

5. T1 Biomedical Research Investigator Initiated T1  T2 Translational Research Industry Sponsored Commercialization Basic Research Data Pilot Studies New Research Questions Study Setup Study Design & Approval Outcomes Research Clinical Practice Clinical Trial Data Recruitment & Enrollment Evidence-based Patient Care and Policy EMR Data Submission & Reporting Evidence-based Review Study Execution Required Data Sharing Outcomes Reporting Public Information A Lifecycle View of Clinical Research From: C Broverman, Partners

6. How EMR’s could accelerate clinical research (Front-end)

7. How EMR’s could accelerate clinical research (Back-end)

8. T1 Biomedical Research Investigator Initiated T1  T2 Translational Research Industry Sponsored Commercialization Basic Research Data Pilot Studies New Research Questions Study Setup Study Design & Approval Outcomes Research Clinical Practice Clinical Trial Data Recruitment & Enrollment Evidence-based Patient Care and Policy EMR Data Submission & Reporting Evidence-based Review Study Execution Required Data Sharing Outcomes Reporting Public Information The EMR & Clinical Research: “Front-End” Issues From: C Broverman, Partners

9. Degrees of Constraints #1: The Regulatory Environment

10. Degrees of Constraints #2: Involved parties & roles

11. Degrees of Constraints #3: Clinical contexts • Inpatient versus outpatient • Full grant versus partial grant • Orders versus results • Radiology results versus laboratory results versus other clinical results • Clinical documentation • Need to ensure consistency with current practices, consents and assurances

12. Degrees of Constraints #4: Who can see what?

13. Degrees of Constraints #5: Contractual obligations • Pharmaceutical trials: Contractual requirements for confidentiality • Varies by contract terms • NIH Certificates of Confidentiality • Certificates of Confidentiality are issued by the National Institutes of Health (NIH) to protect the privacy of research subjects by protecting investigators and institutions from being compelled to release information that could be used to identify subjects with a research project. Certificates of Confidentiality are issued to institutions or universities where the research is conducted. They allow the investigator and others who have access to research records to refuse to disclose identifying information in any civil, criminal, administrative, legislative, or other proceeding, whether at the federal, state, or local level. • (From http://grants2.nih.gov/grants/policy/coc/background.htm)

14. Degrees of Constraints #6 (a & b): Integrating clinical research decisions into clinical care workflows 6a Registration Documentation Results review Billing Release of Information Data extraction into CTMS 6b Solutions must fit EMR functional capabilities Same vendor’s functional capabilities may differ between settings (inpatient versus outpatient)

15. Six workbooks • Sixteen research data domains • Data entry versus data visibility • Current versus Desired & Proposed Solution 576 cells to fill in With 14 user roles: 8064 cells! Working down the scenarios….

16. Our previous solution: Based on three desiderata* • Patient safety trumps investigator’s needs • Number one priority for COMIRB, research advocates, risk management • Confidentiality amongst TCH caregivers ≠ confidentiality/disclosures beyond TCH • When conflicts arise, return back to paper • Work with vendor to develop EMR-based solution * Latin for “something desired as essential”

17. Our previous solution: 3.5 answers required staying with paper

18. Our current solution…..

19. T1 Biomedical Research Investigator Initiated T1  T2 Translational Research Industry Sponsored Commercialization Basic Research Data Pilot Studies New Research Questions Study Setup Study Design & Approval Outcomes Research Clinical Practice Clinical Trial Data Recruitment & Enrollment Evidence-based Patient Care and Policy EMR Data Submission & Reporting Evidence-based Review Study Execution Required Data Sharing Outcomes Reporting Public Information The EMR & Clinical Research: “Back-End” Issues From: C Broverman, Partners

20. Data quality – The EMR’s dirty laundry • Suppose the previous issues were solved and investigators can easily use the EMR as a rich source of data for clinical research…… …..what is the quality of the results that come back?

21. Martial Status by Age: Would this result be worrisome?

22. It’s tough being 6 years old…….

24. Should we be worried? • No • Large numbers will swamp out effect of anomalous data or use trimmed data • Simulation techniques are insensitive to small errors • Yes • Public reporting could highlight data anomalies • Genomic associations look for small signals (small differences in risks) amongst populations

25. GIGO: Garbage in  Gospel Out

27. Where are we going from here? • Defining clear rules of what is required versus desired • Balancing patient safety versus research needs • May need to decide which rules to break • Who “owns” the final decisions on compromises? • Working to eliminate artificial implementation barriers • Designing workflows so that every patient is a research subject • Using EMR data for clinical research with a high degree of skepticism. Seek multiple paths for confirming findings

28. Thank you! Michael.Kahn@ucdenver.edu