electronic health records facilitating clinical research

1. Electronic Health RecordsFacilitating Clinical Research

5. Cleveland Clinic and Research (2009) Funding & Scope $272 M Total Research Funding $91 M in Federal NIH Awards Over 2,200 active projects 434 Active Registries with avg. 4 � 6 cohorts per registry Generalizability of Patient Population 75% of patients came from Cleveland�s seven adjacent counties 1.5 M visits in the regional medical practice sites (community-based clinics) �Centers of Excellence� for numerous diseases areas

6. Cleveland Clinic EHR implementation Path

9. Our investigators want the EHR to help them! Identify current research subjects in the EHR Develop tools to help recruit potential research subjects Appropriately bill activity occurring in EHR-managed visits to the sponsor for research activity versus usual payor for standard of care. Develop Single Source capability with extraction to eCRF Capture rich structured data from the EHR (phenotypic) and combine with bio-informatics data (genotypic) Easily move valid data from EMR into research registries Facilitate secure EMR access for research monitors

10. Provides EMR-centric Resources for other groups

11. Leveraging the EHR to capture critical data for researchers

13. Multidisciplinary eResearch Team

14. Guidance�

15. A Research Study Scenario(These parameters and numbers are purely fictional and intended only to demonstrate the scenario) Inclusion Diabetes Type 2 Age 18 to 65 at screening Treatment Na�ve or Oral mono-therapy Exclusion Uncontrolled Hypertension Triglycerides >= 1000 mg/dl Lipid-lowering therapy not stable for 1 year History of myocardial infarction or unstable angina History of coronary artery bypass graft surgery or angioplasty History of insulin use (other than gestational diabetes) History of substance abuse or unlikely to finish study

16. Analysis of Protocol Criteria

17. Ontology & Vocabulary

18. High-level Summary of the Impact of Each Criterion

20. Trial Support

23. Physician attitudes� Embi PJ, Jain AK, Harris CM. Physicians' perceptions of an electronic health record-based clinical trial alert approach to subject recruitment: A survey. BMC Med Inform Decis Mak. 2008 Apr 2;8:13.

24. What are the characteristics of the alert? False Positives Many referrals made for each enrollee � excessive false positives� EHR may not capture key criteria. Chart review may not validate �computable� criteria Patients may not necessarily be good candidates or willing to consent False Negatives Documentation gap Time lag between presentation and documentation Only patients who have come in for a visit

25. Integrating study criteria with scheduling�

26. EHR-data based recruitment lists work! Cleveland Clinic involved in multi-site clinical trial for safety of NIH - H1N1 vaccine among children with severe asthma. Comparison of the eResearch services to the Severe Asthma Research Program (SARP) network registry. eResearch led to higher enrollment, 93/540 (17.2%) eResearch vs. 24/109 (22%) for SARP. Performance was similar to the volunteer registry without significant increase in costly screen failures Diversity in terms of race/ethnicity of the subjects was increased using EHR-based identification Parikh P, Jain A, et al. Recruitment and Enrollment of Asthmatics in a Phase II Clinical Trial, ATS Meeting, May 17, 2010.

27. Increasing participation and diversity� (U Pitt) Over a 22-month period, EMR-prompts for recruitment: PCPs referred 794 patients via EMR-prompts and 176 (22%) met study inclusion criteria and enrolled, 8,095 patients were approached by wait room-based recruiters of whom 193 (2.4%) enrolled. Subjects enrolled by EMR-prompted PCPs were more likely to be non-white (23% vs 5%; P < 0.001), male (28% vs 18%; P = 0.03) Rollman BL et al. Comparison of electronic physician prompts versus waitroom case-finding on clinical trial enrollment. J Gen Intern Med. 2008 Apr;23(4):447-50.

28. Three recent outcomes and CER projects� Projects: Modeling cardiovascular outcomes in patients on oral hypoglycemic agents Modeling cardiovascular complication rates in patients admitted to the hospital with acute coronary syndrome Identifying determinants of progression of kidney disease in patients with chronic kidney disease Why was the EHR used? Size and scope of required electronic data was mostly already in the EHR Competitive advantage for obtaining sponsors

29. Strategies to Overcome EHR Data Reliability Issues RELIABILITY CHALLENGES Death is not always reliably captured in an EHR derived data set. Documentation of certain exclusions and adverse events are generally not captured as structured data Prescription medication dispensed and taken including OTC Patients may have fragmented care with some clinical data outside institutional EHR.

31. Explorys Population Explorer Cleveland Clinic spin-off - Software-as-a-Service offering� Explore: Search, browse, and define cohorts based on clinically normalized dataset from multiple providers. Compare: Analyze temporal measures between cohorts. Collaborate: Safely connect and share with trust peers and sponsors. Engage: Analyze in-depth HIPAA compliant datasets or recruit across internal or distributed trusted networks.

32. The Explorys Unified Platform

33. Questions? Anil Jain, MD, FACP Cleveland Clinic jaina@ccf.org