1. Electronic Health Records Facilitating Clinical Research
Clinical Research Management Workshop
Tuesday, June 22, 2010
Anil Jain, MD, FACP
Senior IT Executive, Information Technology, Cleveland Clinic
Managing Director, eResearch, eCleveland Clinic
Co-Director, Biomedical Informatics, CTSC, Case Western Reserve University 1 1

2. Objectives Provide an overview of eResearch at the Cleveland Clinic
Review our experience with simulation of clinical trial protocols using EHR data
Demonstrate our ability to enhance clinical trial recruitment with EHRs
Show how we have leveraged EHR data for comparative effectiveness research

3. The Cleveland Clinic

4. The Cleveland Clinic The Cleveland Clinic Founded in 1921
Not-for-profit group practice
2096 Professional Staff and scientists
Innovations include:
First coronary angiography
Development and refinement of coronary bypass surgery
First minimally invasive aortic heart valve surgery
First successful larynx transplant
Discovery of first gene linked to coronary artery disease
CCHS Statistics (2009):
Operating Revenue: ~ 5.6 Billion
Clinical visits: Nearly 4 Million
Admissions: 155,000
U.S.News & World Report rankings:
4th among nation’s 6,000 hospitals
Nation’s #1 heart program
16 specialties ranked
The Cleveland Clinic
The Cleveland Clinic

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
1999
2000
2002
2004
2006
2008
2010
HIE
OR IS
Billing & Reimbursement
Inpt Notes
ADT
Inpatient CPOE
Document Scanning
Tethered Patient Portal
Enterprise Master Patient Index
Electronic Prescriptions
Clinical Decision Support and Quality Reporting
Ambulatory EMR CPOE + Documentation
Integrated Lab/Radiology Results Reporting
Scheduling & Registration 6 6

7. EHR data is routinely used at our Institution…
Health Wellness and Prevention
Immunizations, Osteoporosis, Diabetes and Cancer screening
Disease-based Reporting
Diabetes, Hypertension, Heart Failure, Asthma
Patient Safety
Adverse Drug Events and sentinel events
FDA Public Health Advisories (e.g., Vioxx® and Ortho-Evra®)
Local Consensus (e.g., Avandia ® notification)
Public Reporting
Pediatric Immunizations (SIRS), Communicable Diseases, process measures and quality indicators (JCAHO, HEDIS, CMS, etc.)
EHR Data
Reports

8. Clinical Care Domain Clinical Research Domain
The Silos…
Clinical Care
Domain
EMR w/multiple interfaces
Aggregates ALL avail. data
Organized around Patients
HIPAA Policy
HL7 a common
Messaging standard
Non-Structured & Structured
Clinical Research
Domain
Independent Data
Organized around a Study
Well Behaved Data
Biostatisticians Manage
Drug Trials
21 CFR Part 11
Involves Sponsor and FDA
+/- CDISC Standards 8

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 9

10. Provides EMR-centric Resources for other groups
Quality & Patient
Safety
Institute
Quantitative
Health
Sciences
eAnalytics and Research:
Research Standardization
Center for Clinical Trials &
Clinical Operations
Clinical & Translational Science Consortium

11. Leveraging the EHR to capture critical data for researchers
Electronic
Health
Record
Personal Health Record
Clinical Events
Adverse Reactions
Patient Outcomes
Clarity
???
Dialysis
Center
Local
“EMR”s
Registries
Genomics
Imaging
Registries
eResearch Db 11

12. Clinical Research Cycle
Subject
Recruitment
Clinical Data
eCRF
Data Collection
Trial
Management
Site Selection
Regulated
Databases
eResearch
Services
Protocol
Assessment
Research
Billing
Protocol
Development
Data
Analysis
Research
Hypothesis
Knowledge
Dissemination 12

13. Multidisciplinary eResearch Team13

14. Guidance… 14

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 Naïve 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
Inclusion and Exclusion Study Criteria
Data Source
Demo
Vitals
Dx/Proc Rx
Lab
Chart
Diabetes Type 2 X
Age 18 to 65
Rx Naïve or mono-therapy
No Uncontrolled Hypertension
No Triglycerides >=1000
If being treated, stable dose of lipid-lowering agent
No Hx of MI/USA, CABG/PTCA
No Insulin use (except gest DM)
No history of substance abuse
Likely to complete study

17. Ontology & Vocabulary Information Standard Vocabulary Diagnoses
ICD9 Codes
IMO Terms
SNOMED Mapping (limited, via IMO)
Medications
First Data Bank / NDDF+
National Drug Code
RxNorm (limited, thru eResearch cross-walk)
Laboratory
Local Codes
LOINC Mapping (limited, thru eResearch cross-walk via RELMA tool)
Allergies
Local
Family History
Procedures
CPT™, ICD9 Procedure Codes

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

19. Assess Each Criteria for Ethnic/Gender Diversity

20. e Trial Support eResearch Tools Traditional Recruitment Strategies
Clinical
Data Repository
Electronic
Health
Record
eResearch
Tools
Traditional
Recruitment
Strategies
Research
Coordinator 20 20

21. 21

22. 22

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 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…
Site
Admin
Provider
Clinical
Trial Alert
Study Filter
(patient &
provider)
Study
Criteria
AUTOMATED
PROCESS
DURING THE
NIGHT
DURING THE
OFFICE VISIT
Patient Schedules
(sites)
Secure
EHR order
enrolls Pt
into Study
Patient
Tracked
via EHR 25

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 Apr;23(4): 27

28. Three recent outcomes and CER 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.
POTENTIAL SOLUTIONS
EHR data can be linked to SSDI assuming appropriate permissions
Text mining/analysis of progress notes, discharge summaries, operative reports can identify some events/adverse events
Widespread adoption of Medication-Reconciliation
Leveraging Health Information Exchanges or inclusion of payer data can mitigate the problems with fragmented care

30. eResearch Facilitated Studies…

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
Healthcare Cloud
Secure
high-speed VPN
OLAP
Comp
Matrix
Search
Engine
Alert
Population
Exploration
Comparative
Effectiveness
Business
Intelligence
Pharma-
covigilance
Open
Module
Questions:

33. “Not everything that can be counted counts, and not everything that counts can be counted.”
Sign hanging on Albert Einstein’s Princeton University Office
Questions?
Anil Jain, MD, FACP
Cleveland Clinic