Anil Jain, MD, FACP – 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
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Anil Jain, MD, FACP – 1 Electronic Health Records Facilitating Clinical Research Clinical Research Management Workshop Tuesday, June 22, 2010 Anil Jain,
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Anil Jain, MD, FACP – 1
Electronic Health RecordsFacilitating Clinical Research
Clinical Research Management Workshop
Tuesday, June 22, 2010
Anil Jain, MD, FACP
Senior IT Executive, Information Technology, Cleveland ClinicManaging Director, eResearch, eCleveland Clinic
Co-Director, Biomedical Informatics, CTSC, Case Western Reserve University
Anil Jain, MD, FACP – 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
Anil Jain, MD, FACP – 3
The Cleveland Clinic The Cleveland Clinic
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The Cleveland Clinic The Cleveland Clinic • Founded in 1921
• Not-for-profitgroup practice
• 2096 Professional Staffand 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
(SIRS), Communicable Diseases, process measures and quality indicators (JCAHO, HEDIS, CMS, etc.)
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Clinical Care Domain
EMR w/multiple interfacesAggregates ALL avail. dataOrganized around Patients
HIPAA PolicyHL7 a common
Messaging standard
Non-Structured & Structured
Clinical ResearchDomain
Independent DataOrganized around a Study
Well Behaved DataBiostatisticians Manage
Drug Trials21 CFR Part 11
Involves Sponsor and FDA+/- CDISC Standards
The Silos…
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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
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Provides EMR-centric Resources for other groups
eAnalytics and
Research:Research
Standardization
eAnalytics and
Research:Research
Standardization
QuantitativeHealth
Sciences
QuantitativeHealth
Sciences
Quality & PatientSafety
Institute
Quality & PatientSafety
Institute
Center for Clinical Trials &
Clinical Operations
Center for Clinical Trials &
Clinical Operations
Clinical & Translational
Science Consortium
Clinical & Translational
Science Consortium
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Leveraging the EHR to capture critical data for researchers
ElectronicHealthRecord
Personal Health Record
Clinical EventsAdverse ReactionsPatient Outcomes
Clarity
eResearch Db
Registries
DialysisCenter
Local“EMR”s
Genomics
Imaging
Registries
???
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ResearchHypothesis
ProtocolDevelopment
Site Selection
SubjectRecruitment
Clinical Data
TrialManagement
DataAnalysis
KnowledgeDissemination
eResearchServices
ProtocolAssessment
Clinical Research Cycle
ResearchBilling
RegulatedDatabases
eCRFData Collection
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Systems Analysts
ClinicalInformatician
DatabaseAdministrator
ClinicalAnalysts
Programmers
Biostats/Epi*
Multidisciplinary eResearch Team
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ResearchInstitute
Biostats
HIPAA &Security
ResearchInformatics
eLearning& Training
GeneralCounsel
ClinicalResearch
Unit
CorporateCompliance
IRB
InternalAudit
eResearch
Guidance…
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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
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Analysis of Protocol Criteria
Inclusion and Exclusion Study Criteria
Data Source
Demo Vitals Dx/Proc Rx Lab Chart
Diabetes Type 2 X X
Age 18 to 65 X
Rx Naïve or mono-therapy X
No Uncontrolled Hypertension X
No Triglycerides >=1000 X
If being treated, stable dose of lipid-lowering agent
X
No Hx of MI/USA, CABG/PTCA X
No Insulin use (except gest DM) X X
No history of substance abuse X
Likely to complete study X
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Ontology & Vocabulary
Information Standard VocabularyDiagnoses • 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 • Local
Procedures • CPT™, ICD9 Procedure Codes
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High-level Summary of the Impact of Each Criterion
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Assess Each Criteria for Ethnic/Gender Diversity
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eResearchTools
ElectronicHealthRecord
Clinical Data Repository
ResearchCoordinator
Trial Support
TraditionalRecruitmentStrategies
e
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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.
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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
??
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AUTOMATEDPROCESS
DURING THENIGHT
DURING THEOFFICE VISIT
Integrating study criteria with scheduling…
StudyCriteriaStudy
Criteria
Study Filter(patient &provider)
Study Filter(patient &provider)
PatientSchedules
(sites)
PatientSchedules
(sites)
ProviderClinical
Trial Alert
ProviderClinical
Trial Alert
SiteAdmin
SiteAdmin
SecureEmail
SecureEmail
EHR orderenrolls Ptinto Study
EHR orderenrolls Ptinto Study
PatientTrackedvia EHR
PatientTrackedvia EHR
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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.
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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.
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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
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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