Transcript Cleveland Clinic and Research

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
Anil Jain, MD, FACP – 1
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
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The Cleveland Clinic
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The Cleveland Clinic
• Founded in 1921
• Not-for-profit
group practice
• 2096 Professional Staff
and scientists
•
• U.S.News & World Report
rankings:
♦ 4th among nation’s 6,000
hospitals
♦ Nation’s #1 heart program
♦ 16 specialties ranked
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 Anil Jain, MD, FACP – 4
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
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Cleveland Clinic EHR implementation Path
1999
2000
2002
2004
2006
2008
2010
HIE
OR IS
Billing & Reimbursement
Inpt Notes
Inpatient CPOE
ADT
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
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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 OrthoEvra®)
– Local Consensus (e.g.,
Avandia ® notification)
EHR Data
• Public Reporting
– Pediatric Immunizations
(SIRS), Communicable
Diseases, process measures
and quality indicators
(JCAHO, HEDIS, CMS, etc.)
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Reports
The Silos…
Clinical Care
Domain
Clinical Research
Domain
EMR w/multiple interfaces
Aggregates ALL avail. data
Organized around Patients
HIPAA Policy
HL7 a common
Messaging standard
Non-Structured & Structured
Independent Data
Organized around a Study
Well Behaved Data
Biostatisticians Manage
Drug Trials
21 CFR Part 11
Involves Sponsor and FDA
+/- CDISC Standards
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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
Quality & Patient
Safety
Institute
Quantitative
Health
Sciences
eAnalytics
and Research:
Research
Standardization
Clinical &
Translational
Science
Consortium
Center for
Clinical Trials &
Clinical
Operations
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Leveraging the EHR to capture critical data for researchers
Clinical Events
Adverse Reactions
Patient Outcomes
Electronic
Health
Record
Local
“EMR”s
Personal
Health Record
Clarity
???
Dialysis
Center
Registries
Genomics
Imaging
Registries
eResearch Db
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Clinical Research Cycle
Subject
Recruitment
Clinical Data
Trial
Management
Site Selection
eCRF
Data Collection
Regulated
Databases
Protocol
Assessment
eResearch
Services
Protocol
Development
Research
Hypothesis
Research
Billing
Data
Analysis
Knowledge
Dissemination
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Multidisciplinary eResearch Team
Systems Analysts
Biostats/Epi*
Clinical
Informatician
Programmers
Database
Administrator
Clinical
Analysts
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Guidance…
Research
Institute
Internal
Audit
Biostats
HIPAA &
Security
IRB
eResearch
Research
Informatics
Corporate
Compliance
Clinical
Research
Unit
General
Counsel
eLearning
& Training
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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
Diabetes Type 2
Age 18 to 65
Dx/Proc
Rx
X
X
Chart
X
Rx Naïve or mono-therapy
No Uncontrolled Hypertension
Lab
X
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 Vocabulary
Diagnoses
• ICD9 Codes
• IMO Terms
• SNOMED Mapping (limited, via IMO)
Medications
• First Data Bank / NDDF+
• National Drug Code
• RxNorm (limited, thru eResearch crosswalk)
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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eTrial Support
Traditional
Recruitment
Strategies
eResearch
Tools
Research
Coordinator
Electronic
Health
Record
Clinical
Data Repository
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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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Integrating study criteria with scheduling…
Study Filter
(patient &
provider)
Site
Admin
Study
Criteria
AUTOMATED
PROCESS
DURING THE
NIGHT
Patient
Schedules
(sites)
Provider
Clinical
Trial Alert
DURING THE
OFFICE VISIT
Secure
Email
EHR order
enrolls Pt
into Study
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Patient
Tracked
via EHR
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
POTENTIAL SOLUTIONS
• Death is not always reliably
• EHR data can be linked to SSDI
• Documentation of certain
• Text mining/analysis of progress
• Prescription medication
• Widespread adoption of
captured in an EHR derived data
set.
exclusions and adverse events
are generally not captured as
structured data
dispensed and taken including
OTC
• Patients may have fragmented
care with some clinical data
outside institutional EHR.
assuming appropriate
permissions
notes, discharge summaries,
operative reports can identify
some events/adverse events
Medication-Reconciliation
• Leveraging Health Information
Exchanges or inclusion of payer
data can mitigate the problems
with fragmented care
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eResearch Facilitated Studies…
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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.
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http://www.explorys.net/
The Explorys Unified Platform
Explorys
Healthcare Cloud
Secure
high-speed VPN
Search
Engine
Comp
Matrix
OLAP
Alert
Engine
Open
Module
Open
Module
Open
Module
Open
Module
Population
Exploration
Comparative
Effectiveness
Business
Intelligence
Pharmacovigilance
Questions: [email protected]
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“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
[email protected]
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