Transcript Clinical Research Management System

Using EMR Data for Population Registries
Diana Gumas, JHMCIS Senior Director for Research
Systems, EPR and EPR2020/Amalga
David Thiemann, Center for Clinical Data Analysis
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Potential Data Uses
• Sample Size Estimates (aggregate data without IRB approval)
– Feasibility, grant applications, statistical planning
• Identifying patients for enrollment/recruitment
– By diagnosis, pathology, stage, labs, meds
• Identifying/creating matched study controls
• Obtaining current demographics (name, address) for mail
solicitation
– From research list or by clinic, provider, clinical criteria
• Obtaining ongoing clinical + administrative data on a registry
panel
– Labs, visits, procedures, immunizations, CPT/ICD9 codes,
resource use
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Possible research data sources
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EPR (JHH & JHBMC)
Sunrise Clinical Manager (JHH – inpatient)
Meditech (Bayview)
Casemix Datamart
GE Centricity (JHCP)
EPR2020
Departmental Systems (ED, OR, Anesthesia)
Clinical Research Management System (CRMS)
IDX (professional fees)
Death Registry
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Methods for Data Access
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Historical: Researcher Negotiates Access With
Clinical System /DBA
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Logistic nightmare, technical challenge
Clinical Research Management System (CRMS)
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Study cohort with real-time links to enterprise data
Center for Clinical Data Analysis
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Monthly/quarterly data extracts from designated systems
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Clinical Research Management System
(CRMS)
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1,054 Users
1079 Active Studies
25,430 Participants
Data Available in CRMS
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eIRB
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EPR (patient demographics)
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Study participants / accruals
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Electronic Case Report Forms - in next 2-3 months
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Clinical Research Management System
(CRMS)
Ways to extract data
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Canned Reports (click for examples)
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Ad-hoc querying using SQL
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Possible with CCDA support - automated study-specific
data extracts
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EPR2020 Data for Researchers
4.2M Patients, 23.4M Visits
12.3M Documents, 6.8M Radiology Reports
25.6M Lab Results
1.5M Problems, 2.2M Medications, 140K Allergies
Planned
• Bayview & JHCP data
• ICD9 diagnosis codes and CPT charges (IDX)
Future
• Death Registry
• Blood Product Data for Transfusions
• Eclipsys SCM Order data
• HMED (ED), ORMIS, eADR/Medivision
From
EPR
Today
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My Participant’s Lab Data
Reliable.
Driven by the CRMS Participant Registry.
Exportable.
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Registry Cohort Discovery using
EPR2020
A JHM investigator wants to find and enroll diabetic patients
aged 45-65 years
with hemoglobin A1C between 7 and 9%
serum creatinine < 2 mg/dl
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Center for Clinical Data Analysis (CCDA)
Provides periodic (monthly/quarterly) bulk data extracts
(delimited/flat files, .xls):
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Preliminary, anonymous data for feasibility, grant
applications and statistical sample-size estimates
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IRB-approved case-finding--for study enrollment
(mailings, phone solicitation), chart review, and
cohort/case-control studies
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Research data extracts - monthly/quarterly integrated
extracts from EPR, POE, ORMIS, lab/PDS, billing
systems, vaccination/transfusion/culture data, etc.
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How CCDA works
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Email [email protected], cc: [email protected];
phone 410-955-65558 (Thiemann)
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For IRB-approved research:
– Provide full protocol + IRB approval
– Meet to discuss query methods, format
– Iterate, then schedule prod (email extracts, Jshare)
– Cost: $100/hour
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For non-IRB projects (exploratory analyses, QI)
– Same process, cost subsidized by ICTR/JHM
– Do NOT implicitly morph QI into IRB
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The Basics: Getting Clinical Data
Into a Registry Database
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Real work, not ad hoc/bootstrap
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Need $$$ and FTE(s)
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Smart analyst(s) who know database technology
and understand (or can learn) nuances of the
sources and content domain
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Hands-on PI management/guidance
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Statistical liason early, before database schema and
ETL methods are set in stone
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The Extract-Transform-Load process:
Getting Clinical Data into Research DB
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Raw clinical/administrative data is useless for
research
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Build an intermediate (staging) database
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Don’t do data management in SAS/Stata/Excel
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Data dictionary—derivation for each field
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Templated, tested, documented cleanup
scripts/routines.
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Intermediate tables: Log each step/modification
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For each batch, be able to re-create data transform from scratch
Version control, change control and documentation are vital
Build data versioning into the database
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Transforming Data
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Raw data typically string (char/text) fields
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Unanalyzable characters (* < >, comments) still have
meaning
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~3% of pts have multiple/non-preferred MRNs
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Need 1-to-many link table
Assays/reference ranges/coding changes
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Put non-numeric data in separate field. Avoid numerical
recoding (999)
Avoid using raw codes (CPT/ICD) in research db
Map clinical codes to research terms
Defer analytic assumptions. When recoding data,
anticipate problems. Keep options open.
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More Data Transform Challenges
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NEVER trust raw data. Learn business logic of
source system.
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CPTs morph annually, internal complexity/redundancy
Lab assays/reference/terms change
Parsing is inherently unreliable
Administrative names/groups change (clinic #s, departments).
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Duplicate-value problems (labs, orders)
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System-attribution source/datetime (POE, lab)
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Always run an aggregate (“group by” ) query to
identify alternative names (eg lab name) and values
(number, result) before transform. Otherwise you’ll
miss something
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Understanding Business Logic
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Trust but verify: Test coding accuracy
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Run min/max queries, aggregates, outer joins
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Providers may habitually use imprecise/inaccurate diagnosis
codes (especially in profee data)
ICD9 procedure indications often a billing fiction
Trained coders may make systematic errors
Different content domains may have different standards (inpt vs
outpt coders)
Don’t infer/assume dependencies unless enforced by source
system.
Confirm date ranges, data ranges, relative proportions by year
Don’t assume that null rows actually are empty.
Maybe the query missed something
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JHM Clinical Data Landscape:
Past, Present and Future
Past : Babble of unintegrated systems
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EPR (antiquated technology, VSAM files, DB2)
contains text, not queryable, analyzable data
Present: EPR2020 (aka Amalga) –integrated data!!
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Has everything in EPR, plus JHCP, plus gradually
adding data from clinical/departmental/administative
systems (IDX CPTs, transfusion medicine, ORMIS,
HMED, eADR, death registry, ad infinitum)
Future: ? Epic, ? JHM Data Warehouse
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Epic: One system replacing all major JHM systems
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JHH timeline: 4+ years
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JHM Data Sources: Casemix Datamart
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Gold standard for JHM (non-profee) administrative
data, including payer/insurance data
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Combines data from Keane (hospital charges), ADT
(admission/discharge/transfer), HDM (ICD9
diagnosis + procedure coding), HSCRC (regulatory
submissions)
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Not a true data warehouse; meager reconciliation
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Best source for length of stay, resource use, ICD9
diagnoses
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Outpatient ICD9s limited
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Has JHH + BMC + HCGH data
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JHM Data Sources: IDX (profee)
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Gold standard for inpatient +outpatient CPT (profee
charge) data
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ICD9 diagnosis data problematic
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Limitation: No data from non-faculty providers
(private physicians, etc.)
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Difficult to query. Has a data warehouse, limited
access.
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Early target for EPR2020/Amalga integration.
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JHH Data Sources: SCM/POE
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Sunrise Clinical Manager/Provider Order Entry
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Replicated transactional database, difficult to query
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For registry purposes POE has large
attribution/process challenges: Stutter-step orders,
multiple alerts, imputed times
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Great source for inpatient meds, labs, physiologic
monitor data
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No codified ICD9/Snomed/RxNorm data
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No outpatient data
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JHH Data Sources: SCC/AIM
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Sunrise Critical Care (aka Emtek, Eclipsys). JHH
ICUs + stepdown units + oncology
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AIM analytic database contains selected but
comprehensive batch extract
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Sunsets as ICUs switch to POE ClinDoc
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Challenging to query. Lots of denormalized fields
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JHH + BMC Data Sources: PDS
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PDS=Pathology Data Systems
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Includes lab, transfusion medicine, anatomic
pathology, cytopath, John Boitnott’s death registry
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Lab data also available via EPR2020/Amalga and
POE
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BMC Data Sources: Meditech
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Shrink-wrapped, comprehensive inpatient +
outpatient clinical + financial system
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Difficult for ad hoc research queries.
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Exports data to Datamart and EPR2020
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BMC-JHH patient linkage doable but difficult, needs
caution
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JHCP Data Sources: GE Centricity
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All clinical + administrative data for JHCP clinics
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Largely opaque to research query; JHCP sometimes
collaborates directly, especially for its
physician/investigators
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Early target for EPR2020/Amalga integration
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Linkage challenges to BMC and JHH mrns
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JHH Departmental Data:
ORMIS + eADR/Medivision
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ORMIS: Operating Room Management Information
System
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Mostly transactional
scheduling/tracking/administrative data, limited
clinical data.
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Has diagnoses, procedures, case start/stop times
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eADR/Medivision (anesthesia) still evolving, limited
research data access
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Design challenges similar to legacy SCC criticalcare system.
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JHH Departmental Data:
HMED (Emergency Department)
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Mostly opaque to research
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Replicated data hosted by Datamart
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