Transforming OMOP CDMv2 to CDMv4: Broadening CER Applicability
Michael G Kahn, MD,
1
PhD ;
4
Brandt ;
3
MS ;Daniella
5
PhD ;
6
PhD ;
Elias
Patrick Hosokawa,
Meeker,
Lola Ogunyemi,
Christian Reich, MD7; Lucila Ohno-Machado, MD, PhD8; Lisa Schilling, MD, MSPH2,3
Patrick Ryan,
7
PhD ;
Department of Pediatrics1, Department of Medicine2, Colorado Health Outcomes Program3, University of Colorado Anschutz Medical Campus, Aurora, CO; American Academy of Family
Physicians, Leawood, KS4;Rand Corporation, Santa Monica, CA5; Charles Drew University, Los Angeles, CA6; Observational Medical Outcomes Partnership, Foundation of the NIH7;
University of California San Diego, San Diego, CA8
ABSTRACT
The Scalable Architecture for Federated Translational
Inquiries Network (SAFTINet) and the Scalable National
Network for Effectiveness Research (SCANNER) are
AHRQ-funded projects tasked with constructing distributed
research network infrastructures to support comparative
effectiveness research. Both projects independently selected
the OMOP CDMv2 data model as the starting point for their
information model and collaborated closely with the OMOP
team to modify CDMv2 to the current CDMv4. The purpose of
the modifications is to support a broader array of comparative
effectiveness questions than OMOP’s original drug
surveillance outcome focus, including those that address the
impact of health care delivery system and insurance
characteristics. Using a SAFTINet research question as a
guide, important CDM revisions are highlighted.
Distributed Health Data Network
“…a system that allows secure remote analysis of
separate data sets, each derived from a different medical
organization’s or health plan’s records.” (2)
Benefits:
•
•
•
•
Data owners retain physical and logical control
Eliminates need to secure a central repository
Minimizes need to disclose PHI
Allows local data use/access logging
Scalable Architecture for Federated Translational
Inquiries Network (SAFTINet)
SAFTINet requirements:
• Develop a distributed data network that collects and links
data from multiple and different healthcare delivery settings
• Clinical data from electronic health records (EHR)
• Medicaid claims data
• Demonstrate capabilities for conducting methodologically
rigorous Comparative Effectiveness Research (CER)
Comparative Effectiveness Research (CER)
• CER is the generation and synthesis of evidence that
compares the benefits and harms of alternative methods to
prevent, diagnose, treat and monitor a clinical condition, or to
improve the delivery of care.
• The purpose of CER is to assist consumers, clinicians,
purchasers, and policy makers to make informed decisions that
will improve health care at both the individual and population
levels. (1)
Data Needs for CER
A SAFTINet CER “use case”:
• A prospective, observational comparative
effectiveness cohort study to determine the
effects of medical home delivery system
characteristics on the control of asthma in
adults and children.
DELIVERY SYSTEM
FACTORS
+
COVARIATES
→
Enhancing the OMOP common data model to meet the needs of CER
CER Data Examples and CDMv4 Storage Locations
OMOP common data model (CDMv2)
• A critical first step for SAFTINet was to select a
common data model that would support comparative
effectiveness research (CER)
• The lack of interoperability of EHR and other data
sources in a multi-institution distributed data
network is a challenge for CER and requires:
• A flexible common data model
• Mapping of idiosyncratic local codes/domains to
standardized terminologies
• Use of standardized terminologies for concept hierarchies &
intelligent queries
• The objective for this presentation is to show how
the OMOP CDM V2 was enhanced to meet broader
needs of CER
• The new components of the OMOP CDM V4 are highlighted
and linked to CER needs.
Specific data elements to be included in limited dataset :
Organization Information
(Organization Table)
Organization ID
Organization name
Care Site Information
(Care Site and Location Tables)
Care site ID
Care site name (for all encounters)
Care site type (for all encounters)
Care site location (zip code)
Provider Characteristics
(Provider Table)
Provider ID
Provider Type
Provider Specialty
Provider Home Clinic
OUTCOMES
(chronic disease
control)
CER data requirements:
1. Data to identify and track a cohort of patients with a
diagnosis of asthma
2. Data to characterize patients, practices, providers,
and aspects of health care delivery
3. Data pertaining to health outcomes, including health
care utilization, treatment, morbidity and mortality,
and patient-reported outcomes
4. Hierarchies and linkages among patients,
encounters, providers, practices and organizations
Cohort identification criteria:
1. Patients with at least two diagnosis codes for asthma
(493.XX) within any 18 month span in the prior 4
years
2. Childhood asthma: age 2-17 years inclusive
3. Adult asthma: >18 years
4. Exclusion criteria:
Other respiratory conditions: specific for adults and
children (not listed)
Characterization of systems, practices, providers
and patients
1. Patient and practice demographics
2. Domains representative of the delivery of organized
and coordinated care
Asthma Outcomes:
1. Poor asthma control as indicated by:
An asthma exacerbation in a 6-month period,
defined as:
• A prescription for oral steroids (as indicated
by clinical or administrative claims data), or
• A visit pattern indicative of an exacerbation:
3 asthma-related visits (outpatient)
occurring in 14 days or less OR asthmarelated ED visit or hospitalization
2. Patient-reported asthma control:
Asthma Control Test total score < 20 (poor control)
Administrative enrollment data
(Payer Plan Period Table)
Medication data (Drug Exposure
and Drug Era Tables)
OMOP common data model (CDMv4)
Person
Provider
Location
Care_site
Observation_period
Visit_occurrence
Requirements of a CER-supportive CDM:
The CDM must support the analytic availability of:
• Longitudinal patient-level data
• Identify/track cohorts of interest
• Socioeconomic/demographic data (race,
ethnicity)
• Link patients & outcomes by
• Place of Care and affiliated Organization
• Care Provider
• Geographical locations: patients, places of
care
• Link patients & outcomes to insurance/payer
coverage data
• Payers and coverage plan, benefits,
enrollment periods
• Link patients & outcomes to cost of care data
• Drugs, procedures, visits
Condition data (Condition
Occurrence and Condition Era
Tables)
Procedure data (Procedure
Occurrence Table)
Organization
Payer_plan_period
Drug_exposure
Drug_era
Drug_cost
Condition_occurrence
OBJECTIVES
Data Type (Source Table in
common data model)
Patient Demographic data
(Person and Location Tables)
Condition_era
Procedure_occurrence
Procedure_cost
Observation
• Health Outcomes of
Interest
• Drugs of Interest
• Interventions
Cohort
Death
Patient ID
Patient Gender
Patient Race/ethnicity
Patient Year of birth (all patients are under age 90)
Patient language of preference
Patient socioeconomic status (income level as Percent Family
Poverty level)
Zip code of residence
County of residence
Insurance type and benefit plan
Enrollment start and end dates
All medications for patients included in the sample, for the full
seven-year study period, including:
Medication code (e.g., NDC code)
Dose
Units
Dosing instructions
Quantity
Number of refills
Prescription date
Fulfillment date
Associated diagnosis code
Diagnostic codes from visits and problem lists
Diagnostic start and end dates
Associated provider
Associated visit
Procedure codes for procedures, including E&M, CPT, and
ICD-9 PC:
 Pulmonary function testing
 Spirometry
 Mechanical ventilation
 Continuous nebulized therapy
 Endotracheal intubation
 Critical care
Date procedure completed
Associated diagnosis codes
Results of procedure (if available)
Encounter ID
Encounter data (Visit Occurrence Encounter data for all primary care and specialty care
Table)
encounters, ED or urgent care visits, and hospitalizations,
including:
Encounter start date
Encounter end date
Encounter types
Vital signs and assessments
(Observation Table)
Standardized
Vocabulary
Condition data (Condition
Occurrence Table)
Cohort (Cohort table)
Death (Death Table)
Entity Relationship Diagram
Weight (including units)
Height (including units)
Height percentile (children)
Weight percentile (children)
Smoking status
Pulmonary function test type and results (e.g.,FEV1 from
spirometry)
Medical history
ACT total score
ACT item response value (items 1-6)
ACT completion date
C-ACT total score
C-ACT item response values (items 1-6)
Observation date
Condition data for all diagnoses (from encounters, problem
list and chief complaint fields, where available) including:
Diagnosis code
Condition start date
Condition end date
Asthma cohort
Date of death
DISCUSSION
Input from researchers, statisticians and analysts in conjunction
with several research question “use cases” was used to guide
CDM modifications to support broader CER applications.
This input was used by data modeling experts to make final CDM
revisions.
Several tables (Provider, Care Site, Organization) were added in
order to:
(1) Support assessments of delivery system characteristics and
multi-level modeling;
(2) Support economic analyses (procedure cost, drug cost, and
payer plan period),
(3) Improve the capture of mortality information (death), and
(4) Support prospective assessment (cohort)
REFERENCES
1) Institute of Medicine, Initial Priorities for Comparative
Effectiveness Research, June 2009.
www.iom.edu/Reports/2009/ComparativeEffectivenessRese
archPriorities.aspx
2) Judith C. Maro et al., “Design of a National Distributed Health
Data Network,”. Annals of Internal Medicine, Vol. 151, No. 5,
September 1, 2009
ACKNOWLEDGMENTS
This work was supported by the Agency for Healthcare
Research and Quality:
R01 HS019908 “Scalable Architecture for Federated
Translational Inquiries Network” (SAFTINet) PI: Lisa M.
Schilling, MD, MSPH\
R01 HS19913-01 SCANNER: Scalable National Network for
Effectiveness Research, PI: Lucila Ohno-Machado,
University of California San Diego