Secondary use of existing electronic health data from multiple healthcare organizations requires: • Harmonization of local data structure with a common data model. • Harmonization of local source values with a common vocabulary Centralized mapping of local source values allows standardization across organizations Data conforming to the OMOP CDM V4 can be used to operationalize observational CER studies. Implications for Policy, Delivery, or Practice Though EHRs all use different backend databases, they can be harmonized to a CDM for research purposes. We recommend that the EHR industry move toward having a standard data model so that the initial harmonization step is less cumbersome.

1. Operationalizing Asthma Analytic Plan using OMOP Common Data Model
Elias Brandt, BS, BA1; Bethany Kwan, PhD, MSPH2; Marion Sills, MD, MPH2,3, Barbara Yawn MD, MSc, FAAFP4, Monica Federico, MD3, Patrick
Hosokawa, MS2 and Lisa Schilling, MD, MSPH2
1AAFP National Research Network, 2University of Colorado School of Medicine, 3Children’s Hospital Colorado, 4Olmsted Medical Center
OBJECTIVE
• To operationalize an analytic plan designed to
model the association between practices’
medical home characteristics and asthma
control in children and adults using a database
of existing electronic health data structured
according to the OMOP V4 Common Data
Model.
OPERATIONALIZATION OF ASTHMA ANALYTIC VARIABLES
• Step 1: Define cohort of patients for extraction from standardized data
• Create list of subjects identified based on Age, Gender, Visits, and Diagnoses
• Simplified example for illustration:
• Step 2: Extract standardized data elements related to list of patients
• Simplified example for illustration:
CONCLUSION
• Secondary use of existing electronic health data from
multiple healthcare organizations requires:
• Harmonization of local data structure with a
common data model.
• Harmonization of local source values with a common
vocabulary
• Centralized mapping of local source values allows
standardization across organizations
• Data conforming to the OMOP CDM V4 can be used to
operationalize observational CER studies.
• Implications for Policy, Delivery, or Practice
• Though EHRs all use different backend databases,
they can be harmonized to a CDM for research
purposes. We recommend that the EHR industry
move toward having a standard data model so that
the initial harmonization step is less cumbersome.
SAFTINET ASTHMA
STUDY
• The Scalable Architecture for Federated
Translational Inquiries Network (SAFTINet) was
designed to federate electronic health data to
support quality improvement and comparative
effectiveness research (CER).
• Federated databases include existing
administrative, clinical (e.g., from electronic health
records; EHRs), Medicaid claims and enrollment
data, and patient-reported data collected during
routine clinical care, which have been harmonized
to the Observational Medical Outcomes
Partnership (OMOP) common data model (CDM)
Version 4.
• SAFTINet Asthma Study
• Prospective, longitudinal cohort study,
• Utilizing survey methodologies and secondary
use of structured Clinical, administrative, and
claims data.
• Population
• Adults and children with asthma cared for in
participating primary care practices in SAFTINet.
OMOP V4 COMMON DATA MODELTWO-STEP STANDARDIZATION OF
SOURCE DATA
• Source data includes:
• EHR administrative and clinical data
• Medicaid claims and enrollment data,
• Patient-reported data collected during routine clinical care
• Working with each partner individually ensured that their data could
conform to the OMOP V4 CDM and that the data required for the study was
available before extracting data for secondary use.
• Step 1: Structure of source data from each partner harmonized to the
(OMOP) CDM Version 4
• Step 2: Values in source data from each partner harmonized to the OMOP
V4 Vocabulary
Source Field Applied Rule Destination Field Data Type
PersonRowKey person_source_value String (50) / Required
‘CDW’ derived x_data_source_type String (20) / Required
NULL Not available medicaid_id_number String (50)
PersonYearOfBirth year_of_birth Number(4) / Required
Derived Derived from PersonDateOfBirth month_of_birth Number (2)
PersonDateOfBirth day_of_birth Number (2)
SexName gender_source_value String (50)
RaceName race_source_value String (50)
EthnicityName ethnicity_source_value String (50)
Partner Ethnicity Source Values Standardized Concept ID Standardized Concept Name
CINA_Cherokee E: Unknown / Not Reported 0 No matching concept
Denver Health UNKNOWN 0 No matching concept
CACHIE Not Reported 0 No matching concept
CINA_Cherokee E: Hispanic or Latino 38003563 Hispanic or Latino
Denver Health Hispanic 38003563 Hispanic or Latino
CACHIE Hispanic/Latino 38003563 Hispanic or Latino
CINA_Cherokee E: Not Hispanic or Latino 38003564 Not Hispanic or Latino
CACHIE Non-Hispanic (Other) 38003564 Not Hispanic or Latino
OMOP VOCABULARIES
OMOP Standard Vocabulary Used in CDM Field
SNOMED-CT condition_concept_id
RxNorm drug_concept_id
LOINC observation_concept_id
SNOMED-CT observation_concept_id
CMS Place of Service place_of_service_concept_id
CPT-4 procedure_concept_id
HCPCS procedure_concept_id
ICD-9-Procedure procedure_concept_id
CMS Specialty specialty_concept_id
CDC Race race_concept_id
Ethnicity ethnicity_concept_id
HL7Administrative Sex gender_concept_id
• Step 3: Transform extracted data elements into asthma variables
• Simplified example for illustration:
Asthma Analysis Plan Cohort Definition:
Child w/ asthma (Active patient):
Implementation using OMOP CDM 4:
Select subjects where: OMOP Table
Between 2 and 17 years as of 01-July-2012
year_of_birth > 1997 and year_of_birth < 2010
Or year_of_birth = 1997 and month_of_birth >= 7
Or year_of_birth = 2010 and month_of_birth < 7 x_demographic
Two diagnosis codes for asthma (493.XX) 6- year
period of available EHR data (01-Jan-2008 – 31-Dec-
2013)
condition_concept_id in (4141622, 4232595, 312950,
252341, 259055, 4119298, 252658, 261048, 254980,
256448, 256448, 256448, 256448, 443801, 313236,
4112831, 317009, 256716, 257581) AND
x_condition_update_date > 1/1/2008 condition_occurrence
Asthma variable domain: Asthma Variable Logic for Extraction Query
Pull records of patients identified in cohort from Person table
Select * from person where person_id in [cohort definition
query]
Pull selected records from observation
Select * from observation WHERE person_id in [cohort definition
query] AND observation_concept_id in [list of concept ids
related to ACT]
Subject Demographics
Asthma Control Test (Patient-
Reported Outcome)
pt_id
pt_child
pt_gender
pt_race
pt_ethn
pt_birthdate
pt_age
pt_id
act_date
act_score
act_control
Asthma Variable Logic for Operationalization from OMOP V4 CDM
pt_birthdate Concatenate month_of_birth, day_of_birth, and year_of_birth
pt_age Calculate age as of 7/1/12 using pt_birthdate
pt_child Set to "1" if pt_age < 18
asthma_dxcode
Set to "1" if condition_concept_id in ((4141622, 4232595, 312950, 252341,
259055, 4119298, 252658, 261048, 254980, 256448, 256448, 256448, 256448,
443801, 313236, 4112831, 317009, 256716, 257581)
exacerbation_type1
Oral steroids or IM/IV steroids exposure
Set to "1" if [drug_exposure].[drug_concept_id] in (list of concept ids for
oral steroids or IM/IV steroids)
exacerbation_type3
Administration of inhaled beta-agonist medication at an outpatient visit
Set to "1" if procedure_occurrence.procedure_concept_id in (list of
concept_ids for administration of beta-agonists) OR
(drug_exposure.drug_concept_id in (list of concept ids for beta agonists
AND drug_type_concept_id = 38000179)
exacerbation_type4
An asthma-related emergency department visit or hospitalization with
asthma listed as the primary or secondary diagnosis
Set to "1" if condition_concept_id in ((4141622, 4232595, 312950, 252341,
259055, 4119298, 252658, 261048, 254980, 256448, 256448, 256448, 256448,
443801, 313236, 4112831, 317009, 256716, 257581) AND
condition_type_concept_id in (38000215, 38000216) AND
visit.occurrence.visit_place_of_service = 8870