Appl Clin Inform 2015; 06(03): 536-547
DOI: 10.4338/ACI-2014-12-CR-0121
Case Report
Schattauer GmbH

Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership

F. FitzHenry
1   Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN
2   Department of Biomedical Informatics; Vanderbilt University, Nashville, TN
,
F.S. Resnic
3   Division of Cardiology, Brigham and Women’s Hospital, Boston, MA
,
S.L. Robbins
3   Division of Cardiology, Brigham and Women’s Hospital, Boston, MA
,
J. Denton
1   Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN
4   Division of General Internal Medicine and Public Health, Vanderbilt University, Nashville, TN
,
L. Nookala
1   Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN
4   Division of General Internal Medicine and Public Health, Vanderbilt University, Nashville, TN
,
D. Meeker
5   Department of Health, RAND Corporation, Santa Monica, CA
,
L. Ohno-Machado
6   Division of Biomedical Informatics, University of California, San Diego, CA
,
M.E. Matheny
1   Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN
2   Department of Biomedical Informatics; Vanderbilt University, Nashville, TN
4   Division of General Internal Medicine and Public Health, Vanderbilt University, Nashville, TN
7   Department of Biostatistics, Vanderbilt University, Nashville, TN
› Author Affiliations
Further Information

Publication History

received: 31 December 2014

accepted in revised form: 17 July 2015

Publication Date:
19 December 2017 (online)

Summary

Background: Adoption of a common data model across health systems is a key infrastructure requirement to allow large scale distributed comparative effectiveness analyses. There are a growing number of common data models (CDM), such as Mini-Sentinel, and the Observational Medical Outcomes Partnership (OMOP) CDMs.

Objective: In this case study, we describe the challenges and opportunities of a study specific use of the OMOP CDM by two health systems and describe three comparative effectiveness use cases developed from the CDM.

Methods: The project transformed two health system databases (using crosswalks provided) into the OMOP CDM. Cohorts were developed from the transformed CDMs for three comparative effectiveness use case examples. Administrative/billing, demographic, order history, medication, and laboratory were included in the CDM transformation and cohort development rules.

Results: Record counts per person month are presented for the eligible cohorts, highlighting differences between the civilian and federal datasets, e.g. the federal data set had more outpatient visits per person month (6.44 vs. 2.05 per person month). The count of medications per person month reflected the fact that one system‘s medications were extracted from orders while the other system had pharmacy fills and medication administration records. The federal system also had a higher prevalence of the conditions in all three use cases. Both systems required manual coding of some types of data to convert to the CDM.

Conclusion: The data transformation to the CDM was time consuming and resources required were substantial, beyond requirements for collecting native source data. The need to manually code subsets of data limited the conversion. However, once the native data was converted to the CDM, both systems were then able to use the same queries to identify cohorts. Thus, the CDM minimized the effort to develop cohorts and analyze the results across the sites.

FitzHenry F, Resnic FS, Robbins SL, Denton J, Nookala L, Meeker D, Ohno-Machado L, Matheny ME. A Case Report on Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership. Appl Clin Inform 2015; 6: 536–547

http://dx.doi.org/10.4338/ACI-2014-12-CR-0121