Methods Inf Med 2015; 54(01): 65-74
DOI: 10.3414/ME13-02-0019
Focus Theme – Original Articles
Schattauer GmbH

Harmonization of Detailed Clinical Models with Clinical Study Data Standards

G. Jiang
1   Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA
,
J. Evans
2   Clinical Data Interchange Standards Consortium (CDISC), Austin, Texas, USA
,
T. A. Oniki
3   Intermountain Medical Center, Intermountain Healthcare, Murray, Utah, USA
,
J. F. Coyle
3   Intermountain Medical Center, Intermountain Healthcare, Murray, Utah, USA
,
L. Bain
2   Clinical Data Interchange Standards Consortium (CDISC), Austin, Texas, USA
,
S. M. Huff
3   Intermountain Medical Center, Intermountain Healthcare, Murray, Utah, USA
,
R. D. Kush
2   Clinical Data Interchange Standards Consortium (CDISC), Austin, Texas, USA
,
C. G. Chute
1   Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota, USA
› Author Affiliations
Further Information

Publication History

received: 07 June 2013

accepted: 23 April 2014

Publication Date:
22 January 2018 (online)

Summary

Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on “Managing Interoperability and Complexity in Health Systems”.

Background: Data sharing and integration between the clinical research data management system and the electronic health record system remains a challenging issue. To approach the issue, there is emerging interest in utilizing the Detailed Clinical Model (DCM) approach across a variety of contexts. The Intermountain Healthcare Clinical Element Models (CEMs) have been adopted by the Office of the National Coordinator awarded Strategic Health IT Advanced Research Projects for normalization (SHARPn) project for normalizing patient data from the electronic health records (EHR).

Objective: The objective of the present study is to describe our preliminary efforts toward harmonization of the SHARPn CEMs with CDISC (Clinical Data Interchange Standards Consortium) clinical study data standards.

Methods: We were focused on three generic domains: demographics, lab tests, and medications. We performed a panel review on each data element extracted from the CDISC templates and SHARPn CEMs.

Results: We have identified a set of data elements that are common to the context of both clinical study and broad secondary use of EHR data and discussed outstanding harmonization issues.

Conclusions: We consider that the outcomes would be useful for defining new requirements for the DCM modeling community and ultimately facilitating the semantic interoper-ability between systems for both clinical study and broad secondary use domains.