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Healthc Inform Res. 2016 Jan;22(1):54-8. doi: 10.4258/hir.2016.22.1.54. Epub 2016 Jan 31.

Conversion and Data Quality Assessment of Electronic Health Record Data at a Korean Tertiary Teaching Hospital to a Common Data Model for Distributed Network Research.

Author information

1
Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea.; Observational Health Data Sciences and Informatics, New York, NY, USA.
2
Observational Health Data Sciences and Informatics, New York, NY, USA.; Department of Nursing Science, Dongyang University, Yeongju, Korea.
3
Observational Health Data Sciences and Informatics, New York, NY, USA.; Mibyeong Research Center, Korea Institute of Oriental Medicine, Daejeon, Korea.
4
Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea.
5
Observational Health Data Sciences and Informatics, New York, NY, USA.; Global Epidemiology, Janssen Research and Development LLC, Titusville, NJ, USA.

Abstract

OBJECTIVES:

A distributed research network (DRN) has the advantages of improved statistical power, and it can reveal more significant relationships by increasing sample size. However, differences in data structure constitute a major barrier to integrating data among DRN partners. We describe our experience converting Electronic Health Records (EHR) to the Observational Health Data Sciences and Informatics (OHDSI) Common Data Model (CDM).

METHODS:

We transformed the EHR of a hospital into Observational Medical Outcomes Partnership (OMOP) CDM ver. 4.0 used in OHDSI. All EHR codes were mapped and converted into the standard vocabulary of the CDM. All data required by the CDM were extracted, transformed, and loaded (ETL) into the CDM structure. To validate and improve the quality of the transformed dataset, the open-source data characterization program ACHILLES was run on the converted data.

RESULTS:

Patient, drug, condition, procedure, and visit data from 2.07 million patients who visited the subject hospital from July 1994 to November 2014 were transformed into the CDM. The transformed dataset was named the AUSOM. ACHILLES revealed 36 errors and 13 warnings in the AUSOM. We reviewed and corrected 28 errors. The summarized results of the AUSOM processed with ACHILLES are available at http://ami.ajou.ac.kr:8080/.

CONCLUSIONS:

We successfully converted our EHRs to a CDM and were able to participate as a data partner in an international DRN. Converting local records in this manner will provide various opportunities for researchers and data holders.

KEYWORDS:

Clinical Coding; Common Data Model; Electronic Health Records; Epidemiologic Methods; Observational Health Data Sciences and Informatics (OHDSI)

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