Healthc Inform Res.  2016 Jan;22(1):54-58. 10.4258/hir.2016.22.1.54.

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

• Yoon D
• Ahn EK
• Park MY
• Cho SY
• Ryan P
• Schuemie M
• Shin D
• Park H
• Park RW

• KMID: 2166917
• DOI: http://doi.org/10.4258/hir.2016.22.1.54

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.

Keyword

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

MeSH Terms

Clinical Coding
Data Accuracy*
Dataset
Electronic Health Records*
Epidemiologic Methods
Hospitals, Teaching*
Humans
Informatics
Sample Size
Vocabulary

Figure

• 

  Figure 1 AUSOM data visualized using ACHILLES (Automated Characterization of Health Information at Large-scale Longitudinal Evidence Systems). (A) Basic information about the population in the database is shown in the dashboard tab. (B, C) The prevalence and number of records per person are shown using the size and color of the boxes in the tree maps at the tops of the following tabs: Conditions, Condition Eras, Observations, Drug Eras, Drug Exposures, Procedures, and Visits. Trends and related information for the selected box in the tree map at each tab are visualized below the tree map. The data for (B) essential hypertension and (C) the serum and plasma cholesterol levels are shown (doughnut chart at the bottom right; blue, above the reference range; orange, below the reference range; green, within reference range). The website is available at http://ami.ajou.ac.kr:8080/.

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