Converting clinical document architecture documents to the common data model for incorporating health information exchange data in observational health studies: CDA to CDM
Open accessAbstract
Background Utilization of standard health information exchange (HIE) data is growing due to the high adoption rate and interoperability of electronic health record (EHR) systems. However, integration of HIE data into an EHR system is not yet fully adopted in clinical research. In addition, data quality should be verified for the secondary use of these data. Thus, the aims of this study were to convert referral documents in a Health Level 7 (HL7) clinical document architecture (CDA) to the common data model (CDM) to facilitate HIE data availability for longitudinal data analysis, and to identify data quality levels for application in future clinical studies.Methods A total of 21,492 referral CDA documents accumulated for over 10 years in a tertiary general hospital in South Korea were analyzed. To convert CDA documents to the Observational Medical Outcomes Partnership (OMOP) CDM, processes such as CDA parsing, data cleaning, standard vocabulary mapping, CDA-to-CDM mapping, and CDM conversion were performed. The quality of CDM data was then evaluated using the Achilles Heel and visualized with the Achilles tool.Results Mapping five CDA elements (document header, problem, medication, laboratory, and procedure) into an OMOP CDM table resulted in population of 9 CDM tables (person, visit_occurrence, condition_occurrence, drug_exposure, measurement, observation, procedure_occurrence, care_site, and provider). Three CDM tables (drug_era, condition_era, and observation_period) were derived from the converted table. From vocabulary mapping codes in CDA documents according to domain, 98.6% of conditions, 68.8% of drugs, 35.7% of measurements, 100% of observation, and 56.4% of procedures were mapped as standard concepts. The conversion rates of the CDA to the OMOP CDM were 96.3% for conditions, 97.2% for drug exposure, 98.1% for procedure occurrence, 55.1% for measurements, and 100% for observation.Conclusions We examined the possibility of CDM conversion by defining mapping rules for CDA-to-CDM conversion using the referral CDA documents collected from clinics in actual medical practice. Although mapping standard vocabulary for CDM conversion requires further improvement, the conversion could facilitate further research on the usage patterns of medical resources and referral patterns.
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CDA is an XML-based standard for clinical document content that is flexible and can be read by both humans and machines.
“The reason CDA is so popular is that it is an XML-based standard for clinical document content that is flexible and can be read by both humans and machines.”
From Interoperability of heterogeneous health information systems: a systematic literature review (2023) · cites this paper as [29]
CDA allows the entire patient medical history to be displayed in one document, reusable in several applications, and eliminates content diversity.
“It allows the entire patient medical history to be displayed in one document, reusable in several applications, eliminates content diversity [29].”
From Interoperability of heterogeneous health information systems: a systematic literature review (2023) · cites this paper as [29]
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- Authors
- Ji, H., Kim, S., Yi, S., Hwang, H., Kim, J.-W., & Yoo, S.
- Affiliations
- Seoul National University Bundang Hospital (KR)
- Corresponding author
- Yoo, Sooyoung
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 107
- Pages
- 103459
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W3030643620
- Cited by
- 29
- References
- 13
- Citations by year
- 2025: 4 · 2024: 8 · 2023: 9 · 2022: 4 · 2021: 2 · 2020: 2
- FWCI
- 3.72
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- SNUBH Research Fund
- MOTIE
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- common data model, OMOP, health information exchange, vocabulary mapping, data quality, electronic health records, secondary use of health data
- MeSH terms
- Humans, Referral and Consultation, Republic of Korea, Electronic Health Records, Health Information Exchange
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