Hyerim Ji1, Seok Kim1, Soyoung Yi1, Hee Hwang2, Jeong-Whun Kim3, Sooyoung Yoo4. 1. Office of eHealth Research and Business, Seoul National University Bundang Hospital, Republic of Korea. 2. Office of eHealth Research and Business, Seoul National University Bundang Hospital, Republic of Korea; Department of Pediatrics, Seoul National University Bundang Hospital, Republic of Korea. 3. Department of Otorhinolaryngology, Seoul National University Bundang Hospital, Republic of Korea. 4. Office of eHealth Research and Business, Seoul National University Bundang Hospital, Republic of Korea. Electronic address: yoosoo0@snubh.org.
Abstract
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.
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.
Authors: Byungkon Kang; Jisang Yoon; Ha Young Kim; Sung Jin Jo; Yourim Lee; Hye Jin Kam Journal: J Am Med Inform Assoc Date: 2021-07-14 Impact factor: 4.497