Kumagai Chihiro, Hoshimoto Hiroyuki, Komuro Masato, Watabe Daisuke, Sakurai Risa, Kitamura Shingo, Mitsui Seiji, Watanabe Hiroshi, Miyo Kengo, Ueki Kohjiro
Japan Health Research Promotion Bureau, Tokyo, Japan.
National Center for Global Health and Medicine, Tokyo, Japan.
Stud Health Technol Inform. 2025 Aug 7;329:169-173. doi: 10.3233/SHTI250823.
The 6NC-EHRs Database (6NC-EHRsDB) is based on clinical data output from electronic health records (EHRs) at six national centers. This study evaluated the reproducibility of the DB constructed in HL7 v2.5 to the HL7 FHIR® (FHIR). First, we clarified the correspondence between the 80 items collected in the 6NC-EHRsDB and each in FHIR. Next, FHIR resources (Condition, Medication Request, and Observation LabResult) extracted from EHRs were compared with data in 6NC-EHRsDB to evaluate the number of records, data item coverage, and the accuracy of data values were evaluated. 74 of the 80 items in 6NC-EHRsDB were successfully mapped to FHIR. The verification of coverage revealed that standard codes such as drug and disease names were not included in the data. In the records, 99.9% of Condition, 99.2% of Medication Request, and 99.9% of Observation LabResult were found to match after adjustment. Concerning the accuracy of data values, Medication Request had the lowest agreement rate of 69.1% for the prescription start date, and Observation LabResult had the lowest agreement rate of 58.9% for standard values. Based on the issues identified in this study, the reproducibility of DBs constructed with HL7 v2.5 using FHIR are expected to be improved. Our result will enhance data utilization and interoperability among medical information systems by eliminating deficiencies in implementing FHIR and enabling the assignment of accurate standard codes.
6NC电子健康记录数据库(6NC-EHRsDB)基于六个国家中心电子健康记录(EHRs)的临床数据输出。本研究评估了以HL7 v2.5构建的该数据库与HL7 FHIR®(FHIR)之间的再现性。首先,我们明确了6NC-EHRsDB中收集的80项内容与FHIR中各项内容的对应关系。接下来,将从EHRs中提取的FHIR资源(病情、用药申请和检验实验室结果)与6NC-EHRsDB中的数据进行比较,以评估记录数量、数据项覆盖范围,并评估数据值的准确性。6NC-EHRsDB的80项内容中有74项成功映射到了FHIR。覆盖范围验证发现数据中未包含药品和疾病名称等标准代码。在记录中,调整后发现99.9%的病情、99.2%的用药申请和99.9%的检验实验室结果相匹配。关于数据值的准确性,用药申请的处方开始日期一致率最低,为69.1%,检验实验室结果的标准值一致率最低,为58.9%。基于本研究中发现的问题,预计使用FHIR以HL7 v2.5构建的数据库的再现性将得到提高。我们的结果将通过消除实施FHIR中的缺陷并实现准确标准代码的分配,提高医疗信息系统之间的数据利用率和互操作性。