将基层医疗数据转化为观察性医疗结果合作组织通用数据模型:开发和可用性研究。

IF 3.1 3区 医学 Q2 MEDICAL INFORMATICS
Mathilde Fruchart, Paul Quindroit, Chloé Jacquemont, Jean-Baptiste Beuscart, Matthieu Calafiore, Antoine Lamer
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引用次数: 0

摘要

背景:患者监测软件会产生大量数据,这些数据可重复用于临床审计和科学研究。观察性健康数据科学与信息学(OHDSI)联盟开发了观察性医疗结果合作组织(OMOP)通用数据模型(CDM),以规范电子健康记录数据,促进大规模观察性和纵向研究:本研究旨在将基础医疗数据转换为 OMOP CDM 格式:我们从法国瓦特雷洛斯一家多学科医疗中心的电子健康记录中提取了基础医疗数据。我们在本地初级医疗数据库设计与 OMOP CDM 表和字段之间进行了结构映射。本地法文词汇表概念与 OHDSI 标准词汇表进行了映射。为了验证将基础医疗数据转换为 OMOP CDM 格式的实施情况,我们使用了一组查询。通过开发仪表板实现了实际应用:我们将 18,395 名患者的数据导入了 OMOP CDM,这些数据与 20 年间的 592,226 次问诊相对应。共实施了 18 个 OMOP CDM 表。共确定了 17 个与初级保健相关的本地词汇表,这些词汇表与患者特征(性别、地点、出生年份和种族)、测量单位、生物计量、实验室检测结果、病史和药物处方相对应。在语义映射过程中,10,221 个初级医疗概念被映射为标准的 OHDSI 概念。通过比较完成转换后获得的结果与源软件中获得的结果,使用了五个查询来验证 OMOP CDM。最后,开发了一个仪表盘原型,用于直观显示医疗中心的活动、实验室检测结果和药物处方数据:法国一家医疗机构的基础医疗数据已被转换成 OMOP CDM 格式。有关人口统计学、单位、测量和初级保健咨询步骤的数据已在 OHDSI 词汇表中提供。实验室检测结果和药物处方数据被映射到可用的词汇表中,并在最终模型中进行了结构化处理。仪表板应用程序为医护人员提供了有关其实践的反馈信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Transforming Primary Care Data Into the Observational Medical Outcomes Partnership Common Data Model: Development and Usability Study.

Background: Patient-monitoring software generates a large amount of data that can be reused for clinical audits and scientific research. The Observational Health Data Sciences and Informatics (OHDSI) consortium developed the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to standardize electronic health record data and promote large-scale observational and longitudinal research.

Objective: This study aimed to transform primary care data into the OMOP CDM format.

Methods: We extracted primary care data from electronic health records at a multidisciplinary health center in Wattrelos, France. We performed structural mapping between the design of our local primary care database and the OMOP CDM tables and fields. Local French vocabularies concepts were mapped to OHDSI standard vocabularies. To validate the implementation of primary care data into the OMOP CDM format, we applied a set of queries. A practical application was achieved through the development of a dashboard.

Results: Data from 18,395 patients were implemented into the OMOP CDM, corresponding to 592,226 consultations over a period of 20 years. A total of 18 OMOP CDM tables were implemented. A total of 17 local vocabularies were identified as being related to primary care and corresponded to patient characteristics (sex, location, year of birth, and race), units of measurement, biometric measures, laboratory test results, medical histories, and drug prescriptions. During semantic mapping, 10,221 primary care concepts were mapped to standard OHDSI concepts. Five queries were used to validate the OMOP CDM by comparing the results obtained after the completion of the transformations with the results obtained in the source software. Lastly, a prototype dashboard was developed to visualize the activity of the health center, the laboratory test results, and the drug prescription data.

Conclusions: Primary care data from a French health care facility have been implemented into the OMOP CDM format. Data concerning demographics, units, measurements, and primary care consultation steps were already available in OHDSI vocabularies. Laboratory test results and drug prescription data were mapped to available vocabularies and structured in the final model. A dashboard application provided health care professionals with feedback on their practice.

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来源期刊
JMIR Medical Informatics
JMIR Medical Informatics Medicine-Health Informatics
CiteScore
7.90
自引率
3.10%
发文量
173
审稿时长
12 weeks
期刊介绍: JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals. Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.
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