Transforming Primary Care Data Into the Observational Medical Outcomes Partnership Common Data Model: Development and Usability Study.

EHR Observational Medical Outcomes Partnership common data model dashboard data reuse data warehouse electronic health record patient monitoring patient tracking system primary care primary care data reproducible research

Journal

JMIR medical informatics
ISSN: 2291-9694
Titre abrégé: JMIR Med Inform
Pays: Canada
ID NLM: 101645109

Informations de publication

Date de publication:
13 Aug 2024
Historique:
received: 01 06 2023
revised: 11 04 2024
accepted: 11 04 2024
medline: 14 8 2024
pubmed: 14 8 2024
entrez: 14 8 2024
Statut: epublish

Résumé

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

Sections du résumé

Background UNASSIGNED
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 UNASSIGNED
This study aimed to transform primary care data into the OMOP CDM format.
Methods UNASSIGNED
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 UNASSIGNED
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 UNASSIGNED
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.

Identifiants

pubmed: 39140273
pii: v12i1e49542
doi: 10.2196/49542
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e49542

Informations de copyright

© Mathilde Fruchart, Paul Quindroit, Chloé Jacquemont, Jean-Baptiste Beuscart, Matthieu Calafiore, Antoine Lamer. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).

Auteurs

Mathilde Fruchart (M)

Univ Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des Technologies de santé et des, Pratiques médicales, 2 Place de Verdun, Lille, F-59000, France.

Paul Quindroit (P)

Univ Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des Technologies de santé et des, Pratiques médicales, 2 Place de Verdun, Lille, F-59000, France.

Chloé Jacquemont (C)

Département de Médecine Générale, University of Lille, Lille, France.

Jean-Baptiste Beuscart (JB)

Univ Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des Technologies de santé et des, Pratiques médicales, 2 Place de Verdun, Lille, F-59000, France.

Matthieu Calafiore (M)

Univ Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des Technologies de santé et des, Pratiques médicales, 2 Place de Verdun, Lille, F-59000, France.
Département de Médecine Générale, University of Lille, Lille, France.

Antoine Lamer (A)

Univ Lille, CHU Lille, ULR 2694 - METRICS: Évaluation des Technologies de santé et des, Pratiques médicales, 2 Place de Verdun, Lille, F-59000, France.
F2RSM Psy - Fédération régionale de recherche en psychiatrie et santé mentale Hauts-de-France, Saint-André-Lez-Lille, France.

Classifications MeSH