Literature DB >> 31914471

Transforming French Electronic Health Records into the Observational Medical Outcome Partnership's Common Data Model: A Feasibility Study.

Antoine Lamer1, Nicolas Depas1, Matthieu Doutreligne2, Adrien Parrot3,4, David Verloop5, Marguerite-Marie Defebvre5, Grégoire Ficheur1, Emmanuel Chazard1, Jean-Baptiste Beuscart1.   

Abstract

BACKGROUND: Common data models (CDMs) enable data to be standardized, and facilitate data exchange, sharing, and storage, particularly when the data have been collected via distinct, heterogeneous systems. Moreover, CDMs provide tools for data quality assessment, integration into models, visualization, and analysis. The observational medical outcome partnership (OMOP) provides a CDM for organizing and standardizing databases. Common data models not only facilitate data integration but also (and especially for the OMOP model) extends the range of available statistical analyses.
OBJECTIVE: This study aimed to evaluate the feasibility of implementing French national electronic health records in the OMOP CDM.
METHODS: The OMOP's specifications were used to audit the source data, specify the transformation into the OMOP CDM, implement an extract-transform-load process to feed data from the French health care system into the OMOP CDM, and evaluate the final database.
RESULTS: Seventeen vocabularies corresponding to the French context were added to the OMOP CDM's concepts. Three French terminologies were automatically mapped to standardized vocabularies. We loaded nine tables from the OMOP CDM's "standardized clinical data" section, and three tables from the "standardized health system data" section. Outpatient and inpatient data from 38,730 individuals were integrated. The median (interquartile range) number of outpatient and inpatient stays per patient was 160 (19-364).
CONCLUSION: Our results demonstrated that data from the French national health care system can be integrated into the OMOP CDM. One of the main challenges was the use of international OMOP concepts to annotate data recorded in a French context. The use of local terminologies was an obstacle to conceptual mapping; with the exception of an adaptation of the International Classification of Diseases 10th Revision, the French health care system does not use international terminologies. It would be interesting to extend our present findings to the 65 million people registered in the French health care system. Georg Thieme Verlag KG Stuttgart · New York.

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Mesh:

Year:  2020        PMID: 31914471      PMCID: PMC6949163          DOI: 10.1055/s-0039-3402754

Source DB:  PubMed          Journal:  Appl Clin Inform        ISSN: 1869-0327            Impact factor:   2.342


  7 in total

1.  Transformation and Evaluation of the MIMIC Database in the OMOP Common Data Model: Development and Usability Study.

Authors:  Nicolas Paris; Adrien Parrot; Antoine Lamer
Journal:  JMIR Med Inform       Date:  2021-12-14

2.  Deep-learning-based automated terminology mapping in OMOP-CDM.

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

3.  Patient Cohort Identification on Time Series Data Using the OMOP Common Data Model.

Authors:  Christian Maier; Lorenz A Kapsner; Sebastian Mate; Hans-Ulrich Prokosch; Stefan Kraus
Journal:  Appl Clin Inform       Date:  2021-01-27       Impact factor: 2.342

4.  Big data analysis and artificial intelligence in epilepsy - common data model analysis and machine learning-based seizure detection and forecasting.

Authors:  Yoon Gi Chung; Yonghoon Jeon; Sooyoung Yoo; Hunmin Kim; Hee Hwang
Journal:  Clin Exp Pediatr       Date:  2021-11-26

5.  EHR-Independent Predictive Decision Support Architecture Based on OMOP.

Authors:  Philipp Unberath; Hans Ulrich Prokosch; Julian Gründner; Marcel Erpenbeck; Christian Maier; Jan Christoph
Journal:  Appl Clin Inform       Date:  2020-06-03       Impact factor: 2.342

6.  Extract, transform, load framework for the conversion of health databases to OMOP.

Authors:  Juan C Quiroz; Tim Chard; Zhisheng Sa; Angus Ritchie; Louisa Jorm; Blanca Gallego
Journal:  PLoS One       Date:  2022-04-11       Impact factor: 3.240

7.  Conversion of Automated 12-Lead Electrocardiogram Interpretations to OMOP CDM Vocabulary.

Authors:  Sunho Choi; Hyung Joon Joo; Yoojoong Kim; Jong-Ho Kim; Junhee Seok
Journal:  Appl Clin Inform       Date:  2022-09-21       Impact factor: 2.762

  7 in total

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