Towards Implementation of OMOP in a German University Hospital Consortium

Maier, Christian; Lang, L.; Storf, Holger; Vormstein, Patric; Bieber, R.; Bernarding, Johannes; Herrmann, Tim; Haverkamp, Christian; Horki, Petar; Laufer, Julian; Berger, F.; Höning, Gunther; Fritsch, Hans-Walter; Schüttler, J.; Ganslandt, Thomas; Prokosch, Hans‐Ulrich; Sedlmayr, Martin

doi:10.1055/s-0037-1617452

Cited by 57 publications

(58 citation statements)

References 19 publications

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“…Several researchers have described the conversion of their data into the OMOP CDM format in various contexts. [13][14][15][16][17] However, to the best of our knowledge, the present study is the first to have attempted to replicate the OMOP's findings and to provide codes and specifications for French longitudinal health care data.…”

Section: Objectivesmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Objectivesmentioning

confidence: 99%

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

et al. 2020

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“…Therefore, we created a new vocabulary within the OMOP CDM using the HUGO Gene Nomenclature Committee (HGNC) 7 to enable the mapping of genomic data. For an in-depth overview of how to add custom vocabularies, refer to Maier et al 8 To retrieve the input data for the prediction model from the OMOP database, we decided to not access the database directly via SQL statements, but rather use an abstraction layer, that is, the REST WebAPI, built on top of the OMOP CDM. For its application within the OHDSI ATLAS tool, the existing REST GET path /person/id only retrieves stored observation and measurement concepts with their respective start and end date, but not their actual stored values.…”

Section: Methodsmentioning

confidence: 99%

EHR-Independent Predictive Decision Support Architecture Based on OMOP

et al. 2020

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Background The increasing availability of molecular and clinical data of cancer patients combined with novel machine learning techniques has the potential to enhance clinical decision support, example, for assessing a patient's relapse risk. While these prediction models often produce promising results, a deployment in clinical settings is rarely pursued. Objectives In this study, we demonstrate how prediction tools can be integrated generically into a clinical setting and provide an exemplary use case for predicting relapse risk in melanoma patients. Methods To make the decision support architecture independent of the electronic health record (EHR) and transferable to different hospital environments, it was based on the widely used Observational Medical Outcomes Partnership (OMOP) common data model (CDM) rather than on a proprietary EHR data structure. The usability of our exemplary implementation was evaluated by means of conducting user interviews including the thinking-aloud protocol and the system usability scale (SUS) questionnaire. Results An extract-transform-load process was developed to extract relevant clinical and molecular data from their original sources and map them to OMOP. Further, the OMOP WebAPI was adapted to retrieve all data for a single patient and transfer them into the decision support Web application for enabling physicians to easily consult the prediction service including monitoring of transferred data. The evaluation of the application resulted in a SUS score of 86.7. Conclusion This work proposes an EHR-independent means of integrating prediction models for deployment in clinical settings, utilizing the OMOP CDM. The usability evaluation revealed that the application is generally suitable for routine use while also illustrating small aspects for improvement.

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“…At analysis time, a researcher can choose to perform analysis on the source value, source concept ID, or standard concept ID, although the OHDSI recommended Best Practice is to use the standard because those represent a lingua franca across the international database network. This problem is less acute if research is done in a US-based claims network that relies only on ICD-9-CM (but now is challenged with making the transition to ICD-10-CM) but is particularly challenging when trying to facilitate research across claims data, EHRs, and registries in the United States, Europe, 4 and Asia-Pacific regions, 5,6 where there is much greater heterogeneity in source vocabularies and coding practices.…”

Section: To the Editormentioning

confidence: 99%