Appl Clin Inform 2020; 11(03): 399-404
DOI: 10.1055/s-0040-1710393
Research Article
Georg Thieme Verlag KG Stuttgart · New York

EHR-Independent Predictive Decision Support Architecture Based on OMOP

Philipp Unberath
1  Department of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
,
Hans Ulrich Prokosch
1  Department of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
,
Julian Gründner
1  Department of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
,
Marcel Erpenbeck
1  Department of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
,
Christian Maier
1  Department of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
,
Jan Christoph
1  Department of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
› Institutsangaben
Funding This research has been conducted within the MelEVIR project. MelEVIR is funded by the German Federal Ministry of Education and Research (BMBF) under the Funding Number FKZ 031L0073A.
Weitere Informationen

Publikationsverlauf

10. Februar 2020

06. April 2020

Publikationsdatum:
03. Juni 2020 (online)

Abstract

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.

Note

The present work was performed in (partial) fulfillment of the requirements for obtaining the degree “Dr. rer. biol. hum.” from the Friedrich-Alexander-Universität Erlangen-Nürnberg (P.U.).


Protection of Human and Animal Subjects

Ethical approval was not required.