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Appl Clin Inform 2021; 12(01): 057-064
DOI: 10.1055/s-0040-1721481
Research Article

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

Christian Maier
1   Chair of Medical Informatics, Friedrich–Alexander–Universität Erlangen–Nürnberg (FAU), Erlangen, Bayern, Germany
,
Lorenz A. Kapsner
2   Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Bayern, Germany
,
Sebastian Mate
2   Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Bayern, Germany
,
Hans-Ulrich Prokosch
1   Chair of Medical Informatics, Friedrich–Alexander–Universität Erlangen–Nürnberg (FAU), Erlangen, Bayern, Germany
2   Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Bayern, Germany
,
Stefan Kraus
3   Department of Computer Science, Mannheim University of Applied Sciences, Mannheim, Baden-Württemberg, Germany
› Author Affiliations Funding This work was funded in part by the German Federal Ministry of Education and Research (BMBF) within the Medical Informatics Initiative (MIRACUM Consortium) under the Funding Number FKZ: 01ZZ1801A. The present work was performed in fulfillment of the requirements for obtaining the degree “Dr. rer. biol. hum.” from the Friedrich-Alexander-Universität Erlangen-Nürnberg (CM).
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Abstract

Background The identification of patient cohorts for recruiting patients into clinical trials requires an evaluation of study-specific inclusion and exclusion criteria. These criteria are specified depending on corresponding clinical facts. Some of these facts may not be present in the clinical source systems and need to be calculated either in advance or at cohort query runtime (so-called feasibility query).

Objectives We use the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) as the repository for our clinical data. However, Atlas, the graphical user interface of OMOP, does not offer the functionality to perform calculations on facts data. Therefore, we were in search for a different approach. The objective of this study is to investigate whether the Arden Syntax can be used for feasibility queries on the OMOP CDM to enable on-the-fly calculations at query runtime, to eliminate the need to precalculate data elements that are involved with researchers' criteria specification.

Methods We implemented a service that reads the facts from the OMOP repository and provides it in a form which an Arden Syntax Medical Logic Module (MLM) can process. Then, we implemented an MLM that applies the eligibility criteria to every patient data set and outputs the list of eligible cases (i.e., performs the feasibility query).

Results The study resulted in an MLM-based feasibility query that identifies cases of overventilation as an example of how an on-the-fly calculation can be realized. The algorithm is split into two MLMs to provide the reusability of the approach.

Conclusion We found that MLMs are a suitable technology for feasibility queries on the OMOP CDM. Our method of performing on-the-fly calculations can be employed with any OMOP instance and without touching existing infrastructure like the Extract, Transform and Load pipeline. Therefore, we think that it is a well-suited method to perform on-the-fly calculations on OMOP.

Keywords

information storage and retrieval - clinical data - electronic health records - Arden Syntax - Medical Logic Modules

Protection of Human and Animal Subjects

Only anonymized data was used. Therefore, the authors declare that the study was conducted in accordance with the ethical principles of the Helsinki Declaration.


Supplementary Material



Publication History

Received: 22 June 2020

Accepted: 04 November 2020

Article published online:
27 January 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

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