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

Maier C, Kapsner L, Mate S, Prokosch HU, Kraus S (2021)

Publication Type: Journal article

Publication year: 2021


Book Volume: 12

Pages Range: 57-64

Journal Issue: 1

DOI: 10.1055/s-0040-1721481


Background â 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 â 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 â 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 â 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 â 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.

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Maier, C., Kapsner, L., Mate, S., Prokosch, H.-U., & Kraus, S. (2021). Patient Cohort Identification on Time Series Data Using the OMOP Common Data Model. Applied Clinical Informatics, 12(1), 57-64.


Maier, Christian, et al. "Patient Cohort Identification on Time Series Data Using the OMOP Common Data Model." Applied Clinical Informatics 12.1 (2021): 57-64.

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