TY - JOUR
T1 - Toward cross-platform electronic health record-driven phenotyping using Clinical Quality Language
AU - Brandt, Pascal S.
AU - Kiefer, Richard C.
AU - Pacheco, Jennifer A.
AU - Adekkanattu, Prakash
AU - Sholle, Evan T.
AU - Ahmad, Faraz S.
AU - Xu, Jie
AU - Xu, Zhenxing
AU - Ancker, Jessica S.
AU - Wang, Fei
AU - Luo, Yuan
AU - Jiang, Guoqian
AU - Pathak, Jyotishman
AU - Rasmussen, Luke V.
N1 - Funding Information:
This research is funded in part by NIH grant R01GM105688. P. S. B. is funded by the Fulbright Foreign Student Program and the South African National Research Foundation. We would also like to acknowledge the work done by the CQL language and library authors Bryn Rhodes, Chris Moesel, and Christopher Schuler, and the Circe authors Chris Knoll and Pavel Grafkin. Additionally, we would like to thank the OMOP on FHIR author Myung Choi and HAPI FHIR author James Agnew for their support on GitHub and Slack, respectively.
Funding Information:
This research is funded in part by NIH grant R01GM105688. P. S. B. is funded by the Fulbright Foreign Student Program and the South African National Research Foundation. We would also like to acknowledge the work done by the CQL language and library authors Bryn Rhodes, Chris Moesel, and Christopher Schuler, and the Circe authors Chris Knoll and Pavel Grafkin. Additionally, we would like to thank the OMOP on FHIR author Myung Choi and HAPI FHIR author James Agnew for their support on GitHub and Slack, respectively.
Publisher Copyright:
© 2020 The Authors. Learning Health Systems published by Wiley Periodicals LLC on behalf of the University of Michigan.
PY - 2020/10/1
Y1 - 2020/10/1
N2 - Introduction: Electronic health record (EHR)-driven phenotyping is a critical first step in generating biomedical knowledge from EHR data. Despite recent progress, current phenotyping approaches are manual, time-consuming, error-prone, and platform-specific. This results in duplication of effort and highly variable results across systems and institutions, and is not scalable or portable. In this work, we investigate how the nascent Clinical Quality Language (CQL) can address these issues and enable high-throughput, cross-platform phenotyping. Methods: We selected a clinically validated heart failure (HF) phenotype definition and translated it into CQL, then developed a CQL execution engine to integrate with the Observational Health Data Sciences and Informatics (OHDSI) platform. We executed the phenotype definition at two large academic medical centers, Northwestern Medicine and Weill Cornell Medicine, and conducted results verification (n = 100) to determine precision and recall. We additionally executed the same phenotype definition against two different data platforms, OHDSI and Fast Healthcare Interoperability Resources (FHIR), using the same underlying dataset and compared the results. Results: CQL is expressive enough to represent the HF phenotype definition, including Boolean and aggregate operators, and temporal relationships between data elements. The language design also enabled the implementation of a custom execution engine with relative ease, and results verification at both sites revealed that precision and recall were both 100%. Cross-platform execution resulted in identical patient cohorts generated by both data platforms. Conclusions: CQL supports the representation of arbitrarily complex phenotype definitions, and our execution engine implementation demonstrated cross-platform execution against two widely used clinical data platforms. The language thus has the potential to help address current limitations with portability in EHR-driven phenotyping and scale in learning health systems.
AB - Introduction: Electronic health record (EHR)-driven phenotyping is a critical first step in generating biomedical knowledge from EHR data. Despite recent progress, current phenotyping approaches are manual, time-consuming, error-prone, and platform-specific. This results in duplication of effort and highly variable results across systems and institutions, and is not scalable or portable. In this work, we investigate how the nascent Clinical Quality Language (CQL) can address these issues and enable high-throughput, cross-platform phenotyping. Methods: We selected a clinically validated heart failure (HF) phenotype definition and translated it into CQL, then developed a CQL execution engine to integrate with the Observational Health Data Sciences and Informatics (OHDSI) platform. We executed the phenotype definition at two large academic medical centers, Northwestern Medicine and Weill Cornell Medicine, and conducted results verification (n = 100) to determine precision and recall. We additionally executed the same phenotype definition against two different data platforms, OHDSI and Fast Healthcare Interoperability Resources (FHIR), using the same underlying dataset and compared the results. Results: CQL is expressive enough to represent the HF phenotype definition, including Boolean and aggregate operators, and temporal relationships between data elements. The language design also enabled the implementation of a custom execution engine with relative ease, and results verification at both sites revealed that precision and recall were both 100%. Cross-platform execution resulted in identical patient cohorts generated by both data platforms. Conclusions: CQL supports the representation of arbitrarily complex phenotype definitions, and our execution engine implementation demonstrated cross-platform execution against two widely used clinical data platforms. The language thus has the potential to help address current limitations with portability in EHR-driven phenotyping and scale in learning health systems.
KW - Clinical Quality Language
KW - common data models
KW - electronic health records
KW - phenotyping
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U2 - 10.1002/lrh2.10233
DO - 10.1002/lrh2.10233
M3 - Article
C2 - 33083538
AN - SCOPUS:85087153567
SN - 2379-6146
VL - 4
JO - Learning Health Systems
JF - Learning Health Systems
IS - 4
M1 - e10233
ER -
