ArticleLearning health systems2020
Toward cross-platform electronic health record-driven phenotyping using Clinical Quality Language.
Article in Learning health systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Electronic Health Record-Driven Approaches in Primary Care to Strengthen Hypertension Management Among Racial and Ethnic Minoritized Groups in the United States: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Development and implementation of a computerised clinical decision support system for hospital-in-the-home patient identification.Internal medicine journal · 2026Article
- Towards cross-application model-agnostic federated cohort discovery.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Phenotype execution and modeling architecture to support disease surveillance and real-world evidence studies: English sentinel network evaluation.JAMIA open · 2024Article
- Charting Tomorrow's Healthcare: A Traditional Literature Review for an Artificial Intelligence-Driven Future.Cureus · 2024Review
- A flexible symbolic regression method for constructing interpretable clinical prediction models.NPJ digital medicine · 2023Article
- Characterizing variability of electronic health record-driven phenotype definitions.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Article
- HL7 FHIR-based tools and initiatives to support clinical research: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Design and validation of a FHIR-based EHR-driven phenotyping toolbox.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Development of a repository of computable phenotype definitions using the clinical quality language.JAMIA open · 2021Article
- Desiderata for the development of next-generation electronic health record phenotype libraries.GigaScience · 2021Article
- ACE: the Advanced Cohort Engine for searching longitudinal patient records.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Leveraging the Electronic Health Record to Address the COVID-19 Pandemic.Mayo Clinic proceedings · 2021Review
- Toward cross-platform electronic health record-driven phenotyping using Clinical Quality Language.Learning health systems · 2020Article
- Why does human phenomics matter today?Learning health systems · 2020Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
introductionElectronic 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.
methodsWe 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.
resultsCQL 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.
conclusionsCQL 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.