ArticleJournal of biomedical informatics2019
Making work visible for electronic phenotype implementation: Lessons learned from the eMERGE network.
Article in Journal of biomedical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genetics in chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference.Kidney international · 2022Guideline
- SHREC: A framework for advancing next-generation computational phenotyping with large language models.PLOS digital health · 2026Article
- SHREC: A Framework for Advancing Next-Generation Computational Phenotyping with Large Language Models.ArXiv · 2025Article
- Lightweight Language Models are Prone to Reasoning Errors for Complex Computational Phenotyping Tasks.ArXiv · 2025Article
- Real-World Evidence BRIDGE: A Tool to Connect Protocol With Code Programming.Pharmacoepidemiology and drug safety · 2024Article
- PHEONA: An Evaluation Framework for Large Language Model-based Approaches to Computational Phenotyping.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Representing and utilizing clinical textual data for real world studies: An OHDSI approach.Journal of biomedical informatics · 2023Article
- Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network.Scientific reports · 2023Article
- Translating and evaluating historic phenotyping algorithms using SNOMED CT.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Not all phenotypes are created equal: covariates of success in e-phenotype specification.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Psychosocial-Behavioral Phenotyping: A Novel Precision Health Approach to Modeling Behavioral, Psychological, and Social Determinants of Health Using Machine Learning.Annals of behavioral medicine : a publication of the Society of Behavioral Medicine · 2022Article
- Multimodal machine learning in precision health: A scoping review.NPJ digital medicine · 2022Article
- Opportunities and challenges for the use of common controls in sequencing studies.Nature reviews. Genetics · 2022Review
- OARD: Open annotations for rare diseases and their phenotypes based on real-world data.American journal of human genetics · 2022Article
- A Framework for Augmented Intelligence in Allergy and Immunology Practice and Research-A Work Group Report of the AAAAI Health Informatics, Technology, and Education Committee.The journal of allergy and clinical immunology. In practice · 2022Article
- Standardized Health data and Research Exchange (SHaRE): promoting a learning health system.JAMIA open · 2022Article
- Developing real-world evidence from real-world data: Transforming raw data into analytical datasets.Learning health systems · 2022Article
- The Role of Electronic Health Records in Advancing Genomic Medicine.Annual review of genomics and human genetics · 2021Review
- Clinical comparison between trial participants and potentially eligible patients using electronic health record data: A generalizability assessment method.Journal of biomedical informatics · 2021Article
- Leveraging the Electronic Health Record to Address the COVID-19 Pandemic.Mayo Clinic proceedings · 2021Review
Corrections and comments
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Authors and funding
22 authors.
Funding
Abstract
backgroundImplementation of phenotype algorithms requires phenotype engineers to interpret human-readable algorithms and translate the description (text and flowcharts) into computable phenotypes - a process that can be labor intensive and error prone. To address the critical need for reducing the implementation efforts, it is important to develop portable algorithms.
methodsWe conducted a retrospective analysis of phenotype algorithms developed in the Electronic Medical Records and Genomics (eMERGE) network and identified common customization tasks required for implementation. A novel scoring system was developed to quantify portability from three aspects: Knowledge conversion, clause Interpretation, and Programming (KIP). Tasks were grouped into twenty representative categories. Experienced phenotype engineers were asked to estimate the average time spent on each category and evaluate time saving enabled by a common data model (CDM), specifically the Observational Medical Outcomes Partnership (OMOP) model, for each category.
resultsA total of 485 distinct clauses (phenotype criteria) were identified from 55 phenotype algorithms, corresponding to 1153 customization tasks. In addition to 25 non-phenotype-specific tasks, 46 tasks are related to interpretation, 613 tasks are related to knowledge conversion, and 469 tasks are related to programming. A score between 0 and 2 (0 for easy, 1 for moderate, and 2 for difficult portability) is assigned for each aspect, yielding a total KIP score range of 0 to 6. The average clause-wise KIP score to reflect portability is 1.37 ± 1.38. Specifically, the average knowledge (K) score is 0.64 ± 0.66, interpretation (I) score is 0.33 ± 0.55, and programming (P) score is 0.40 ± 0.64. 5% of the categories can be completed within one hour (median). 70% of the categories take from days to months to complete. The OMOP model can assist with vocabulary mapping tasks.
conclusionThis study presents firsthand knowledge of the substantial implementation efforts in phenotyping and introduces a novel metric (KIP) to measure portability of phenotype algorithms for quantifying such efforts across the eMERGE Network. Phenotype developers are encouraged to analyze and optimize the portability in regards to knowledge, interpretation and programming. CDMs can be used to improve the portability for some 'knowledge-oriented' tasks.
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Registered trials
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.