ArticleBMC medical informatics and decision making2022
PheNominal: an EHR-integrated web application for structured deep phenotyping at the point of care.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 18 citations in OpenAlex.
- PhenoSS: phenotype semantic similarity-based approach for rare disease prediction and patient clustering.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- PhenoSS: Phenotype semantic similarity-based approach for rare disease prediction and patient clustering.medRxiv : the preprint server for health sciences · 2026Article
- Large Language Models in Bio-Ontology Research: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Design and implementation of a natural language processing system at the point of care: MiADE (medical information AI data extractor).BMC medical informatics and decision making · 2025Article
- A machine learning decision support tool optimizes WGS utilization in a neonatal intensive care unit.NPJ digital medicine · 2025Article
- A Machine Learning Decision Support Tool Optimizes Whole Genome Sequencing Utilization in a Neonatal Intensive Care Unit.medRxiv : the preprint server for health sciences · 2024Article
- A corpus of GA4GH Phenopackets: case-level phenotyping for genomic diagnostics and discovery.medRxiv : the preprint server for health sciences · 2024Article
- Use of Epic Electronic Health Record System for Health Care Research: Scoping Review.Journal of medical Internet research · 2023Article
- Predicting functional effects of ion channel variants using new phenotypic machine learning methods.PLoS computational biology · 2023Article
- OARD: Open annotations for rare diseases and their phenotypes based on real-world data.American journal of human genetics · 2022Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundClinical phenotype information greatly facilitates genetic diagnostic interpretations pipelines in disease. While post-hoc extraction using natural language processing on unstructured clinical notes continues to improve, there is a need to improve point-of-care collection of patient phenotypes. Therefore, we developed "PheNominal", a point-of-care web application, embedded within Epic electronic health record (EHR) workflows, to permit capture of standardized phenotype data.
methodsUsing bi-directional web services available within commercial EHRs, we developed a lightweight web application that allows users to rapidly browse and identify relevant terms from the Human Phenotype Ontology (HPO). Selected terms are saved discretely within the patient's EHR, permitting reuse both in clinical notes as well as in downstream diagnostic and research pipelines.
resultsIn the 16 months since implementation, PheNominal was used to capture discrete phenotype data for over 1500 individuals and 11,000 HPO terms during clinic and inpatient encounters for a genetic diagnostic consultation service within a quaternary-care pediatric academic medical center. An average of 7 HPO terms were captured per patient. Compared to a manual workflow, the average time to enter terms for a patient was reduced from 15 to 5 min per patient, and there were fewer annotation errors.
conclusionsModern EHRs support integration of external applications using application programming interfaces. We describe a practical application of these interfaces to facilitate deep phenotype capture in a discrete, structured format within a busy clinical workflow. Future versions will include a vendor-agnostic implementation using FHIR. We describe pilot efforts to integrate structured phenotyping through controlled dictionaries into diagnostic and research pipelines, reducing manual effort for phenotype documentation and reducing errors in data entry.
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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 Socratic graph.