Pheno+: An Electronic Health Record-Integrated, Clinician-in-the-Loop Application for Efficient Phenotyping…

Abstract

Objective To develop and evaluate Pheno+, an electronic health record (EHR)-integrated application that streamlines the generation of structured phenotypes for genomic testing and analysis. Materials and Methods We developed Pheno+ as a Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources (SMART on FHIR) application embedded within the University of Utah Health EHR. Pheno+ retrieves clinician-selected clinical notes and applies ClinPhen, a natural language processing tool that identifies candidate Human Phenotype Ontology (HPO) terms from clinical text. Clinicians review and curate the resulting HPO terms within the EHR. We used an eight case crossover design to compare total workflow time and HPO term counts between Pheno+ and a standard workflow involving physician chart review followed by HPO term selection by a molecular geneticist. In a separate cohort of 17 patients with confirmed monogenic diagnoses, we evaluated the utility of Pheno+-derived HPO terms for diagnostic gene prioritization by comparing Exomiser performance with and without phenotype input. Results Pheno+ reduced mean total workflow time by 66%, from 31.1 to 10.5 minutes (p < 0.001). Exomiser ranked the causative gene first in all 17 cases (100%) using Pheno+-derived HPO terms. Discussion and Conclusion Pheno+ reduced the time and personnel required to generate clinician-curated HPO term lists while producing phenotypes that provided meaningful diagnostic information for downstream genomic analyses. These findings support EHR-integrated, clinician-in-the-loop phenotyping for genomic workflows.

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BibTeX RIS

BibTeX

@misc{lebleu2026pheno,
  title = {{Pheno+: An Electronic Health Record-Integrated, Clinician-in-the-Loop Application for Efficient Phenotyping in Clinical Genomics}},
  author = {E. L. Lebleu and I. B. Cooperstein and S. M. Jenkins and P. Bayrak-Toydemir and R. Mao and A. Ward and T. Di Sera and C. M. Solorzano and T. Ellsworth and K. Simek and P. Taber and P. Warner and K. B. Kawamoto and G. T. Marth and M. Tristani-Firouzi},
  howpublished = {medRxiv},
  year = {2026},
  month = nov,
  doi = {10.64898/2026.11.02.26366992},
  url = {https://www.medrxiv.org/content/10.64898/2026.11.02.26366992v1},
  note = {CC BY},
}

RIS

TY  - UNPB
TI  - Pheno+: An Electronic Health Record-Integrated, Clinician-in-the-Loop Application for Efficient Phenotyping in Clinical Genomics
AU  - E. L. Lebleu
AU  - I. B. Cooperstein
AU  - S. M. Jenkins
AU  - P. Bayrak-Toydemir
AU  - R. Mao
AU  - A. Ward
AU  - T. Di Sera
AU  - C. M. Solorzano
AU  - T. Ellsworth
AU  - K. Simek
AU  - P. Taber
AU  - P. Warner
AU  - K. B. Kawamoto
AU  - G. T. Marth
AU  - M. Tristani-Firouzi
PY  - 2026
DA  - 2026/11/02
PB  - medRxiv
DO  - 10.64898/2026.11.02.26366992
UR  - https://www.medrxiv.org/content/10.64898/2026.11.02.26366992v1
AB  - Objective To develop and evaluate Pheno+, an electronic health record (EHR)-integrated application that streamlines the generation of structured phenotypes for genomic testing and analysis. Materials and Methods We developed Pheno+ as a Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources (SMART on FHIR) application embedded within the University of Utah Health EHR. Pheno+ retrieves clinician-selected clinical notes and applies ClinPhen, a natural language processing tool that identifies candidate Human Phenotype Ontology (HPO) terms from clinical text. Clinicians review and curate the resulting HPO terms within the EHR. We used an eight case crossover design to compare total workflow time and HPO term counts between Pheno+ and a standard workflow involving physician chart review followed by HPO term selection by a molecular geneticist. In a separate cohort of 17 patients with confirmed monogenic diagnoses, we evaluated the utility of Pheno+-derived HPO terms for diagnostic gene prioritization by comparing Exomiser performance with and without phenotype input. Results Pheno+ reduced mean total workflow time by 66%, from 31.1 to 10.5 minutes (p < 0.001). Exomiser ranked the causative gene first in all 17 cases (100%) using Pheno+-derived HPO terms. Discussion and Conclusion Pheno+ reduced the time and personnel required to generate clinician-curated HPO term lists while producing phenotypes that provided meaningful diagnostic information for downstream genomic analyses. These findings support EHR-integrated, clinician-in-the-loop phenotyping for genomic workflows.
ER  -

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