Usability and workflow implications of an electronic health record-integrated tool for automated phenotyping: Formative evaluation of Pheno+
- 1Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT
Abstract
ObjectiveTo evaluate usability and workflow implications of Pheno+, an electronic health record- (EHR) embedded natural language processing tool that extracts patient phenotypes and maps them to Human Phenotype Ontology (HPO) terms.
Materials and MethodsThis study performed a mixed-methods formative evaluation of Pheno+ in the context of rapid genome sequencing in the neonatal intensive care unit. Two rounds of think-aloud interviews and surveys were conducted with neonatologists, advanced practice providers, medical geneticists, genetic counselors, non-genetics pediatric subspecialists and laboratory molecular geneticists. Usability was assessed using the System Usability Scale (SUS). Structured data were analyzed descriptively. Qualitative data underwent hybrid deductive-inductive thematic analysis.
ResultsTwenty-eight interviewees participated in two rounds of data collection. Users retained an average of 24.2 HPO terms from an average of 45.6 terms per patient. Mean time curating terms was 8:59 per patient. Final SUS score was 76 (above scale mean). Qualitative themes included: burden of curating terms; interpretation of phenotypes; adding information to phenotypes; role of genetics expertise; and tool uses beyond the original core use case for Pheno+.
DiscussionPheno+ demonstrated positive usability but required curation effort to reduce erroneous terms. Participant responses indicate that effective use may depend on knowledge of HPO structure and laboratory workflows. Automated phenotyping seems likely to reduce net clinician workload, while also reshaping clinical responsibilities, expertise requirements, and communication in genetic testing.
ConclusionTools like Pheno+ can support scalable genomic medicine. Design and implementation may benefit from addressing curation burden, phenotype contextualization, expertise requirements, and other workflow needs.
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BibTeX
@misc{taber2026usability,
title = {{Usability and workflow implications of an electronic health record-integrated tool for automated phenotyping: Formative evaluation of Pheno+}},
author = {P. Taber and L. Weaver and T. Di Sera and C. Solorzano and E. Lebleu and M. Bolyard and J. Butler and T. Ellsworth and I. Cooperstein and P. Estabrooks and K. Kawamoto and P. B. Warner and K. Simek and M. Tristani-Firouzi and A. Ward and D. Martin and S. Malone Jenkins},
howpublished = {medRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.22.26363702},
url = {https://www.medrxiv.org/content/10.64898/2026.09.22.26363702v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Usability and workflow implications of an electronic health record-integrated tool for automated phenotyping: Formative evaluation of Pheno+
AU - P. Taber
AU - L. Weaver
AU - T. Di Sera
AU - C. Solorzano
AU - E. Lebleu
AU - M. Bolyard
AU - J. Butler
AU - T. Ellsworth
AU - I. Cooperstein
AU - P. Estabrooks
AU - K. Kawamoto
AU - P. B. Warner
AU - K. Simek
AU - M. Tristani-Firouzi
AU - A. Ward
AU - D. Martin
AU - S. Malone Jenkins
PY - 2026
DA - 2026/09/22
PB - medRxiv
DO - 10.64898/2026.09.22.26363702
UR - https://www.medrxiv.org/content/10.64898/2026.09.22.26363702v1
AB - ObjectiveTo evaluate usability and workflow implications of Pheno+, an electronic health record- (EHR) embedded natural language processing tool that extracts patient phenotypes and maps them to Human Phenotype Ontology (HPO) terms. Materials and MethodsThis study performed a mixed-methods formative evaluation of Pheno+ in the context of rapid genome sequencing in the neonatal intensive care unit. Two rounds of think-aloud interviews and surveys were conducted with neonatologists, advanced practice providers, medical geneticists, genetic counselors, non-genetics pediatric subspecialists and laboratory molecular geneticists. Usability was assessed using the System Usability Scale (SUS). Structured data were analyzed descriptively. Qualitative data underwent hybrid deductive-inductive thematic analysis. ResultsTwenty-eight interviewees participated in two rounds of data collection. Users retained an average of 24.2 HPO terms from an average of 45.6 terms per patient. Mean time curating terms was 8:59 per patient. Final SUS score was 76 (above scale mean). Qualitative themes included: burden of curating terms; interpretation of phenotypes; adding information to phenotypes; role of genetics expertise; and tool uses beyond the original core use case for Pheno+. DiscussionPheno+ demonstrated positive usability but required curation effort to reduce erroneous terms. Participant responses indicate that effective use may depend on knowledge of HPO structure and laboratory workflows. Automated phenotyping seems likely to reduce net clinician workload, while also reshaping clinical responsibilities, expertise requirements, and communication in genetic testing. ConclusionTools like Pheno+ can support scalable genomic medicine. Design and implementation may benefit from addressing curation burden, phenotype contextualization, expertise requirements, and other workflow needs.
ER -