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PheNominal: an EHR-integrated web application for structured deep phenotyping at the point of care.
Havrilla, James M; Singaravelu, Anbumalar; Driscoll, Dennis M; Minkovsky, Leonard; Helbig, Ingo; Medne, Livija; Wang, Kai; Krantz, Ian; Desai, Bimal R.
Afiliación
  • Havrilla JM; Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
  • Singaravelu A; Emerging Technology and Transformation Team, Information Services, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
  • Driscoll DM; Emerging Technology and Transformation Team, Information Services, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
  • Minkovsky L; Emerging Technology and Transformation Team, Information Services, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
  • Helbig I; Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
  • Medne L; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, USA.
  • Wang K; Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
  • Krantz I; Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19104, USA.
  • Desai BR; Roberts Individualized Medical Genetics Center, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
BMC Med Inform Decis Mak ; 22(Suppl 2): 198, 2022 07 28.
Article en En | MEDLINE | ID: mdl-35902925
BACKGROUND: Clinical 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. METHODS: Using 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. RESULTS: In 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. CONCLUSIONS: Modern 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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 1_ASSA2030 Problema de salud: 1_sistemas_informacao_saude Asunto principal: Sistemas de Atención de Punto / Registros Electrónicos de Salud Tipo de estudio: Guideline / Prognostic_studies Límite: Child / Humans Idioma: En Revista: BMC Med Inform Decis Mak Asunto de la revista: INFORMATICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 1_ASSA2030 Problema de salud: 1_sistemas_informacao_saude Asunto principal: Sistemas de Atención de Punto / Registros Electrónicos de Salud Tipo de estudio: Guideline / Prognostic_studies Límite: Child / Humans Idioma: En Revista: BMC Med Inform Decis Mak Asunto de la revista: INFORMATICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos
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