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PhenoPad: Building AI enabled note-taking interfaces for patient encounters

Current clinical note-taking approaches cannot capture the entirety of information available from patient encounters and detract from patient-clinician interactions. By surveying healthcare providers’ current note-taking practices and attitudes toward new clinical technologies, we developed a patien...

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Autores principales: Wang, Jixuan, Yang, Jingbo, Zhang, Haochi, Lu, Helen, Skreta, Marta, Husić, Mia, Arbabi, Aryan, Sultanum, Nicole, Brudno, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8795160/
https://www.ncbi.nlm.nih.gov/pubmed/35087180
http://dx.doi.org/10.1038/s41746-021-00555-9
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author Wang, Jixuan
Yang, Jingbo
Zhang, Haochi
Lu, Helen
Skreta, Marta
Husić, Mia
Arbabi, Aryan
Sultanum, Nicole
Brudno, Michael
author_facet Wang, Jixuan
Yang, Jingbo
Zhang, Haochi
Lu, Helen
Skreta, Marta
Husić, Mia
Arbabi, Aryan
Sultanum, Nicole
Brudno, Michael
author_sort Wang, Jixuan
collection PubMed
description Current clinical note-taking approaches cannot capture the entirety of information available from patient encounters and detract from patient-clinician interactions. By surveying healthcare providers’ current note-taking practices and attitudes toward new clinical technologies, we developed a patient-centered paradigm for clinical note-taking that makes use of hybrid tablet/keyboard devices and artificial intelligence (AI) technologies. PhenoPad is an intelligent clinical note-taking interface that captures free-form notes and standard phenotypic information via a variety of modalities, including speech and natural language processing techniques, handwriting recognition, and more. The output is unobtrusively presented on mobile devices to clinicians for real-time validation and can be automatically transformed into digital formats that would be compatible with integration into electronic health record systems. Semi-structured interviews and trials in clinical settings rendered positive feedback from both clinicians and patients, demonstrating that AI-enabled clinical note-taking under our design improves ease and breadth of information captured during clinical visits without compromising patient-clinician interactions. We open source a proof-of-concept implementation that can lay the foundation for broader clinical use cases.
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spelling pubmed-87951602022-02-07 PhenoPad: Building AI enabled note-taking interfaces for patient encounters Wang, Jixuan Yang, Jingbo Zhang, Haochi Lu, Helen Skreta, Marta Husić, Mia Arbabi, Aryan Sultanum, Nicole Brudno, Michael NPJ Digit Med Article Current clinical note-taking approaches cannot capture the entirety of information available from patient encounters and detract from patient-clinician interactions. By surveying healthcare providers’ current note-taking practices and attitudes toward new clinical technologies, we developed a patient-centered paradigm for clinical note-taking that makes use of hybrid tablet/keyboard devices and artificial intelligence (AI) technologies. PhenoPad is an intelligent clinical note-taking interface that captures free-form notes and standard phenotypic information via a variety of modalities, including speech and natural language processing techniques, handwriting recognition, and more. The output is unobtrusively presented on mobile devices to clinicians for real-time validation and can be automatically transformed into digital formats that would be compatible with integration into electronic health record systems. Semi-structured interviews and trials in clinical settings rendered positive feedback from both clinicians and patients, demonstrating that AI-enabled clinical note-taking under our design improves ease and breadth of information captured during clinical visits without compromising patient-clinician interactions. We open source a proof-of-concept implementation that can lay the foundation for broader clinical use cases. Nature Publishing Group UK 2022-01-27 /pmc/articles/PMC8795160/ /pubmed/35087180 http://dx.doi.org/10.1038/s41746-021-00555-9 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Wang, Jixuan
Yang, Jingbo
Zhang, Haochi
Lu, Helen
Skreta, Marta
Husić, Mia
Arbabi, Aryan
Sultanum, Nicole
Brudno, Michael
PhenoPad: Building AI enabled note-taking interfaces for patient encounters
title PhenoPad: Building AI enabled note-taking interfaces for patient encounters
title_full PhenoPad: Building AI enabled note-taking interfaces for patient encounters
title_fullStr PhenoPad: Building AI enabled note-taking interfaces for patient encounters
title_full_unstemmed PhenoPad: Building AI enabled note-taking interfaces for patient encounters
title_short PhenoPad: Building AI enabled note-taking interfaces for patient encounters
title_sort phenopad: building ai enabled note-taking interfaces for patient encounters
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8795160/
https://www.ncbi.nlm.nih.gov/pubmed/35087180
http://dx.doi.org/10.1038/s41746-021-00555-9
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