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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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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. |
format | Online Article Text |
id | pubmed-8795160 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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