PhenoPad: Building AI enabled note-taking interfaces for patient encounters
Abstract 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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-01-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-021-00555-9 |
| _version_ | 1827776240682008576 |
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| author | Jixuan Wang Jingbo Yang Haochi Zhang Helen Lu Marta Skreta Mia Husić Aryan Arbabi Nicole Sultanum Michael Brudno |
| author_facet | Jixuan Wang Jingbo Yang Haochi Zhang Helen Lu Marta Skreta Mia Husić Aryan Arbabi Nicole Sultanum Michael Brudno |
| author_sort | Jixuan Wang |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-11T14:01:27Z |
| format | Article |
| id | doaj.art-9c638992fbb3407c9cbc21ef9baa6a1a |
| institution | Directory Open Access Journal |
| issn | 2398-6352 |
| language | English |
| last_indexed | 2024-03-11T14:01:27Z |
| publishDate | 2022-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj.art-9c638992fbb3407c9cbc21ef9baa6a1a2023-11-02T04:16:31ZengNature Portfolionpj Digital Medicine2398-63522022-01-01511910.1038/s41746-021-00555-9PhenoPad: Building AI enabled note-taking interfaces for patient encountersJixuan Wang0Jingbo Yang1Haochi Zhang2Helen Lu3Marta Skreta4Mia Husić5Aryan Arbabi6Nicole Sultanum7Michael Brudno8Department of Computer Science, University of TorontoDepartment of Computer Science, University of TorontoDATA Team & Techna Institute, University Health NetworkDepartment of Computer Science, University of TorontoDepartment of Computer Science, University of TorontoCentre for Computational Medicine, The Hospital For Sick ChildrenDepartment of Computer Science, University of TorontoDepartment of Computer Science, University of TorontoDepartment of Computer Science, University of TorontoAbstract 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.https://doi.org/10.1038/s41746-021-00555-9 |
| spellingShingle | Jixuan Wang Jingbo Yang Haochi Zhang Helen Lu Marta Skreta Mia Husić Aryan Arbabi Nicole Sultanum Michael Brudno PhenoPad: Building AI enabled note-taking interfaces for patient encounters npj Digital Medicine |
| 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 |
| url | https://doi.org/10.1038/s41746-021-00555-9 |
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