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

Author:

Wang JixuanORCID,Yang Jingbo,Zhang Haochi,Lu Helen,Skreta MartaORCID,Husić Mia,Arbabi Aryan,Sultanum Nicole,Brudno MichaelORCID

Abstract

AbstractCurrent 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.

Funder

Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada

RBC fellowship

Publisher

Springer Science and Business Media LLC

Subject

Health Information Management,Health Informatics,Computer Science Applications,Medicine (miscellaneous)

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