npj Digital Medicine (Jan 2022)

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

  • Jixuan Wang,
  • Jingbo Yang,
  • Haochi Zhang,
  • Helen Lu,
  • Marta Skreta,
  • Mia Husić,
  • Aryan Arbabi,
  • Nicole Sultanum,
  • Michael Brudno

DOI
https://doi.org/10.1038/s41746-021-00555-9
Journal volume & issue
Vol. 5, no. 1
pp. 1 – 9

Abstract

Read online

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.