Health AI Central
Peer-Reviewed Publication
Stud Health Technol Inform2025;3291904-1906.August 7, 2025Journal Article

Voice for All: Evaluating the Accuracy and Equity of Automatic Speech Recognition Systems in Transcribing Patient Communications in Home Healthcare.

Zidu Xu1, Sasha Vergez2, Elyas Esmaeili3, Ali Zolnour3, Krystal Anne Briggs4, Jihye Kim Scroggins1, Seyed Farid Hosseini Ebrahimabad5, James M Noble6, Maxim Topaz1,7, Suzanne Bakken8, Kathryn H Bowles9, Ian Spens2, Nicole Onorato2, Sridevi Sridharan2, Margaret V McDonald2, Maryam Zolnoori1,3
1School of Nursing, Columbia University, New York, NY 10032, United States.
2Center for Home Care Policy & Research, VNS Health, New York, NY 10017, United States.
3Columbia University Irving Medical Center, New York, NY 10032, United States.
4Department of Computer Science, Columbia University, New York, NY 10027, United States.
5Department of Automatic Control and Computer Science, Politehnica University of Bucharest, Bucharest RO-060042, Romania.
6Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging Brain, GH Sergievsky Center, Columbia University, New York, NY 10032, United States.
7Data Science Institute, Columbia University, New York, NY 10027, United States.
8Department of Biomedical Informatics, Columbia University, New York, NY 10032, United States.
9University of Pennsylvania School of Nursing, Philadelphia, PA 19104, United States.
PubMed DOI

Abstract

Integrating automatic speech recognition (ASR) systems into home healthcare workflows can enhance risk prediction models. However, ASR systems exhibit disparities in transcription accuracy across racial and linguistic groups, highlighting an equity gap that could bias healthcare delivery. We evaluated four ASR systems-AWS General, AWS Medical, Whisper, and Wave2Vec-in transcribing 860 patient-nurs…

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