{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:49:50Z","timestamp":1777510190467,"version":"3.51.4"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686080","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>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\u2014AWS General, AWS Medical, Whisper, and Wave2Vec\u2014in transcribing 860 patient\u2013nurse utterances (475 Black, 385 White). Word error rate (WER) was the primary measure. AWS General achieved the highest accuracy (median WER 39%), but all systems were less accurate for Black patients, particularly in linguistic domains \u201cAffect,\u201d \u201cSocial,\u201d and \u201cDrives.\u201d AWS Medical outperformed others on medical terms, although filler words, repetition, and nonmedical terms challenged every system. These findings underscore the need for diverse training datasets and improved dialect sensitivity to ensure equitable ASR performance and robust risk identification in home healthcare.<\/jats:p>","DOI":"10.3233\/shti251273","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:46:48Z","timestamp":1754567208000},"source":"Crossref","is-referenced-by-count":2,"title":["Voice for All: Evaluating the Accuracy and Equity of Automatic Speech Recognition Systems in Transcribing Patient Communications in Home Healthcare"],"prefix":"10.3233","author":[{"given":"Zidu","family":"Xu","sequence":"first","affiliation":[{"name":"School of Nursing, Columbia University, New York, NY 10032, United States"}]},{"given":"Sasha","family":"Vergez","sequence":"additional","affiliation":[{"name":"Center for Home Care Policy & Research, VNS Health, New York, NY 10017, United States"}]},{"given":"Elyas","family":"Esmaeili","sequence":"additional","affiliation":[{"name":"Columbia University Irving Medical Center, New York, NY 10032, United States"}]},{"given":"Ali","family":"Zolnour","sequence":"additional","affiliation":[{"name":"Columbia University Irving Medical Center, New York, NY 10032, United States"}]},{"given":"Krystal Anne","family":"Briggs","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Columbia University, New York, NY 10027, United States"}]},{"given":"Jihye Kim","family":"Scroggins","sequence":"additional","affiliation":[{"name":"School of Nursing, Columbia University, New York, NY 10032, United States"}]},{"given":"Seyed Farid","family":"Hosseini Ebrahimabad","sequence":"additional","affiliation":[{"name":"Department of Automatic Control and Computer Science, Politehnica University of Bucharest, Bucharest RO-060042, Romania"}]},{"given":"James M.","family":"Noble","sequence":"additional","affiliation":[{"name":"Department of Neurology, Taub Institute for Research on Alzheimer\u2019s Disease and the Aging Brain, GH Sergievsky Center, Columbia University, New York, NY 10032, United States"}]},{"given":"Maxim","family":"Topaz","sequence":"additional","affiliation":[{"name":"School of Nursing, Columbia University, New York, NY 10032, United States"},{"name":"Data Science Institute, Columbia University, New York, NY 10027, United States"}]},{"given":"Suzanne","family":"Bakken","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University, New York, NY 10032, United States"}]},{"given":"Kathryn H.","family":"Bowles","sequence":"additional","affiliation":[{"name":"University of Pennsylvania School of Nursing, Philadelphia, PA 19104, United States"}]},{"given":"Ian","family":"Spens","sequence":"additional","affiliation":[{"name":"Center for Home Care Policy & Research, VNS Health, New York, NY 10017, United States"}]},{"given":"Nicole","family":"Onorato","sequence":"additional","affiliation":[{"name":"Center for Home Care Policy & Research, VNS Health, New York, NY 10017, United States"}]},{"given":"Sridevi","family":"Sridharan","sequence":"additional","affiliation":[{"name":"Center for Home Care Policy & Research, VNS Health, New York, NY 10017, United States"}]},{"given":"Margaret V.","family":"McDonald","sequence":"additional","affiliation":[{"name":"Center for Home Care Policy & Research, VNS Health, New York, NY 10017, United States"}]},{"given":"Maryam","family":"Zolnoori","sequence":"additional","affiliation":[{"name":"School of Nursing, Columbia University, New York, NY 10032, United States"},{"name":"Columbia University Irving Medical Center, New York, NY 10032, United States"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI251273","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:46:49Z","timestamp":1754567209000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI251273"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti251273","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}