SLPPilotJMIR rehabilitation and assistive technologies2026

Assessing the Role of Medical Caption Technology to Support Physician-Patient Communication for Patients With Hearing Loss: Mixed Methods Pilot Study.

Sarah E Hughes, Liang-Yuan Wu, Lindsay J Ma and 2 others

PMID 41538699

WHAT IT FOUND

Ten adults with hearing loss rated live captions during masked mock doctor visits as easy, trustworthy and satisfying, and said they felt less stressed; accuracy for complex medical terms was not proven.

Key findings

01Participants said captions were not distracting in 86% of scenarios, easy to watch in 90%, trustworthy in 90%, and satisfying in 76%.

02The captioning system's word error rates for mock doctor speech ranged from 0.127 to 0.167 across the 10 analysed sessions.

03Nine of ten participants described feeling reassured, relaxed or comfortable, and captions were reported as helpful when masks made lip reading difficult.

STILL TO COME

How it was doneWhat they foundWhat it means for SLPs

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What it does not show

Only 10 participants were analysed, and the study was too small to detect differences between subgroups. The visits were scripted mock scenarios with medical students wearing masks, not real clinical conversations. Most participants already used captioning or other hearing assistive technology, so people trying captions for the first time may react differently. The sample preferred spoken English and did not include people who primarily use American Sign Language. The captioning system was not optimised for medical vocabulary, and participants worried it could misrecognise complex terms or medication names. Word error rate treats all transcription errors equally, so it may not show the risk of a wrong medication name or diagnosis. One participant's data were lost because of equipment failure.

Declared interests

The authors declared no conflicts of interest.

The easy way to misread this

Do not read this as proof that live captions improve real clinical care. It was a small simulated pilot of usability and acceptability, and accuracy for complex medical terms was not established.

Summarised by AI from the full paper, without a clinician reviewing it. Check it against the source before it changes what you do. Read it on PubMed →