Currently submitted to: Journal of Medical Internet Research
Date Submitted: Jun 17, 2026
Open Peer Review Period: Jun 18, 2026 - Aug 13, 2026
(closed for review but you can still tweet)
NOTE: This is an unreviewed Preprint
Warning: This is a unreviewed preprint (What is a preprint?). Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note "no longer under consideration" will appear above).
Peer review me: Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period (in this case, a "Peer Review Me" button to sign up as reviewer is displayed above). All preprints currently open for review are listed here. Outside of the formal open peer-review period we encourage you to tweet about the preprint.
Citation: Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).
Final version: If our system detects a final peer-reviewed "version of record" (VoR) published in any journal, a link to that VoR will appear below. Readers are then encourage to cite the VoR instead of this preprint.
Settings: If you are the author, you can login and change the preprint display settings, but the preprint URL/DOI is supposed to be stable and citable, so it should not be removed once posted.
Submit: To post your own preprint, simply submit to any JMIR journal, and choose the appropriate settings to expose your submitted version as preprint.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Speech-Driven Reporting in Long-Term Care: A Mixed Methods Evaluation Study
ABSTRACT
Background:
Long-term care (LTR) faces critical challenges driven by workforce shortages, an aging population, and a growing population of people living with dementia. Administrative burdens add to this pressure, as healthcare professionals spend up to 40% of their working time on administration and documentation. Speech-driven AI reporting (SDR) may offer a technological solution to alleviate administrative reporting workload and enhance the workflow efficiency of care workers.
Objective:
This study aimed to empirically study the effects of SDR on documentation time, transcription accuracy measured by Word Error Rate, user experiences, and the client-caregiver interaction within nursing homes and home care settings.
Methods:
A mixed-methods study, involving 21 healthcare organizations, was conducted in the Netherlands between January and September of 2025. An experimental evaluation study comparing speech-driven and typed reporting under controlled conditions (n=35), complemented by a cross-sectional questionnaire study among care professionals from 14 elderly care organizations (n=293). Documentation time and Word Error Rate were analyzed using linear mixed models. Associations between system use duration and user experience were examined using correlation analyses.
Results:
The controlled evaluation study demonstrated a significant reduction in reporting time. SDR was found to be significantly faster than typing (p < 0.01), with a significant interaction between reporting device and method (p = 0.01), being 3.5 times faster on smartphones (34 seconds vs. 122 s) and 2.3 times faster on laptops (43 vs. 102 seconds). The SDR AI software demonstrated high transcription accuracy (Word Error Rate <0.05). SDR did change the reporting process: healthcare workers reported more directly after they provided care for their clients (19.0% vs 42.1%; p<0.001) and fewer reports were made after the end of their shift. Also, no correlations between SDR use and technology acceptance aspects, or perceived work pressure were determined.
Conclusions:
The current SDR technology offers time savings and high accuracy regardless of the device used (smartphone or laptop). However, the technological capability alone does not automatically translate to reduced perceived work pressure by care workers. The findings suggest that the challenge has shifted from technical feasibility to implementation strategy and behavioral change.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.