Currently accepted at: JMIR AI
Date Submitted: Jul 29, 2025
Open Peer Review Period: Jul 31, 2025 - Sep 25, 2025
Date Accepted: Feb 24, 2026
(closed for review but you can still tweet)
This paper has been accepted and is currently in production.
It will appear shortly on 10.2196/80549
The final accepted version (not copyedited yet) is in this tab.
Warning: This is an author submission that is not peer-reviewed or edited.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.
Evaluating Patient and Professional Satisfaction and Documentation Time Reduction through an AI-Driven Automatic Clinical Note Generation in Primary Care: A Proof of Concept
ABSTRACT
Background:
The workload that stems from writing clinical histories is one of the main sources of stress and overload for primary care professionals, accounting for up to 43% of the working day. The introduction of technology, specifically artificial intelligence, in the field of health could significantly reduce the time spent writing clinical reports without compromising the quality of care.
Objective:
The objective of this study was to evaluate the impact of implementing an AI solution for the automatic transcription of consultations in several Primary Care Centres in Catalonia.
Methods:
A proof of concept of a multi-centre study was carried out with alternating assignment of consultations to the intervention group (use of an artificial intelligence assistant that automatically generates consultation notes) or control group (usual clinical practice). The impact was evaluated through the recorded documentation time, the initial quality of the transcription measured with the Levenshtein distance and corrected words per minute, and the perceived satisfaction of patients and professionals through questionnaires evaluated through a Likert scale.
Results:
For the intervention group, the average processing time was 6.63%, while the review time by the professional amounted to 15.2%. In the control group, the writing time that professionals devoted to it was 30.37%. Therefore, the time savings were in an estimated range of 9% to 15% of the total consultation time. Levenshtein analysis showed that in most cases the review was <24 words per minute, indicating a high-quality initial transcription. The satisfaction surveys were answered by 289 patients and 213 professionals. Patient satisfaction was high (≥ 4/5) with no statistically significant differences between the control and intervention groups. The professionals rated the audio quality at 8.88/10 (SD= 1.31) (medicine) and 7.99/10 (SD= 1.76) (nursing), and the transcription at 8.01/10 (SD= 1.72) and 7.73/10 (SD= 1.76), respectively.
Conclusions:
The implementation of an AI tool demonstrated significant time savings, good professional acceptance and did not show a negative impact on patient satisfaction. The results suggest the possible use of these tools as a form of support for healthcare professionals. Randomised controlled trials are necessary to confirm the benefits in terms of healthcare quality and system efficiency. Clinical Trial: ClinicalTrials.gov NCT06618092; https://clinicaltrials.gov/study/NCT06618092
Citation
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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.