Assessing Patient-Reported Satisfaction With Care and Documentation Time in Primary Care Through AI-Driven Automatic Clinical Note Generation: Protocol for a Proof-of-Concept Study
BackgroundRelisten is an artificial intelligence (AI)–based software developed by Recog Analytics that improves patient care by facilitating more natural interactions between health care professionals and patients. This tool extracts relevant information from recorded convers...
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2025-04-01
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| Series: | JMIR Research Protocols |
| Online Access: | https://www.researchprotocols.org/2025/1/e66232 |
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| Summary: | BackgroundRelisten is an artificial intelligence (AI)–based software developed by Recog Analytics that improves patient care by facilitating more natural interactions between health care professionals and patients. This tool extracts relevant information from recorded conversations, structuring it in the medical record, and sending it to the Health Information System after the professional’s approval. This approach allows professionals to focus on the patient without the need to perform clinical documentation tasks.
ObjectiveThis study aims to evaluate patient-reported satisfaction and perceived quality of care, assess health care professionals’ satisfaction with the care provided, and measure the time spent on entering records into the electronic medical record using this AI-powered solution.
MethodsThis proof-of-concept (PoC) study is conducted as a multicenter trial with the participation of several health care professionals (nurses and physicians) in primary care centers (CAPs). The key outcome measures include (1) patient-reported quality of care (evaluated through anonymous surveys), (2) health care professionals’ satisfaction with the care provided (assessed through surveys and structured interviews), and (3) time saved on clinical documentation (determined by comparing the time spent manually writing notes versus reviewing and correcting AI-generated notes). Statistical analyses will be performed for each objective, using independent sample comparison tests according to normality evaluated with the Kolmogorov-Smirnov test and Lilliefors correction. Stratified statistical tests will also be performed to consider the variance between professionals.
ResultsThe protocol has been developed using the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) checklist. Recruitment began in July 2024, and as of November 2024, a total of 318 patients have been enrolled. Recruitment is expected to be completed by March 2025. Data analysis will take place between April and May 2025, with results expected to be published in June 2025.
ConclusionsWe expect an improvement in the perceived quality of care reported by patients and a significant reduction in the time spent taking clinical notes, with a saving of at least 30 seconds per visit. Although a high quality of the notes generated is expected, it is uncertain whether a significant improvement over the control group, which is already expected to have high-quality notes, will be demonstrated.
Trial RegistrationClinicalTrials.gov NCT06618092; https://clinicaltrials.gov/study/NCT06618092
International Registered Report Identifier (IRRID)DERR1-10.2196/66232 |
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| ISSN: | 1929-0748 |