TY  - JOUR
AU  - Vidal-Alaball, Josep
AU  - Alonso, Carlos
AU  - Heinisch, Daniel Hugo
AU  - Castaño, Alberto
AU  - Sánchez-Freire, Encarna
AU  - Benito Serrano, María Luisa
AU  - Ferrer Pascual, Carla
AU  - Menacho, Ignacio
AU  - Acosta-Rojas, Ruthy
AU  - Cardona Gubert, Odda
AU  - Farrés Creus, Rosa
AU  - Armengol Alegre, Joan
AU  - Martínez Querol, Carles
AU  - Moreno-Martinez, Marina
AU  - Gonfaus Font, Mercè
AU  - Narejos, Silvia
AU  - Gomez-Fernandez, Anna
PY  - 2025
DA  - 2025/4/7
TI  - 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
JO  - JMIR Res Protoc
SP  - e66232
VL  - 14
KW  - primary health care
KW  - patient satisfaction
KW  - artificial intelligence
KW  - medical records systems
KW  - computerized
KW  - patient-centered care
AB  - Background: Relisten 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. Objective: This 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. Methods: This 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. Results: The 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. Conclusions: We 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 Registration: ClinicalTrials.gov NCT06618092; https://clinicaltrials.gov/study/NCT06618092 International Registered Report Identifier (IRRID): DERR1-10.2196/66232 
SN  - 1929-0748
UR  - https://www.researchprotocols.org/2025/1/e66232
UR  - https://doi.org/10.2196/66232
DO  - 10.2196/66232
ID  - info:doi/10.2196/66232
ER  -