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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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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| author | Josep Vidal-Alaball Carlos Alonso Daniel Hugo Heinisch Alberto Castaño Encarna Sánchez-Freire María Luisa Benito Serrano Carla Ferrer Pascual Ignacio Menacho Ruthy Acosta-Rojas Odda Cardona Gubert Rosa Farrés Creus Joan Armengol Alegre Carles Martínez Querol Marina Moreno-Martinez Mercè Gonfaus Font Silvia Narejos Anna Gomez-Fernandez |
| author_facet | Josep Vidal-Alaball Carlos Alonso Daniel Hugo Heinisch Alberto Castaño Encarna Sánchez-Freire María Luisa Benito Serrano Carla Ferrer Pascual Ignacio Menacho Ruthy Acosta-Rojas Odda Cardona Gubert Rosa Farrés Creus Joan Armengol Alegre Carles Martínez Querol Marina Moreno-Martinez Mercè Gonfaus Font Silvia Narejos Anna Gomez-Fernandez |
| author_sort | Josep Vidal-Alaball |
| collection | DOAJ |
| description |
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 |
| format | Article |
| id | doaj-art-80bbb741a03f40a0bfae67c0cb7b739e |
| institution | OA Journals |
| issn | 1929-0748 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | JMIR Publications |
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| series | JMIR Research Protocols |
| spelling | doaj-art-80bbb741a03f40a0bfae67c0cb7b739e2025-08-20T02:25:44ZengJMIR PublicationsJMIR Research Protocols1929-07482025-04-0114e6623210.2196/66232Assessing Patient-Reported Satisfaction With Care and Documentation Time in Primary Care Through AI-Driven Automatic Clinical Note Generation: Protocol for a Proof-of-Concept StudyJosep Vidal-Alaballhttps://orcid.org/0000-0002-3527-4242Carlos Alonsohttps://orcid.org/0009-0001-5741-2669Daniel Hugo Heinischhttps://orcid.org/0009-0002-8262-947XAlberto Castañohttps://orcid.org/0000-0002-3946-5820Encarna Sánchez-Freirehttps://orcid.org/0000-0002-1945-5115María Luisa Benito Serranohttps://orcid.org/0000-0001-7835-0068Carla Ferrer Pascualhttps://orcid.org/0009-0002-9831-4958Ignacio Menachohttps://orcid.org/0000-0002-3559-7543Ruthy Acosta-Rojashttps://orcid.org/0000-0003-2225-520XOdda Cardona Guberthttps://orcid.org/0009-0008-8546-0343Rosa Farrés Creushttps://orcid.org/0009-0006-3192-817XJoan Armengol Alegrehttps://orcid.org/0000-0001-9876-1198Carles Martínez Querolhttps://orcid.org/0009-0009-2499-3282Marina Moreno-Martinezhttps://orcid.org/0000-0002-5316-8451Mercè Gonfaus Fonthttps://orcid.org/0009-0008-1150-9896Silvia Narejoshttps://orcid.org/0000-0002-7720-9289Anna Gomez-Fernandezhttps://orcid.org/0009-0005-9125-8027 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)DERR1-10.2196/66232https://www.researchprotocols.org/2025/1/e66232 |
| spellingShingle | Josep Vidal-Alaball Carlos Alonso Daniel Hugo Heinisch Alberto Castaño Encarna Sánchez-Freire María Luisa Benito Serrano Carla Ferrer Pascual Ignacio Menacho Ruthy Acosta-Rojas Odda Cardona Gubert Rosa Farrés Creus Joan Armengol Alegre Carles Martínez Querol Marina Moreno-Martinez Mercè Gonfaus Font Silvia Narejos Anna Gomez-Fernandez 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 JMIR Research Protocols |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| url | https://www.researchprotocols.org/2025/1/e66232 |
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