Vidal-Alaball Josep, Alonso Carlos, Heinisch Daniel Hugo, Castaño Alberto, Sánchez-Freire Encarna, Benito Serrano María Luisa, Ferrer Pascual Carla, Menacho Ignacio, Acosta-Rojas Ruthy, Cardona Gubert Odda, Farrés Creus Rosa, Armengol Alegre Joan, Martínez Querol Carles, Moreno-Martinez Marina, Gonfaus Font Mercè, Narejos Silvia, Gomez-Fernandez Anna
Research and Innovation Unit, Gerència d'Atenció Primària i a la Comunitat de la Catalunya Central, Institut Català de la Salut, Manresa, Spain.
Intelligence for Primary Care Research Group, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Manresa, Spain.
JMIR Res Protoc. 2025 Apr 7;14:e66232. doi: 10.2196/66232.
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.
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.
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.
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.
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.
ClinicalTrials.gov NCT06618092; https://clinicaltrials.gov/study/NCT06618092.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/66232.
Relisten是Recog Analytics开发的一款基于人工智能（AI）的软件，通过促进医疗保健专业人员与患者之间更自然的互动来改善患者护理。该工具从录制的对话中提取相关信息，在病历中进行整理，并在专业人员批准后发送到健康信息系统。这种方法使专业人员能够专注于患者，而无需执行临床文档任务。
本研究旨在评估患者报告的满意度和感知的护理质量，评估医疗保健专业人员对所提供护理的满意度，并测量使用这种人工智能驱动的解决方案将记录录入电子病历所花费的时间。
这项概念验证（PoC）研究作为一项多中心试验进行，有几个初级保健中心（CAPs）的医疗保健专业人员（护士和医生）参与。关键结局指标包括：（1）患者报告的护理质量（通过匿名调查评估）；（2）医疗保健专业人员对所提供护理的满意度（通过调查和结构化访谈评估）；（3）临床文档节省的时间（通过比较手动书写笔记与审核和纠正人工智能生成的笔记所花费的时间来确定）。将针对每个目标进行统计分析，根据用Kolmogorov-Smirnov检验和Lilliefors校正评估的正态性，使用独立样本比较检验。还将进行分层统计检验，以考虑专业人员之间的差异。
该方案已使用SPIRIT（标准方案项目：干预试验建议）清单制定。招募工作于2024年7月开始，截至2024年11月，共招募了318名患者。预计招募工作将于2025年3月完成。数据分析将于2025年4月至5月进行，结果预计于2025年6月发表。
我们预计患者报告的感知护理质量会有所提高，临床记录所花费的时间会大幅减少，每次就诊至少节省30秒。虽然预计生成的记录质量很高，但不确定是否会证明比预期已经具有高质量记录的对照组有显著改善。
ClinicalTrials.gov NCT06618092；https://clinicaltrials.gov/study/NCT06618092。
国际注册报告标识符（IRRID）：DERR1-10.2196/66232。
