sAItisfACES - Impact of an Artificial Intelligence Tool on the Satisfaction of Professionals and Patients in Ambulatory Consultations: A Randomized Cluster Study
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 148
- 试验地点
- 4
- 主要终点
- Satisfaction with the consultation
研究概览
简要总结
This multicenter cluster-randomized study evaluates the impact of an artificial intelligence (AI) tool on the satisfaction of healthcare professionals and patients in outpatient consultations, measuring its effect on perceived satisfaction (through a visual analog scale), the duration of consultations, and the quality and quantity of clinical data recorded. Adult patients (18-80 years) seen in outpatient centers will participate, comparing those using the AI tool with centers following the usual procedure. The tool is expected to reduce the administrative burden, improve user satisfaction and increase the efficiency and quality of the clinical registry. Recruitment will take place between December 2024 and May 2025, with final analysis planned for the end of 2025.
详细描述
This multicenter cluster-randomized study aims to evaluate the impact of an artificial intelligence (AI) tool designed to optimize real-time clinical registration during outpatient consultations. Its effect on patient and healthcare professional satisfaction will be analyzed, measured using a visual analog scale (VAS) and validated tools such as the Patient Experience Questionnaire (PEQ) and the Net Promoter Score (NPS). In addition, the duration of consultations and the quantity and quality of clinical data recorded in the intervention and control groups will be compared. The intervention group will use the AI tool, while the control group will continue with the usual recording without AI. Participants will be adult patients (18-80 years) seen in health centers linked to the study, recruited by prior informed consent. AI is expected to reduce the administrative burden on professionals, allowing them to devote more time to direct care, improving both the quality of the clinical record and the patient experience. Recruitment will take place between December 2024 and May 2025, and will follow the ethical guidelines set out in the Declaration of Helsinki. This project seeks to provide evidence on the implementation of AI-based technologies in the outpatient setting and their impact on the quality of healthcare.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Health Services Research
- 盲法
- Triple (Participant, Investigator, Outcomes Assessor)
盲法说明
The masking is single-blind and patient-centered, which is appropriate for this type of intervention where it is not feasible to blind healthcare professionals. This helps to reduce bias in measurements related to patient satisfaction and experience.
入排标准
- 年龄范围
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients between 18 and 80 years of age.
- •Patients consulting for any health reason in the outpatient clinics of the centers participating in the study.
- •Patients who sign the informed consent to participate in the study.
排除标准
- •Patients who are unable to understand or complete the questionnaires, due to:
- •Language barriers.
- •Cognitive disabilities.
- •Any other reason that prevents their adequate participation.
- •Patients who are currently participating in other clinical trials or research studies that may interfere with the results of this study.
结局指标
主要结局
Satisfaction with the consultation
时间窗: From enrrolment to the end of the consultation the same day.
measured using a 10 cm Visual Analog Scale (VAS) of satisfaction, which assesses the degree of satisfaction perceived by patients and health professionals. From no satisfaction in the left side to Completely satisfied in the right side.
次要结局
- Duration of the consultation(From enrrolment to the end of the consultation the same day.)
- Number of clinical data recorded(From enrrolment to the end of the consultation the same day.)
- Patient Experience (Patient Expectation Questionnaire - PEQ)(at the begining and at the end of the consultation)
- Likelihood of recommendation (Net Promoter Score - NPS)(From enrrolment to the end of the consultation the same day.)
研究者
Raúl Ferrer-Peña
Dr. in Pain Research
Universidad Autonoma de Madrid