Clinical trial NCT06726447: Artificial Intelligence Satisfaction in Professionals and Patients
Official title: sAItisfACES - Impact of an Artificial Intelligence Tool on the Satisfaction of Professionals and Patients in Ambulatory Consultations: A Randomized Cluster Study
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: General Health Status
Keywords: Artificial Intelligence
Patient Satisfaction
Medical Records Systems, Computerized
Health Care Quality, Access, and Evaluation
Lead sponsor: Universidad Autonoma de Madrid
Sponsor class: OTHER
First posted: 2024-12-10
Enrollment: 148
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: Clusters
Primary purpose: HEALTH_SERVICES_RESEARCH

Brief summary: 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.

Detailed description: 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.

Eligibility criteria: Inclusion Criteria:

* 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.

Exclusion Criteria:

* 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.

Primary outcomes:
measure: Satisfaction with the consultation
timeFrame: From enrrolment to the end of the consultation the same day.
Secondary outcomes:
measure: Duration of the consultation
timeFrame: From enrrolment to the end of the consultation the same day.
measure: Number of clinical data recorded
timeFrame: From enrrolment to the end of the consultation the same day.
measure: Patient Experience (Patient Expectation Questionnaire - PEQ)
timeFrame: at the begining and at the end of the consultation
measure: Likelihood of recommendation (Net Promoter Score - NPS)
timeFrame: From enrrolment to the end of the consultation the same day.
Other outcomes:
measure: Sociodemographic variables
timeFrame: at the begining of the consultation, Just after enrrollment.
measure: Sociodemographic variables
timeFrame: at the begining of the consultation, Just after enrrollment.
measure: Sociodemographic variables
timeFrame: at the begining of the consultation, Just after enrrollment.
measure: Sociodemographic variables
timeFrame: at the begining of the consultation, Just after enrrollment.
measure: Sociodemographic variables
timeFrame: at the begining of the consultation, Just after enrrollment.
measure: Sociodemographic variables
timeFrame: at the begining of the consultation, Just after enrrollment.
measure: Sociodemographic variables
timeFrame: at the begining of the consultation, Just after enrrollment.
Locations:
city: Barcelona
country: Spain
facility: ACES Centers
state: Catalonia
city: Madrid
country: Spain
facility: CSEU La Salle - UAM
Study officials:
affiliation: Senior Lecturer and Investigator
name: Raul Ferrer, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019 Jan;25(1):44-56. doi: 10.1038/s41591-018-0300-7. Epub 2019 Jan 7.