Artificial Intelligence Satisfaction in Professionals and Patients (sAItisfACES)

sAItisfACES - Impact of an Artificial Intelligence Tool on the Satisfaction of Professionals and Patients in Ambulatory Consultations: A Randomized Cluster Study

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.

Study Overview

• Status: Recruiting
• Conditions: General Health Status
• Intervention / Treatment: Device: Artificial Intelligence Tool

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.

• Study Type: Interventional
• Enrollment (Estimated): 148
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Raúl Ferrer-Peña, PhD; Phone Number: +34607712148; Email: raul.ferrer@salud.madrid.org

Study Locations

• Spain
  • Catalonia
    • Barcelona, Catalonia, Spain, 28500
      • Recruiting
      • ACES Centers
      • Contact: Raúl Ferrer Peña; Phone Number: 607712148; Email: drraulferrer@gmail.com
  • Madrid
    • Madrid, Madrid, Spain, 28018
      • Not yet recruiting
      • CSEU La Salle - UAM
      • Contact: Raúl Ferrer-Peña, PhD; Phone Number: +34607712148; Email: raul.ferrer@lasallecampus.es

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

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.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Health Services Research
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Triple

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Artificial Intelligence Tool; The health centers assigned to this group will implement an artificial intelligence (AI) tool for clinical registration during outpatient consultations. Healthcare professionals will activate the tool at the beginning of the consultation and deactivate it at the end. The tool will be used to document interactions in real time, generating more detailed records and reducing the administrative burden.	Device: Artificial Intelligence Tool; The intervention in this study consists of the implementation of an artificial intelligence tool for clinical registration during outpatient consultations. This technology facilitates the documentation of interactions in real time, optimizing the workflow of professionals and enabling more patient-centered care.
No Intervention: Standard of care; In the centers assigned to this group, consultations will be carried out in the usual way, using traditional clinical recording methods, without additional technological intervention. This group will serve as a reference to compare differences in satisfaction, duration of consultations and quality of the clinical record.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Satisfaction with the consultation	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.	From enrrolment to the end of the consultation the same day.

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Duration of the consultation	Total time of the consultation, measured manually from the time of entry to the time of departure of the patient.	From enrrolment to the end of the consultation the same day.
Number of clinical data recorded	Total number of words documented in the clinical history generated during the consultation, excluding headings	From enrrolment to the end of the consultation the same day.
Patient Experience (Patient Expectation Questionnaire - PEQ)	Assessment of selected domains of the Patient Expectation Questionnaire (Health Service Process and Professional-Patient Communication). Format: 5-point Likert scale.	at the begining and at the end of the consultation
Likelihood of recommendation (Net Promoter Score - NPS)	Patient's assessment of the likelihood of recommending the service received. Range: 0 (very unlikely) to 10 (very likely).	From enrrolment to the end of the consultation the same day.

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Sociodemographic variables	Age (years)	at the begining of the consultation, Just after enrrollment.
Sociodemographic variables	Sex (Male/Female)	at the begining of the consultation, Just after enrrollment.
Sociodemographic variables	Nationality (Spanish/Other).	at the begining of the consultation, Just after enrrollment.
Sociodemographic variables	Municipality of residence (Rural/Non-rural).	at the begining of the consultation, Just after enrrollment.
Sociodemographic variables	Marital status (Single, Married, Divorced, Widowed).	at the begining of the consultation, Just after enrrollment.
Sociodemographic variables	Employment status (Active/Not active/Pensioner/Employed/Other).	at the begining of the consultation, Just after enrrollment.
Sociodemographic variables	Educational level (None, Primary, Secondary, Vocational training, University, Doctorate).	at the begining of the consultation, Just after enrrollment.

Collaborators and Investigators

• Sponsor: Universidad Autonoma de Madrid
• Investigators: Principal Investigator: Raul Ferrer, PhD, Senior Lecturer and Investigator

Publications and helpful links

General Publications

• 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.

Study record dates

Study Major Dates

• Study Start (Actual): December 1, 2024
• Primary Completion (Actual): June 30, 2025
• Study Completion (Estimated): December 1, 2026

Study Registration Dates

• First Submitted: November 26, 2024
• First Submitted That Met QC Criteria: December 9, 2024
• First Posted (Actual): December 10, 2024

Study Record Updates

• Last Update Posted (Actual): February 27, 2026
• Last Update Submitted That Met QC Criteria: February 25, 2026
• Last Verified: February 1, 2026

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Health Care Quality, Access, and Evaluation; Patient Satisfaction; Medical Records Systems, Computerized
• Additional Relevant MeSH Terms: Behavior; Treatment Adherence and Compliance; Health Behavior; Patient Satisfaction
• Other Study ID Numbers: sAItisfACES - IA satisfaction

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: The study data will not be shared to ensure the confidentiality and privacy of the participants, in accordance with current regulations such as the General Data Protection Regulation (GDPR). In addition, sensitive health-related data are handled, the disclosure of which could compromise the privacy of individuals. Although the data will be anonymized, the team has decided to limit its access to authorized personnel only in order to protect the integrity of the study and minimize ethical or legal risks associated with the distribution of personal information.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No