Artificial Intelligence Satisfaction in Professionals and Patients

NCT06726447 | Recruiting

• 1 other identifier: sAItisfACES - IA satisfaction
• Study Type: interventional
• Target: 148
• Locations: 1 country
• Sites: 2

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.

Conditions

General Health Status

Keywords

Artificial Intelligence; Patient Satisfaction; Medical Records Systems, Computerized; Health Care Quality, Access, and Evaluation

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (4)

• 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.

Other Outcomes (7)

• Sociodemographic variables

  at the begining of the consultation, Just after enrrollment.

• Sociodemographic variables

  at the begining of the consultation, Just after enrrollment.

• Sociodemographic variables

  at the begining of the consultation, Just after enrrollment.

Study Arms (2)

Artificial Intelligence Tool

EXPERIMENTAL

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

Standard of care

NO INTERVENTION

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.

Interventions

Artificial Intelligence Tool DEVICE

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.

Artificial Intelligence Tool

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Universidad Autonoma de Madrid: lead

Study Sites (2)

ACES Centers

Barcelona, Catalonia, 28500, Spain

RECRUITING

CSEU La Salle - UAM

Madrid, Madrid, 28018, Spain

NOT YET RECRUITING

Related Publications (1)

• 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.

  PMID: 30617339 BACKGROUND

MeSH Terms

Conditions

Patient Satisfaction

Condition Hierarchy (Ancestors)

Treatment Adherence and Compliance; Health Behavior; Behavior

Study Officials

• Raul Ferrer, PhD

  Senior Lecturer and Investigator

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Raúl Ferrer-Peña, PhD

CONTACT

+34607712148; raul.ferrer@salud.madrid.org

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: TRIPLE
• Who Masked: PARTICIPANT, INVESTIGATOR, OUTCOMES ASSESSOR
• Masking Details: 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.
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Dr. in Pain Research

Model Details: Clusters

Study Record Dates

• First Submitted: November 26, 2024
• First Posted: December 10, 2024
• Study Start: December 1, 2024
• Primary Completion: June 30, 2025
• Study Completion (Estimated): December 1, 2026
• Last Updated: February 27, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

ES (2)