General Health Status
Conditions
Keywords
Artificial Intelligence, Patient Satisfaction, Medical Records Systems, Computerized, Health Care Quality, Access, and Evaluation
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.
Interventions
DEVICEArtificial 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.
Sponsors
Universidad Autonoma de Madrid
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
TRIPLE (Subject, Investigator, Outcomes Assessor)
Masking description
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.
Intervention model description
Clusters
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Satisfaction with the consultation | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Duration of the consultation | From enrrolment to the end of the consultation the same day. | Total time of the consultation, measured manually from the time of entry to the time of departure of the patient. |
| Number of clinical data recorded | From enrrolment to the end of the consultation the same day. | Total number of words documented in the clinical history generated during the consultation, excluding headings |
| Patient Experience (Patient Expectation Questionnaire - PEQ) | at the begining and at the end of the consultation | Assessment of selected domains of the Patient Expectation Questionnaire (Health Service Process and Professional-Patient Communication). Format: 5-point Likert scale. |
| Likelihood of recommendation (Net Promoter Score - NPS) | From enrrolment to the end of the consultation the same day. | Patient's assessment of the likelihood of recommending the service received. Range: 0 (very unlikely) to 10 (very likely). |
Countries
Spain
Contacts
CONTACTRaúl Ferrer-Peña, PhD
PRINCIPAL_INVESTIGATORRaul Ferrer, PhD
Senior Lecturer and Investigator
Outcome results
None listed