Efficacy of Artificial Intelligence for Gatekeeping in Referrals to Specialized Care

NCT07019116 | Recruiting

• 1 other identifier: 2018-0589
• Study Type: interventional
• Target: 934
• Locations: 1 country
• Sites: 1

Brief Summary

In Rio Grande do Sul, Brazil, the demand for specialty care referrals has increased sharply with the adoption of the electronic regulatory system, especially in rural areas. In 2023 alone, over 79,000 referrals were submitted monthly, totaling 1.7 million annual gatekeeping decisions. Due to workforce limitations, nearly 70% of referrals are authorized automatically, often without clinical validation. This leads to delays for high-risk patients, unnecessary specialist visits, and a growing backlog, currently over 172,000 pending referrals. To address this, an AI algorithm was developed to triage referrals based on urgency and appropriateness. The investigators propose a prospective controlled study with randomized implementation of the AI tool across selected specialty queues in the electronic referral system. The population will consist of referrals from specialties waitlists from municipalities in Rio Grande do Sul. Specialties to be included will be selected by the State Health Department prospectively according to gatekeeping needs. The intervention will be an AI-based triage algorithm. The control will be a standard gatekeeping process. The primary outcome is the proportion of referrals with a final decision (authorized or redirected to primary care) within six months; secondary outcomes include time to decision and appointment, system-level performance metrics. Referrals will be randomly assigned to algorithmic or human gatekeeping with a 1:1 ratio. The algorithm classifies referrals into two groups: not authorized (pending more data or teleconsultation), authorized. Authorization cases are further divided into routine and high-risk referrals to help the manage demand. Each AI prediction provides a probability from 0 to 1 of authorization (or deferring). The implementation threshold is set at 0.8; cases below this level will be classified as low confidence for decision and will not be included. According to the State Health Department's decisions, several referral lines are expected to be selected for the intervention. A sample size 934 (467 per arm) for each included specialty was calculated to detect a 1.2 relative risk for the primary outcome with 90% power and 5% significance.

Conditions

Primary Care; Primary Care Patients With Chronic Conditions

Outcome Measures

Primary Outcomes (1)

• Referrals with final decision

  The proportion of referrals with a final decision includes those authorized for specialist care and those redirected to primary care without an in-person specialist consultation.

  6 months

Secondary Outcomes (4)

• Time to final decision

  6 months

• Time to consult in high-risk patients

  6 months

• Use of remote consultations

  6 months

• Waitlist size over time

  6 months

Study Arms (2)

Standard gatekeeping process

ACTIVE COMPARATOR

In standard gatekeeping, the current process will be used without interventions.

Other: Standard gatekeeping; Other: Subsequent interactions between primary care and regulation system

Artificial Intelligence for Gatekeeping

EXPERIMENTAL

An AI algorithm will perform the first evaluation (triaging) of the referral.

Other: AI algorithm; Other: Subsequent interactions between primary care and regulation system

Interventions

Standard gatekeeping OTHER

Human evaluators (mostly physicians) review referrals and determine, based on established protocols, whether they should be authorized.

Standard gatekeeping process

AI algorithm OTHER

An AI algorithm was developed to perform the first evaluation (triaging) of the referrals inserted in the electronic referral system from the Rio Grande do Sul Health Department.

Artificial Intelligence for Gatekeeping

Subsequent interactions between primary care and regulation system OTHER

After the first evaluation of a referral, several subsequent rounds of interaction between gatekeepers and primary care physicians can be conducted to further detail patient needs and urgency.

Artificial Intelligence for Gatekeeping; Standard gatekeeping process

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• All referrals from a given specialty (waitlist) will be eligible.
• Specialties will be selected following Rio Grande do Sul Health Department priorities.

You may not qualify if:

• Referrals that the AI algorithm can not evaluate. These include referrals with attachments (further information in image or PDF files) and referrals with previous rounds of discussion.
• Referrals in which the algorithm has low confidence in the decision (i.e., informed data lead to a decision with a probability below 80%) will not be included in the study.

Sponsors & Collaborators

• Hospital de Clinicas de Porto Alegre: lead
• Rio Grande do Sul State Health Department - SES/RS: collaborator

Study Sites (1)

Central de Regulação Ambulatorial

Porto Alegre, Rio Grande do Sul, Brazil

RECRUITING

Study Officials

• Dimitris V. Rados, Ph.D.

  TelessaúdeRS

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Dimitris V Rados, Ph.D.

CONTACT

+555133082092; drados@hcpa.edu.br

Natan Katz, Ph.D.

CONTACT

+555133082092; nkatz@hcpa.edu.br

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 5, 2025
• First Posted: June 13, 2025
• Study Start: November 15, 2025
• Primary Completion (Estimated): December 1, 2028
• Study Completion (Estimated): December 1, 2029
• Last Updated: February 20, 2026

Record last verified: 2026-02

Locations

BR (1)