Efficacy of Artificial Intelligence for Gatekeeping in Referrals to Specialized Care

Hospital de Clinicas de Porto Alegre

Status: Not yet enrolling

Conditions: Primary Care Patients With Chronic Conditions; Primary Care

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

Study type: Interventional

Funder types: Other

Identifiers

NCT07019116

2018-0589

Details and patient eligibility

About

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.

Enrollment: 934 estimated patients

Sex: All

Volunteers: No Healthy Volunteers

Inclusion criteria

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

Exclusion criteria

• 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.

Trial design

Primary purpose: Health Services Research

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: None (Open label)

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934 participants in 2 patient groups

Standard gatekeeping process

Active Comparator group

Description:

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

Treatment:

Other: Standard gatekeeping

Other: Subsequent interactions between primary care and regulation system

Artificial Intelligence for Gatekeeping

Experimental group

Description:

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

Treatment:

Other: AI algorithm

Other: Subsequent interactions between primary care and regulation system