Clinical trial NCT07019116: Efficacy of Artificial Intelligence for Gatekeeping in Referrals to Specialized Care
Official title: Efficacy of an Artificial Intelligence Algorithm for Gatekeeping in Referrals From Primary Care to Specialized Care: a Randomized Controlled Trial
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Primary Care
Primary Care Patients With Chronic Conditions
Lead sponsor: Hospital de Clinicas de Porto Alegre
Sponsor class: OTHER
Collaborators: name: Rio Grande do Sul State Health Department - SES/RS
First posted: 2025-06-13
Enrollment: 934
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: HEALTH_SERVICES_RESEARCH

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.

We 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 municipalties in Rio Grande do Sul. Specialties to be included will be selected by the State Health Department prospectivelly 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 managing the demand. For every AI prediction, the response is a probability from 0 to 1 of authorization (or deferring). For implementation this 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.


Eligibility criteria: 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.

Primary outcomes:
measure: Referrals with final decision
timeFrame: 6 months
Secondary outcomes:
measure: Time to final decision
timeFrame: 6 months
measure: Time to consult in high-risk patients
timeFrame: 6 months
measure: Use of remote consultations
timeFrame: 6 months
measure: Waitlist size over time
timeFrame: 6 months
Other outcomes:
Locations:
Study officials:
affiliation: TelessaúdeRS
name: Dimitris V. Rados, Ph.D.
role: PRINCIPAL_INVESTIGATOR
References: