An interventional study of Standard gatekeeping and AI algorithm in Primary Care and Primary Care Patients With Chronic Conditions, sponsored by Hospital de Clinicas de Porto Alegre. Recruiting at 1 site in Brazil. Per ClinicalTrials.gov, last updated 2026-02-20.
Sponsored by Hospital de Clinicas de Porto Alegre · Not applicable, Interventional, and Health services research
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.
Hospital de Clinicas de Porto Alegre is the lead sponsor of 450 studies on the registry; 61 are open to participants now.
Counted across the registry records on this site, refreshed daily.
Exclusion Criteria:
In standard gatekeeping, the current process will be used without interventions.
Other: Standard gatekeeping · Other: Subsequent interactions between primary care and regulation system
An AI algorithm will perform the first evaluation (triaging) of the referral.
Other: AI algorithm · Other: Subsequent interactions between primary care and regulation system
Human evaluators (mostly physicians) review referrals and determine, based on established protocols, whether they should be authorized.
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.
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.
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.
Time frame: 6 months
Time to final decision
Time to final decision (authorization or deferral) for the referral.
Time frame: 6 months
Time to consult in high-risk patients
Time to specialist appointment for high-priority (red/orange) cases.
Time frame: 6 months
Use of remote consultations
Rio Grande do Sul has a provider-to-provider consultation service. The proportion of referrals that used this service will be assessed.
Time frame: 6 months
Waitlist size over time
The overall size of the referral waitlist will be assessed before and after the implementation of the algorithm.
Time frame: 6 months
Plan to share: Undecided — Final data responsibility lies with the Rio Grande do Sul State Health Department, and the data are classified as high-risk health data under the Brazilian General Data Protection Law.
No publications or documents are linked to this record.
Eligibility is decided by the study team. Share this record with your doctor or contact the team directly.
Contact study teamGet an email when the registry record changes — status, dates, results — or when someone posts here.
Sign in to followQuestions and observations about this study, from anyone following it. Not medical advice, and not a channel to the study team — their contact details are on the registry record.
Sign in to join the discussion. Reading takes no account; posting does. You choose a display name, and a pseudonym is the default.
Nothing here yet. If you are running this trial, taking part in it, or weighing whether to, this is the place to say so.
Hospital de Clinicas de Porto Alegre