Primary CareNCT07019116
Efficacy of Artificial Intelligence for Gatekeeping in Referrals to Specialized Care
A study testing Standard gatekeeping, AI algorithm and Subsequent interactions between primary care and regulation system in people with Primary Care and Primary Care Patients With Chronic Conditions, run by Hospital de Clinicas de Porto Alegre at 1 site in Brazil.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Primary Care, Primary Care Patients With Chronic Conditions
- What is being tested?
- Standard gatekeeping, AI algorithm and Subsequent interactions between primary care and regulation system
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 934 participants
- Where
- 1 siteBrazil
- Study dates
- 15 Nov 2025to Dec 2029
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Who may be able to join
- All referrals from a given specialty (waitlist) will be eligible.
- Specialties will be selected following Rio Grande do Sul Health Department priorities.
Who cannot join
- 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 locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Brazil1 site, 1 recruiting
- Recruiting
Central de Regulação Ambulatorial
Porto Alegre, Rio Grande do Sul
Natan Katz, MD, PhD, +55513308-2092, nkatz@hcpa.edu.br
About this study
Official title: Efficacy of an Artificial Intelligence Algorithm for Gatekeeping in Referrals From Primary Care to Specialized Care: a Randomized Controlled Trial
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.
- Being tested
- Standard gatekeeping, AI algorithm, Subsequent interactions between primary care and regulation system
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Standard gatekeepingOther
Human evaluators (mostly physicians) review referrals and determine, based on established protocols, whether they should be authorized.
AI algorithmOther
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.
Subsequent interactions between primary care and regulation systemOther
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.
| Study group | Role | What they receive |
|---|---|---|
| Standard gatekeeping process | Active comparator | In standard gatekeeping, the current process will be used without interventions. Receives: 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. Receives: Other: AI algorithm; Other: Subsequent interactions between primary care and regulation system |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 6 months
Secondary outcomes (4)
Time to final decision
Time to final decision (authorization or deferral) for the referral.
Measured: 6 months
Time to consult in high-risk patients
Time to specialist appointment for high-priority (red/orange) cases.
Measured: 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.
Measured: 6 months
Waitlist size over time
The overall size of the referral waitlist will be assessed before and after the implementation of the algorithm.
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Jun 2025
- Study start15 Nov 2025
- Primary completionDec 2028 (estimated)
- Study completionDec 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Primary CareStudies of the same condition, open and under way.
- More trials run by Hospital de Clinicas de Porto AlegreThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 934 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Hospital de Clinicas de Porto Alegre
- Sponsor type
- University, hospital or other
- Collaborators
- Rio Grande do Sul State Health Department - SES/RS
- Responsible party
- Sponsor
- Investigators
- Dimitris V. Rados, Ph.D., Principal investigator, TelessaúdeRS
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07019116
- Sponsor's protocol ID
- 2018-0589
- Status last verified
- Feb 2026
- First submitted
- 5 Jun 2025
- Last updated
- 20 Feb 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- 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.
Study at a glance
From the registry record, last updated by the sponsor on 20 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Efficacy of an Artificial Intelligence Algorithm for Gatekeeping in Referrals From Primary Care to Specialized Care: a Randomized Controlled Trial
- ClinicalTrials.gov ID
- NCT07019116
- Acronym / sponsor ID
- 2018-0589
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 934 participants (estimated)
- Lead sponsor
- Hospital de Clinicas de Porto AlegreUniversity, hospital or other
- Collaborators
- Rio Grande do Sul State Health Department - SES/RS
- Study start
- 15 Nov 2025 (actual)
- Primary completion
- Dec 2028 (estimated)
- Study completion
- Dec 2029 (estimated)
- First posted
- 13 Jun 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Standard gatekeeping process | Active comparator | Other: Standard gatekeeping, Other: Subsequent interactions between primary care and regulation systemIn standard gatekeeping, the current process will be used without interventions. |
| Artificial Intelligence for Gatekeeping | Experimental | Other: AI algorithm, Other: Subsequent interactions between primary care and regulation systemAn AI algorithm will perform the first evaluation (triaging) of the referral. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (2 listed)
- All referrals from a given specialty (waitlist) will be eligible.
- Specialties will be selected following Rio Grande do Sul Health Department priorities.
Key exclusion criteria (2 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Standard gatekeepingHuman evaluators (mostly physicians) review referrals and determine, based on established protocols, whether they should be authorized. | Other | Standard gatekeeping process |
| AI algorithmAn 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. | Other | Artificial Intelligence for Gatekeeping |
| Subsequent interactions between primary care and regulation systemAfter 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. | Other | Artificial Intelligence for Gatekeeping, Standard gatekeeping process |
Primary outcome
Referrals with final decision
Time frame: 6 months
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.
4 secondary outcomes
Time to final decision
Time frame: 6 months
Time to consult in high-risk patients
Time frame: 6 months
Use of remote consultations
Time frame: 6 months
Waitlist size over time
Time frame: 6 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Feb 2026.
- Individual participant dataSharing planned: 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Dimitris V. Rados, Ph.D. principal investigator
TelessaúdeRS
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Standard gatekeeping, AI algorithm and Subsequent interactions between primary care and regulation system in people with Primary Care and Primary Care Patients With Chronic Conditions, run by Hospital de Clinicas de Porto Alegre at 1 site in Brazil.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 20 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Brazil. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Dimitris V Rados, Ph.D. (+555133082092, drados@hcpa.edu.br), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Referrals with final decision (time frame: 6 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Feb 2026.