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The goal of this clinical trial is to learn whether access to an artificial intelligence (AI) clinical decision support assistant can improve diagnostic accuracy during real-world telemedicine consultations among primary care physicians in El Salvador.
The main questions it aims to answer are:
Researchers will compare physicians with the AI assistant enabled to physicians with the AI assistant temporarily disabled to see if access to AI improves diagnostic accuracy.
Participants (physicians) will:
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| AI Disabled (Standard Telemedicine) | Experimental | Physicians in this arm will conduct telemedicine consultations with the AI assistant features temporarily disabled. They will perform the standard clinical workflow without automated support for history taking or diagnostic suggestions. This arm represents the removal of the AI tool to measure its impact. |
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| AI Enabled (AI-Assisted Telemedicine) | Active Comparator | Physicians in this arm will conduct telemedicine consultations with full access to the DoctorSV AI assistant. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| DoctorSV AI Assistant | Device | An AI tool integrated into the telemedicine platform, built on Google's Gemini Large Language Models (LLMs). The system operates via two modules: (1) a clinical history assistant that supports structured documentation of patient information in real-time and (2) a pre-diagnosis tool that analyzes documented clinical data to generate differential diagnosis suggestions for the physician's consideration. The model uses contextual prompting to ensure suggestions are culturally and clinically appropriate for El Salvador. |
| Measure | Description | Time Frame |
|---|---|---|
| Diagnostic Accuracy | The proportion of consultations where the primary diagnosis recorded by the participating physician matches the "gold standard" reference diagnosis. The reference diagnosis is established by a panel of three independent, blinded expert evaluators reviewing the anonymized clinical notes. A diagnosis is considered "correct" (value = 1) if it matches the reference diagnosis within the same clinically equivalent diagnostic group; otherwise, it is considered "incorrect" (value = 0). The analysis will compare the proportion of correct diagnoses between the AI-enabled and AI-disabled arms. | Through study completion, ~ 12-16 weeks |
| Measure | Description | Time Frame |
|---|---|---|
| Diagnostic Concordance | The level of agreement between the physician's diagnosis and the expert reference diagnosis, measured using Cohen's Kappa coefficient. This measure evaluates the reliability of the diagnoses beyond simple percentage agreement, accounting for agreement occurring by chance. | Through study completion, ~12-16 weeks |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Principal Investigator | Contact | 212-203-3323 | stella@saglobalhealth.org |
| Name | Affiliation | Role |
|---|---|---|
| Manuel Bello, MD | Hospital Nacional El Salvador | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Hospital Nacional El Salvador | San Salvador | El Salvador |
The data will not be sent or shared to servers external to those used by the DoctorSV platform to ensure security and privacy of the participants (physicians) as well as the patients. The data is restricted to the research team and the specific objectives of this study.
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Because participants are assigned to one of two arms and remain in that arm throughout the trial, this is a parallel study model.
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| Standard Telemedicine Workflow (No AI) | Other | Standard primary care consultation via videocall without the assistance of artificial intelligence tools. Physicians rely solely on their own clinical judgment and manual documentation without automated summaries or diagnostic prompts. |
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| Diagnostic Accuracy Stratified by Physician Experience Level |
Evaluation of diagnostic accuracy (proportion of correct diagnoses) compared between subgroups of physicians with "High Experience" (≥1 year in the program or ≥20 consultations) versus "Low Experience" (<1 year in the program or <20 consultations). |
| Through study completion, ~12-16 weeks |
| Diagnostic Accuracy Stratified by Clinical System | The proportion of correct diagnoses stratified by the physiological system of the pathology: Respiratory, Digestive, Urinary, or Ophthalmic. This outcome assesses if the AI's performance or utility varies depending on the specific type of clinical condition. | Through study completion, ~12-16 weeks. |