DOACT Algorithm Versus AI-Based Decision Models in Oral Anticoagulant Therapy for Vascular Patients

ITALO EUGENIO SOUZA GADELHA DE ABREU59 enrolled

Overview

Study using a decision algorithm for the application of an oral anticoagulant calculator in vascular diseases, aimed at validating a clinical decision-support tool for conditions such as deep vein thrombosis, superficial thrombophlebitis, and pulmonary thromboembolism.

Cross-sectional, three-arm comparative validation study evaluating the accuracy and clinical utility of the DOACT algorithm versus standard clinical decision-making and large language model (LLM)-based decision tools.

Study Type

INTERVENTIONAL

Allocation

RANDOMIZED

Purpose

SUPPORTIVE_CARE

Masking

SINGLE

Enrollment

Conditions

Deep Vein Thrombosis Superficial Thrombophlebitis Pulmonary Thromboembolisms Clinical Decision Support Systems Artificial Intelligence

Interventions

DOACT algorithmOTHER

Vascular and non-vascular physicians using DOACT (Dose-Oriented Anticoagulant Calculator for Evidence-Based Decision Tool) to recommend appropriate oral anticoagulant regimens-dose selection and duration responding 15 standardized clinical case vignettes representing patients with vascular diseases such as deep vein thrombosis (DVT), superficial thrombophlebitis, and pulmonary thromboembolism (PTE).

No algorithmOTHER

Vascular and non-vascular physicians using standard clinical decision-making (no use of algorithm) to recommend appropriate oral anticoagulant regimens-dose selection and duration responding 15 standardized clinical case vignettes representing patients with vascular diseases such as deep vein thrombosis (DVT), superficial thrombophlebitis, and pulmonary thromboembolism (PTE).

LLM-based toolsOTHER

Eligibility

Sex: ALLMin age: 18 YearsMax age: 89 YearsHealthy volunteers:

Medical Language ↔ Plain English

Inclusion Criteria * Physicians with residency training in Vascular Surgery or official Board Certification in Vascular Surgery. * Currently practicing clinical and/or surgical vascular care in Brazil. * Completed the informed consent process (TCLE) and voluntarily agreed to participate. Exclusion Criteria * Physicians without formal Vascular Surgery residency and without Board Certification. * Physicians not performing vascular clinical or surgical care (e.g., exclusively administrative, academic, or non-assistance roles). * Less than 1 year of professional experience after medical school graduation. * Did not sign or did not fully complete the TCLE. Large Language Models (LLMs) * Inclusion Criteria * Free-access LLMs available to the public at the time of data collection. * All responses generated using the same standardized prompt. * Capable of producing complete, text-based clinical answers relevant to vascular surgery decision-making. Exclusion Criteria * Paid or subscription-based LLMs. * LLMs requiring institutional licenses, restricted access, or proprietary tokens. * Models unable to generate full responses to the standardized prompt.

Locations (1)

Irmandade da Santa Casa de Misericórdia de São Paulo

São Paulo, São Paulo, Brazil

Outcomes

Primary Outcomes

Accuracy of the DOACT Algorithm in Guiding Oral Anticoagulant Therapy

Accuracy of anticoagulation recommendations Description: Proportion of correct responses generated by the four evaluated LLMs, vascular surgeons, and non-vascular physicians, with and without access to the DOACT algorithm, using standardized clinical vignettes.

Time frame: Day 1

Accuracy of anticoagulation recommendations

Proportion of correct responses generated by LLMs, vascular surgeons, and non-vascular physicians with and without access to the DOACT algorithm. All LLM outputs will be generated using the same standardized prompt, following methodological guidance recommended by IBM for evaluating large language models.

Time frame: Day 1

Data from ClinicalTrials.gov

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