This prospective observational cohort study aims to validate an artificial intelligence (AI) tool designed for pre-anesthetic assessment in Portuguese, tailored to the Brazilian healthcare context. Conducted at a single tertiary hospital, the study will enroll 270 adult patients (aged \>18 years) scheduled for elective non-cardiac surgeries. Participants will use the AI tool to complete a self-assessment, generating general patient guidance and a detailed medical evaluation (the latter withheld from the anesthesiologist). A standard pre-anesthetic evaluation will then be performed by an anesthesiologist blinded to the AI results. A third blinded anesthesiologist will compare the assessments for accuracy, consistency, and risk identification (e.g., ASA classification and perioperative risk models). Primary outcome is concordance between AI and human assessments using Cohen's Kappa. Secondary outcomes include anesthesiologist perceptions of the tool's utility, impact on assessment quality, and patient usability challenges. The study poses minimal risks, with data collected over 24 months, and aims to enhance perioperative safety and efficiency in Brazil.
Patients aged 18 years or older
Patients scheduled for elective non-cardiac surgeries
Patients undergoing diagnostic procedures with isolated sedation or local anesthesia
If a patient undergoes more than one surgical intervention during the same hospitalization, only the major procedure will be considered (i.e., additional procedures during the same admission are not eligible for separate inclusion).
The study population consists of adult patients (aged ≥ 18 years) scheduled for elective non-cardiac surgeries at two tertiary hospitals in Brazil. This population represents a typical cohort in a Brazilian public health system context, including individuals with varying comorbidities and surgical needs, such as those arising from the backlog of procedures in the Unified Health System (SUS) due to the COVID-19 pandemic. The focus is on patients requiring pre-anesthetic assessment for non-emergent, non-cardiac interventions, reflecting common surgical demands in digestive, genitourinary, circulatory, and upper airway/head/neck procedures. The estimated sample size is 270 participants to ensure adequate statistical power for evaluating the concordance between AI-based and human pre-anesthetic assessments.
Cohort
Prospective
AI-based pre-anesthetic assessment
Other interventionPatients use an artificial intelligence (AI) tool based on a Large Language Model (LLM) in Portuguese to complete a pre-anesthetic self-assessment. The tool collects patient information and generates two outputs: Generic Orientations: General instructions sent directly to the patient to aid in surgical preparation. Specific Assessment: A detailed evaluation, including recommendations and warning signs for medical use, which is not shared with the anesthesiologist performing the traditional evaluation.
Anesthesiologist-led pre-anesthetic evaluation
Other interventionEach patient undergoes a standard pre-anesthetic evaluation conducted by an anesthesiologist, following routine clinical practice at both institutions. This evaluation is performed without access to the AI tool's results to ensure blinding. Purpose: To serve as the comparator for the AI-based assessment, allowing evaluation of concordance in risk assessment, quality of information collected, and clinical judgment. Details: The anesthesiologist conducts a clinical interview and review of medical records, assessing factors such as the American Society of Anesthesiologists (ASA) classification, perioperative risk models (for instance, Ex-Care model), and potential complications.
Preoperative Risk Assessment
The primary outcome is the level of concordance between the preoperative risk assessments performed by the artificial intelligence (AI) tool, based on a Large Language Model (LLM) in Portuguese, and those conducted by a human anesthesiologist. This concordance is evaluated in terms of the quality of information collected (e.g., completeness and relevance of patient data) and the precision of clinical judgment (e.g., identification of perioperative risks, American Society of Anesthesiologists (ASA) classification, and alignment with validated surgical risk models such as Ex-Care). Measurement Method: A third anesthesiologist, blinded to both the AI and human evaluations, will compare the assessments. The concordance will be quantified using the Cohen's Kappa coefficient for categorical variables (e.g., quality of information and clinical judgment).
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