DR. ANNA MAGDALENE JOSEPH,DR. LAKSHMI
DOI: https://doi.org/Background: Artificial intelligence (AI) continues to expand its role in healthcare, including anesthesia. Pre-anesthetic evaluations are crucial for assessing patient risk, but language barriers and limited digital accessibility can impede accurate data collection. This study investigated the impact of an AI-powered language translator and processor app on the efficiency, accuracy, and patient satisfaction during pre-anesthetic assessments.
Methods: A prospective cohort study was conducted at Saveetha Medical College and Hospital with 60 patients who primarily spoke languages other than the provider’s. Participants underwent standard and app-assisted pre-anesthetic evaluations. Key metrics included evaluation time, completeness of history, allergy/comorbidity identification, and patient satisfaction. Statistical analyses involved ANOVA, chi-square tests, and Pearson’s correlation.
Results: App-assisted evaluations significantly reduced average time from 27.05 ± 2.21 to 15.98 ± 1.86 minutes (p < 0.001), saving 10.85 minutes per session. Missed history items dropped from 85 to 24, reflecting a 68.6% improvement in accuracy. Allergies were documented in 76.67% and comorbidities in 70% of cases. Patient satisfaction was high (3.67 ± 0.79), with a strong positive correlation to app usage (R = 0.64, p < 0.001).
Conclusion: The AI-powered translator app enhanced the quality and efficiency of pre-anesthetic assessments, especially for linguistically diverse populations. Its implementation supports improved patient safety, engagement, and workflow efficiency. Further research should evaluate its scalability, integration with hospital systems, and broader clinical applications.
