In healthcare, the emphasis on patient safety and the minimization of medical errors cannot be overstated. Despite concerted efforts, many healthcare systems, especially in low-resource regions, still grapple with preventing these errors effectively. This study explores a pioneering application aimed at addressing this challenge by assisting caregivers in gauging potential risks derived from medical notes. The application leverages data from openFDA, delivering real-time, actionable insights regarding prescriptions. Preliminary analyses conducted on the MIMIC-III \cite{mimic} dataset affirm a proof of concept highlighting a reduction in medical errors and an amplification in patient safety. This tool holds promise for drastically enhancing healthcare outcomes in settings with limited resources. To bolster reproducibility and foster further research, the codebase underpinning our methodology is accessible on https://github.com/autonlab/2023.hackAuton/tree/main/prescription_checker. This is a submission for the 30th HackAuton CMU.

翻译：在医疗领域，患者安全与减少医疗差错的重要性不言而喻。尽管各方已付出诸多努力，但许多医疗系统（尤其是资源匮乏地区）仍难以有效预防此类差错。本研究探索一项创新应用，旨在通过辅助医护人员评估病历中潜在风险来应对该挑战。该应用利用openFDA数据，为处方提供实时可操作的见解。基于MIMIC-III \cite{mimic}数据集进行的初步分析证实了概念验证的有效性，表明其能显著减少医疗差错并提升患者安全。这一工具有望在资源有限的环境中大幅改善医疗成效。为促进可重复性并推动后续研究，支撑本方法的核心代码已开源，访问地址：https://github.com/autonlab/2023.hackAuton/tree/main/prescription_checker。本文为第30届卡内基梅隆大学HackAuton竞赛的投稿。