Shin Kong Wu Ho-Su Memorial Hospital
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2024 National Healthcare Quality Award
Highlights of Workload Reduction
By automating the analysis of complex medical records using Al, the system generates concise summaries and risk labels for clinical use, significantly reducing the workload of anesthesiologists during preoperative risk assessments.
According to survey results, the system saves an average of 2.1 minutes per patient. In a typical clinic session with 30 patients, this translates to over an hour saved on manual data review--time that can instead be devoted to patient communication and clinical decision-making, thereby improving the quality and efficiency of care. Among the 22 anesthesiologists surveyed, 64% reported that the system's automated alerts helped them identify critical patient conditions that might otherwise gone unnoticed.
Preoperative anesthesia risk assessment requires the integration of vast amounts of medical information. Traditional manual review is time-consuming and prone to missing critical details, increasing both medical risk and clinician workload. To address this, our hospital has developed the "Comprehensive Surgical Risk Assessment Support System," powered by Azure Open Al's large language model. The system automatically extracts key medical data and tags potential high-risk factors, generating up-to-date patient summaries that assist clinicians in quickly identifying conditions and potential risks, thereby enhancing communication efficiency and patient safety.
Source: 2025 Application of Digital Healthecare