A System Approach to Detect Medical Errors in Operational Data in Hospitals

Medical errors represent a significant challenge in healthcare systems worldwide, leading to increased patient morbidity, mortality, and healthcare costs. Early detection and prevention of such errors in hospital operational data can significantly improve patient safety and overall healthcare quality. This paper proposes a novel, data-driven approach to model a healthcare system for detecting medical errors using advanced machine learning techniques. We leverage electronic health records (EHR) and other hospital operational data sources to develop a comprehensive framework that can automatically identify potential errors in real-time. The model aims to identify patterns and anomalies in the data to detect potential errors and provide insights for process improvement. The proposed model can help healthcare providers to proactively monitor and address medical errors, thereby reducing the risk of harm to patients.