Improving scheduling of emergency physicians using data mining analysis

Emergency departments are the first line in hospitals to face emergency patients. As a major function of emergency medicine, when a patient comes to the emergency department, the emergency medical personnel will first perform a triage procedure and then transfer the patient to associated departments for treatment. Due to the utilization pattern of the Taiwanese people in medicine, the emergency departments in most major hospitals are always overcrowded. The arrangement of manpower or the distribution of resources to handle patients’ demands can affect disease outcomes and quality of medical treatment. Therefore, the prediction of demands of physician manpower certainly will affect the quality and cost in medical treatment, and has significant impact on patients’ life and satisfaction. tudy used data mining, classification and a decision tree to analyze the prediction model of patients’ demand in the Emergency department from real treatment situations. The result was the accuracy of shift anticipation improved from 22% to 50%. This study also used anticipant performance evaluation matrix integrated with loss function to evaluate the performance between the anticipation of demand established by mining and the original arrangement. It helped to save the cost of the medical personnel by 37%. In the end it combined the DMAIC action procedure from 6-Sigma and developed an anticipation model that can be suitable in different departments to dispatch medical personnel. It provided a reference of the decision maker of the hospital.