A Data-Driven Approach to Manage the Length of Stay for Appendectomy Patients

Skyrocketing patient-care costs demand that health-care institutions improve their resource-utilization effectiveness and efficiency. The length of an inpatient's stay has direct significant impacts on patient-care costs, service quality, and outcomes. Despite attempts to manage the length of stay (LOS) for frequently performed surgical procedures (e.g., appendectomies), many service providers cannot achieve the target range allowed by the managed care system. We take a data-driven approach to predict which appendectomy patients will likely have a LOS beyond that reimbursable by the underlying managed care system. We use a support vector machine to construct a generic prediction system and then extend that system by incorporating a resampling or cost-sensitive method to address the imbalanced sample problem. Using 557 appendectomy cases from a tertiary medical center in Taiwan, we examine the effectiveness of the generic prediction system compared with the effectiveness of its extensions. The results suggest the viability of a data-driven approach to manage LOS by enabling service providers to identify in advance those patients who will likely need extended stays. The comparative analyses also show the relative advantages of the oversampling and cost-sensitive methods for addressing the imbalanced sample problem. The findings have important implications for research and practice.