Forecasting Demand for Hospital Services

Abstract

Demand forecasting is one of the important issues related to operations management in health sector. Forecasting patient volume in hospitals provides an important input regarding the correct planning of financial resources, human resources, and material resources. In this chapter, the authors first discuss forecasting patient volume in hospital services and then present a case study involving patient volume forecasting for a local hospital in Turkey. Different traditional statistical methods and machine learning methods are applied to both inpatient and outpatient demand from six polyclinics and a surgery room. Results show that damped trend exponential smoothing method outperforms other methods based on overall performance.