A data mining approach for estimating patient demand for mental health services

Abstract

The ability to better estimate future demand for health services is a critical element to maintaining a stable quality of care. With greater knowledge of how particular events can impact demand, health-care service providers can better allocate available resources to more effectively treat patients’ needs. The incorporation of data mining analytics can leverage available data to identify recurring patterns among relevant variables, and these patterns provide actionable information to corresponding decision markers at health-care organizations. The demand for mental health services can be subject to variation from time of year (seasonality) and economic factors. This study illustrates the effectiveness of data mining analytics in identifying seasonality and economic factors as measured by time that affect patient demand for mental health services. It incorporates a neural network analytic method that is applied to patient demand data at a U.S. medical center. The results indicate that day of week, month of year, and a yearly trend significantly impact the demand for patient services.

Keywords:

• data mining
• seasonal demand
• neural networks
• mental health demand
• decision support systems