Please use this identifier to cite or link to this item: http://hdl.handle.net/10739/645
Title: Using statistical forecasting to optimize staff scheduling in healthcare organizations
Authors: Ganguly, Anirban
Nandi, Saikat
Jindal Global Business School
Superior Business Results, North Carolina, USA
Keywords: Forecasting
Time series forecasting
Staff scheduling
Healthcare organization
Issue Date: 31-Dec-2016
Publisher: Sage
Citation: Ganguly, Anirban and Nandi, Saikat. (2016). Using statistical forecasting to optimize staff scheduling in healthcare organizations. Journal of Health Management, Vol 18 No 1: 172-181
Abstract: Modern-day business environment of healthcare organizations demands the maximization of opera¬tional effectiveness and quality with optimal cost. Therefore, healthcare executives are often required to make difficult decisions based on subjective experience and judgement. An example of such a deci¬sion is scheduling of resources to fulfil demand for service. The effective use of statistical forecasting can lead to better personnel scheduling decisions based on estimates of patient arrival rates, resulting in improvement in quality of service as well as reduction of cost. purpose of this article is to demonstrate the typical steps involved in applying forecasting tech¬niques in patient care: This demonstration involves use of statistical techniques like Analysis of Variance (ANOVA) to identify factors driving demand, and Auto Regressive Integrated Moving Average (ARIMA) to develop a forecasting model for optimal staff scheduling in healthcare organizations based on patient arrival rates. The models are developed and subsequently tested on a set of real data gathered from a regional hospital located in the US. Statistically significant difference in average patient count was found among different days of the week. The findings of the research suggests that resources like cleaning personnel can be better utilized by allocating different proportions of resources to different parts of the week, based on the understanding of different patient load over these time periods
Description: Scopus Index
URI: http://hdl.handle.net/10739/645
ISSN: 0972-0634
Appears in Collections:JGU Research Publications

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