Using Singular Spectrum Analysis to obtain staffing level requirements in emergency units

Abstract

Many Operational Research (OR) techniques use historical data to populate model input parameters. Although the majority of these models take into account stochastic variation of the inputs, they do not necessarily take into account seasonal variations and other stochastic effects that might arise. One of the major applications of OR lies within healthcare, where ever increasing pressure on healthcare systems is having major implications on those who plan the provision of such services. Coping with growing demand for healthcare, as well as the volatile nature of the number of arrivals at a healthcare facility makes modelling healthcare provision one of the most challenging fields of OR. This paper proposes the use of a relatively modern time series technique, Singular Spectrum Analysis (SSA), to improve existing algorithms that give required staffing levels. The methodology is demonstrated using data from a large teaching hospital's emergency unit. Using time dependent queueing theory, as well as SSA, staffing levels are obtained. The performance of our technique is analysed using a weighted mean square error measure, introduced in this paper.

Detecting feeble position oscillations from rotary encoder signal in an industrial robot via singular spectrum analysis

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Health Assessment and Fault Detection System for an Industrial Robot Using the Rotary Encoder Signal

“…(i) SSA is a strong strategy for non-fixed and nonlinear signal examination. It has been widely utilized for cyclic movement identification in the intricated dynamical framework and trend extraction from geophysical, analysis of death series, and climatic time series [39][40][41]. It empowers intricated signal to be decreased into various explainable parts containing a set of cyclic oscillations, noise, and a trend.…”

Performance Analysis of Time-Dependent Queueing Systems: Survey and Classification

“…Consequently, the number of medical personnel is often also time-dependent. Applications include the performance analysis of emergency facilities [42,54], staffing in clinical wards [3,78,239], and ambulance management [199]. Yom-Tov and Mandelbaum [239] derive a model that includes re-entrant patients/repetitive service in clinical wards.…”

“…Collings and Stoneman [42] EXPL Agnihothri and Taylor [3] x x SIPP Brahimi and Worthington [23] x D T A Bennett and Worthington [16] x D T A de Bruin et al [54] x SIPP Singer and Donoso [199] x SIPP Gillard and Knight [78] x CKE Vanberkel et al [222] x x PSA Yom-Tov and Mandelbaum [239] x x MOL Emergency services Kolesar et al [130] x x CKE, SIPP Bookbinder and Martell [22] x x CKE Green and Kolesar [84] SPHA Ingolfsson et al [103] x x CKE, SIPP Green et al [91] x x SIPP Alfa and Margolius [11] x D T A…”