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Research Article Open access CC BY 4.0

Optimum Manpower Allocation in Health Care Using Statistical Process Control

William E. Odinikuku, Jephthah A. Ikimi, Ikechukwu P. Onwuamaeze

Journal of Engineering Research and Reports · pp. 1–9 · Published 1 Nov 2019

10.9734/jerr/2019/v8i116981

Abstract

In many countries manpower problems in the field of health care are regular items on the agenda of policy makers. To avoid mismatches between demand of care and supply of care on national and regional levels, manpower planning models and methods are used to determine adequate numbers of medical specialists to fulfill the future demand of care. Inadequate or inefficient allocation of manpower to various departments in an organization or workplace can lead to undesired outcomes which may include: down time, reduced productivity, workers fatigue, increased production costs, etc. As a result of the above stated problem, there is need to devise a statistical model that will ensure optimal allocation of manpower. In this study, the optimum allocation of two hundred and fifty two general nurses to fifteen wards at a hospital code named WCH located in South-South geopolitical zone, Nigeria was achieved using statistical process control. The study involved the analysis of data obtained from our hub of study for a period of two months. The C-chart was used to check if the process of allocation was in control or not. The result obtained from the study showed that the manpower allocation process was out of statistical control as the allocation of the children emergency ward was outside the upper control limit of the c-chart plot.

Manpower allocation health care general nurse statistical process control c-chart.

Cited by 2

Surveilling public health through statistical process monitoring: A literature review and a unified framework

S. Bersimis, A. Sachlas · Communications in Statistics: Case Studies, Data Analysis and Applications · 2022

Showing 1 of 2 known citations — external sources report more than can currently be individually listed.

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