AbstractsBusiness Management & Administration

Primary Healthcare Staffing Needs Assessment – A Discrete Event Simulation Study

by Tolulope Abe

Institution: University of Washington
Year: 2015
Keywords: Average Wait Time; Discrete Event Simulation; Primary Healthcare Delivery; Staffing Needs; Public health
Record ID: 2057845
Full text PDF: http://hdl.handle.net/1773/27495


Background: Mozambique has a shortage of primary healthcare workers, effecting the quality of primary healthcare delivery. The specific aim of the proposed research is utilizing Industrial & Systems Engineering methods, specifically discrete event simulation, to identify strategies to improve primary healthcare delivery system performance in the Sofala Province of Mozambique. To improve primary healthcare delivery and assist facility-level management with decision-making, the research team decided to study staffing level effects on patient waiting time and develop a decision support tool that determines staffing level needs to maintain an average total wait time of less than 60minutes. Methods: A discrete event simulation study was performed to model primary healthcare facility delivery systems using Arena Simulation software. What-if scenario experiments testing the impact health worker staffing level and patient demand fluctuations have on patient waiting time were designed using Statistical Analysis Software, and the experimental runs were performed using Arena Simulation Process Analyzer application. Minitab was used to perform a regression analysis to find mathematical models of wait time as a function of patient demand and staffing levels. Results: The mathematical relationship of health worker staffing and patient demand levels with average patient wait time was estimated using regression analysis. The number of staff required to provide services was the aggregate of staffing needed for all patient types of a given facility. This approach produced staffing and wait time results that could not be validated and used to create a spreadsheet-based decision support tool. Conclusion: The spreadsheet-based decision support tool aimed to bridge the gap between industrial and systems engineering methods and healthcare stakeholder knowledge of these methods while promoting implementation by allowing decision makers to perform simulations through a user-friendly tool. Although the proposed approach described in this study could not be validated, this method is beneficial when attempting to evaluate performance improvement strategy impacts on measures of performance. To create the spreadsheet-based decision support tool, it is recommended that an alternative approach be used to determine the relationship between wait time, patient demand, and staffing levels, such as queuing analysis.