The COVID-19 pandemic has placed severe demands on healthcare facilities across the world, and in several countries, makeshift COVID-19 centres have been operationalised to handle patient overflow. In developing countries such as India, the public healthcare system (PHS) is organised as a hierarchical network with patient flows from lower-tier primary health centres (PHC) to mid-tier community health centres (CHC) and downstream to district hospitals (DH). In this study, we demonstrate how a network-based modelling and simulation approach utilising generic modelling principles can (a) quantify the extent to which the existing facilities in the PHS can effectively cope with the forecasted COVID-19 caseload; and (b) inform decisions on capacity at makeshift COVID-19 Care Centres (CCC) to handle patient overflows. We apply the approach to an empirical study of a local PHS comprising ten PHCs, three CHCs, one DH and one makeshift CCC. Our work demonstrates how the generic modelling approach finds extensive use in the development of simulations of multi-tier facility networks that may contain multiple instances of generic simulation models of facilities at each network tier. Further, our work demonstrates how multi-tier healthcare facility network simulations can be leveraged for capacity planning in health crises. 

Leveraging multi-tier healthcare facility network simulations for capacity planning in a pandemic

Primary Healthcare Staffing Needs Assessment— A Discrete Event Simulation Study Tolulope Abe Chair of the Supervisory Committee: Associate Professor Kenneth Sherr Global Health 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 decisionmaking, 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.

Tolulope Abe

Papers

Primary Healthcare Staffing Needs Assessment-- A Discrete Event Simulation Study