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

Abstract

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.

Keywords: COVID-19 operations; Capacity planning; Healthcare network simulation; OR in developing countries.

Fig. 1

Facility types, associated resources/services and overarching patient flow in the modelled public healthcare facility network. ICU = intensive care unit. ICU (Ox) = ICU with supplemental oxygen support. ICU (Ven) = ICU with ventilator support.

Fig. 2

Daily total caseloads and caseload by facility type. Note: the total cases per day plot represents the actual number of COVID-19 cases detected in the district, whereas the cases per facility (PHC/CHC/DH) per day plots are estimated using the total cases per day records for use in the model.

Fig. 3

CCC capacity planning outcomes. Average wait times to admission and average bed utilisation estimates for (a) isolation ward (IW), (b) general ward (GW), (c) ICU with supplemental oxygen support (ICU[Ox]), and (d) ICU with ventilator support (ICU[Ven]).

Fig. 4

COVID-19 case load sensitivity analysis results. (a) Average utilisation at the CHC. (b) Average wait times at the CHC for moderately ill patients. (c) Average utilisation estimates at the DH. (d) Average utilisation estimates at the CCC. Doc = doctor; gen = general ward; ICU (ox): ICU with supplemental oxygen support; ICU (ven): ICU with ventilator support; Isolation: isolation ward.

Fig. A.5

Testing and triaging process at the PHC. $x$ = Number of non-COVID-19 patients, 20% of whom present with COVID-like symptoms and hence seek testing; $y$ = number of symptomatic COVID-19 patients.

Fig. B.6

Operational pathways for COVID-19 patients in the healthcare network - divided in parts (a), (b) and (c). PHC = primary health centre, CHC = community health centre, DH = district hospital, CCC = COVID-19 care centre, ICU = intensive care unit, ICU (ven) = ICU with ventilator support unit, ICU (ox) = ICU with supplemental oxygen support.