Model‐informed health system reorganization during emergencies

Abstract

The COVID-19 pandemic presented the world to a novel class of problems highlighting distinctive features that rendered standard academic research and participatory processes less effective in properly informing public health interventions in a timely way. The urgency and rapidity of the emergency required tight integration of novel and highquality simulation modeling with public health policy implementation. By introducing flexibility and agility into standard participatory processes, we aligned the modeling effort with the imposed reality of the emergency to rapidly develop a regional system dynamics (SD) model integrating diverse streams of data that could reliably inform both health system restructuring and public health policy. Using Lombardy data, our SD model was able to generate early projections for the diffusion of the pandemic in neighbor Ticino. Later, it projected the timing and size of peak patient demand. Our work also supported the need for reorganization of the healthcare system and volume flexibility strategies increasing hospital capacity (e.g., intensive care unit [ICU] and ward beds, medical and nursing staff, and oxygen supply) in Ticino. Counterfactual analyses quantify the impact of the decisions supported by our interventions. Our research contributes to our understanding of volume flexibility strategies used by healthcare organizations during emergencies, highlighting the critical role played by available response time in the deployment of strategies that either prioritize critical services or leverage available resources. It also contributes to the literature on participatory systems modeling by describing a flexible and agile participatory process that was successfully deployed in a rapidly evolving high-stakes emergency. K E Y W O R D SCOVID-19 public health policy, healthcare operations management, health system reorganization, participatory system dynamics modeling, volume flexibility strategies 1

ICU capacity expansion under uncertainty in the early stages of a pandemic

“…Gonçalves et al. (2023) propose a complex compartmental model that captures links between the different quantities (e.g., hospital [or ICU] loading influences the number of infected individuals). However, their model is deterministic and cannot capture the uncertainty in the future demand by simply choosing parameter values randomly chosen from arbitrary ranges.…”

“…Epidemic data have two sources of uncertainty, generally ignored in the literature, regarding the new cases each day: (1) the reporting error, such as delays in official registrations of daily cases, and (2) the disease dynamics that are inherently stochastic because recovery time and time to get infected are random variables. We incorporate the uncertainty by using the (joint) probability distribution of the parameter estimators to perform a Monte Carlo sensitivity analysis (Gonçalves et al., 2023) of the parameter values, as described below. We can produce longer‐term forecasts that are also more robust against reporting errors in the data.…”

Impact of the Covid-19 pandemic on inpatient health care in Switzerland – a retrospective study using admission data of all Swiss hospitals

“…In Switzerland, the impact of the Covid-19 pandemic on admissions was roughly similar in the different regions, with Ticino showing the largest reduction with the earliest onset. This can be explained by the geographical proximity and the close economic and social ties of Ticino with Lombardy, the rst region in Europe to be severely affected by the Covid-19 pandemic as early as mid-February 2020 (35). The fact that university hospitals and major hospitals presented the largest all-year de cits in admissions by the end of 2020 might re ect the fact that these hospitals contributed most to the management of the pandemic by treating large numbers of severely ill Covid-19 patients and by keeping intensive care unit capacities free in anticipation of more Covid-19 patients (36).…”

Opportunities for system dynamics research in operations management for public policy

“…An exemplar for this approach is Goncalves et al. (2021), particularly because it describes a consensus‐building approach different from those traditionally described in SD research (Andersen et al., 1997; Vennix, 1999).…”

“…In this case, epidemiology and physiology experts would replace engineers as the domain experts, typical metrics such as the percentage of the population immunized and infected would be used, SEIR (susceptible, exposed, infected, recovered) compartmental models would be employed as the dominant representational paradigm and so on. This could lead to decisions around fast‐tracking vaccine development, prioritizing which population segments to vaccinate first, and allocating capacity (including beds, staffing, and ventilators) to hospitals in different areas (Goncalves et al., 2023)…”