Rapid COVID-19 Modeling Support for Regional Health Systems in England

Self Cite

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

“…The formulation for the logistic function (Equation ()) follows closely Kapmeier and Gonçalves (2018) and Betcheva et al. (2020):

\begin{equation}{F_t} = {B_L}\ + \left( {{B_U} - {B_L}} \right)\left[ {1 - \frac{{{e^{\left( {sD - i} \right)}}}}{{1 + {e^{\left( {sD - i} \right)}}}}} \right],\end{equation}

Section: Methodsmentioning

confidence: 95%

“…Dread changes over time with an asymmetric time constant: It increases quickly with cumulative deaths and decreases slowly with time. The formulation for the logistic function (Equation (1)) follows closely Kapmeier and Gonçalves (2018) and Betcheva et al (2020):…”

Section: 3mentioning

confidence: 99%

Model‐informed health system reorganization during emergencies

Gonçalves

Ferrari

Crivelli

et al. 2023

Self Cite

“…Section 1.1 discusses the importance of accounting for the rapidly changing data environment in estimating these parameters. The most relevant paper is Betcheva et al (2020). The authors develop an integrated disease forecast and resource planning tool for the East of England region.…”

Section: Hospital Workload Modelsmentioning

confidence: 99%

Operations (management) warp speed: Rapid deployment of hospital‐focused predictive/prescriptive analytics for the COVID‐19 pandemic

Shi

Helm

Chen

et al. 2023

At the onset of the COVID‐19 pandemic, hospitals were in dire need of data‐driven analytics to provide support for critical, expensive, and complex decisions. Yet, the majority of analytics being developed were targeted at state‐ and national‐level policy decisions, with little availability of actionable information to support tactical and operational decision‐making and execution at the hospital level. To fill this gap, we developed a multi‐method framework leveraging a parsimonious design philosophy that allows for rapid deployment of high‐impact predictive and prescriptive analytics in a time‐sensitive, dynamic, data‐limited environment, such as a novel pandemic. The product of this research is a workload prediction and decision support tool to provide mission‐critical, actionable information for individual hospitals. Our framework forecasts time‐varying patient workload and demand for critical resources by integrating disease progression models, tailored to data availability during different stages of the pandemic, with a stochastic network model of patient movements among units within individual hospitals. Both components employ adaptive tuning to account for hospital‐dependent, time‐varying parameters that provide consistently accurate predictions by dynamically learning the impact of latent changes in system dynamics. Our decision support system is designed to be portable and easily implementable across hospital data systems for expeditious expansion and deployment. This work was contextually grounded in close collaboration with IU Health, the largest health system in Indiana, which has 18 hospitals serving over one million residents. Our initial prototype was implemented in April 2020 and has supported managerial decisions, from the operational to the strategic, across multiple functionalities at IU Health.

“…Betcheva et al. (2020) describe an intervention to build a similar model with U.K. National Health Service planners and add the mental health sector.…”

Section: Literature Review and Open Questions By Clustermentioning

confidence: 99%

Opportunities for system dynamics research in operations management for public policy

Anderson

Keith²,

López³

2023

Operations management (OM) in the public policy context is extremely complex with many mutually interacting factors characterized by feedback loops, delays and nonlinearities, as well as multiple stakeholders pursuing divergent objectives. Prior researchers have called for a systems approach in these contexts, arguing that standard OM methodologies such as mathematical programming, and queuing theory often cannot fully address these problems. Researchers have employed one such systems approach, system dynamics, successfully for decades for studying OM problems in public policy because it can address such complexity and can also integrate disciplines from outside OM such as political science, epidemiology, ecology, etc. In this paper, we create a roadmap for researchers—both those who are familiar with systems dynamics and those who are not—for the expanded use of system dynamics studying public policy‐related OM problems. We review and organize relevant system dynamics literature in both traditional operations management venues as well as public policy venues unfamiliar to OM audiences. We then identify a set of interesting open questions and potential system dynamics building blocks for answering them by topic. Leveraging this review, we describe under what conditions system dynamics is most appropriate. We then identify several overarching methodological and domain gaps for future research. Finally, we build on previous work to extend a process for using system dynamics with traditional operations management methodologies. It separates model building into two sequential phases: consensus‐building models and detailed operational models. It also incorporates scenario planning and feedback from implementation outcomes.