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

Abstract

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.

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

“…The demand‐focused stream investigates “flattening the epidemic curve” (Evgeniou et al., 2022; Ferguson et al., 2020; Jain & Rayal, 2023; Perkins & Espana, 2020; Shahmanzari et al., 2022) containment measures such as lockdown or quarantine, given a fixed capacity for the ICU (or other pertinent resource). The supply‐focused stream, in which our paper is positioned, focuses on capacity expansion of ICU (or other resource) (Alban et al., 2020; Lu et al., 2021; Ouyang et al., 2020; Shi et al., 2022; Wood et al., 2020). Unlike our use of three models—epidemic, demand, and optimization—many researchers have used just two of the modules.…”

“…Furthermore, Shi et al. (2022) use the projected demand of each hospital in a hospital system for decision models for resource (re)allocation.…”

“…Note that this is the fraction of the reported new cases which underestimates the actual ones. Indeed, the estimated ratio between the actual and official number of total cases for Covid‐19 typically ranges from 4× to 10× in the literature (McCulloh et al., 2020; Shi et al., 2022), depending on the country and the stage of the pandemic. Thus, our method for estimating a compensates for the underestimation in official data, ultimately giving the correct projections for the demand for ICU .…”

Managing pandemics: A POM perspective and directions for future research

“…of Infection in a Region" (Jain & Rayal, 2023), and "Operations (Management) Warp Speed: Rapid Deployment of Hospital-Focused Predictive/Prescriptive Analytics for the COVID-19 Pandemic" (Shi et al, 2023). Zhang et al (2023) focus on quantifying the risk of pausing organ transplants on the providers and patients.…”

“…We accepted four papers that deal with hospital capacity and operations. These include: “Pausing transplants in the face of a global pandemic: Patient survival implications” (Zhang et al., 2023), “Hospital Admission, Facility‐based Isolation, and Social Distancing: An SEIR Model with Constrained Medical Resources” (Chen & Kong, 2023), “Managing Medical Equipment Capacity with Early Spread of Infection in a Region” (Jain & Rayal, 2023), and “Operations (Management) Warp Speed: Rapid Deployment of Hospital‐Focused Predictive/Prescriptive Analytics for the COVID‐19 Pandemic” (Shi et al., 2023).…”