Operations (Management) Warp Speed: Parsimonious Design for Rapid Deployment of Hospital Prediction and Decision Support Framework during a Pandemic

47 Pages Posted: 1 Apr 2021

See all articles by Pengyi Shi

Pengyi Shi

Purdue University - Krannert School of Management

Jonathan Helm

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies

Christopher Chen

Indiana University - Kelley School of Business

Jeff Lim

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies

Rodney P. Parker

Indiana University Bloomington

Troy Tinsley

IU Health System

Jacob Cecil

IU Health System

Date Written: February 28, 2021

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 challenging decisions. Yet, the majority of analytics being developed were targeted toward 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, limited data environment such as a novel pandemic. Our framework integrates 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 to forecast time-varying patient workload and demand for critical resources. Both components employ adaptive tuning to account for hospital-dependent, time-varying parameters that provide consistently accurate predictions as changes in system dynamics are learned over time. The product of this research is a workload prediction and decision support framework customized to individual hospital that specifically targets actionable information based on hospital needs. Yet, by design, our framework is portable across hospital data-systems and easily implementable for expeditious expansion. This work was contextually grounded in close collaboration with IU Health, the largest health system in Indiana with 18 hospitals serving over 1 million residents. Since April 2020, we have implemented our framework to support decisions from operational to strategic made by multiple departments at IU Health as well as the executive leadership team during the course of the COVID-19 pandemic.

Keywords: COVID-19, pandemic, workload prediction, machine learning, stochastic model, queueing

Suggested Citation

Shi, Pengyi and Helm, Jonathan and Chen, Christopher and Lim, Jeff and Parker, Rodney P. and Tinsley, Troy and Cecil, Jacob, Operations (Management) Warp Speed: Parsimonious Design for Rapid Deployment of Hospital Prediction and Decision Support Framework during a Pandemic (February 28, 2021). Available at SSRN: https://ssrn.com/abstract=3815418 or http://dx.doi.org/10.2139/ssrn.3815418

Pengyi Shi (Contact Author)

Purdue University - Krannert School of Management ( email )

1310 Krannert Building
West Lafayette, IN 47907-1310
United States

Jonathan Helm

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies ( email )

Business 670
1309 E. Tenth Street
Bloomington, IN 47401
United States

Christopher Chen

Indiana University - Kelley School of Business ( email )

1309 East Tenth Street
Indianapolis, IN 47405-1701
United States

Jeff Lim

Indiana University - Kelley School of Business - Department of Operation & Decision Technologies ( email )

Business 670
1309 E. Tenth Street
Bloomington, IN 47401
United States

Rodney P. Parker

Indiana University Bloomington ( email )

1309 E 10th Street, HH4129
Bloomington, IN 47405
United States

Troy Tinsley

IU Health System ( email )

Jacob Cecil

IU Health System ( email )

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