Delta Coverage: The Analytics Journey to Implement a Novel Nurse Deployment Program

Jonathan E. Helm (), Pengyi Shi (), Mary Drewes () and Jacob Cecil ()
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Jonathan E. Helm: Kelley School of Business, Indiana University, Bloomington, Indiana 47405
Pengyi Shi: Daniels School of Business, Purdue University, West Lafayette, Indiana 47907
Mary Drewes: Nursing Organization, Indiana University Health, Indianapolis, Indiana 46202
Jacob Cecil: Nursing Organization, Indiana University Health, Indianapolis, Indiana 46202

Interfaces, 2024, vol. 54, issue 5, 431-454

Abstract: Amidst critical levels of nurse shortages, we partnered with Indiana University Health (IUH) to pioneer a novel suite of advanced data and decision analytics to support a new model of nurse staffing. This statewide program leverages a flexible pool of resource nurses who can move between the 16 IUH hospitals located in five diverse regions and serving more than 1.4 million residents. This program breaks the mold of traditional travel and resource nurses by adding flexibility to move nurses between hospitals to dynamically respond to short-term patient census fluctuations. This paradigm shift necessitated the development of analytics to execute these interhospital transfers. Specifically, we develop analytics to create a two-week advance on-call list for travel and a 24- to 48-hour call-in decision. Our Delta Coverage Analytics Suite was launched in October 2021 as a Microsoft PowerBI application and provides an integrated solution that has supported and continues to support this new staffing approach at a statewide scale. The suite contrasts with existing nurse scheduling tools that primarily cater to single hospitals or units. It incorporates (1) a novel patient census forecast based on a deep generative model capturing complex spatial-temporal correlations and avoiding error accumulation occurring in traditional time-series models and (2) a stochastic optimization that prescribes optimal on-call and deployment decisions. The pilot, conducted from May to June 2023, produced a remarkable reduction in understaffing, with estimated annual savings of $2.5 million to IUH and over $1.5 billion on a national scale compared with the conventional solution of hiring travel nurses. As the first program of its kind, our methods establish new benchmarks for evidence-based and data-driven nurse workforce management with the potential to transform how healthcare institutions approach the national nursing shortage crisis.

Keywords: nursing shortage crisis; nursing practice innovation; analytics for staffing; machine learning forecast; predictive-prescriptive integration (search for similar items in EconPapers)
Date: 2024
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