In response to unprecedented surges in the demand for hospital care during the SARS-CoV-2 pandemic, health systems have prioritized patients with COVID-19 to life-saving hospital care to the detriment of other patients. In contrast to these ad hoc policies, we develop a linear programming framework to optimally schedule elective procedures and allocate hospital beds among all planned and emergency patients to minimize years of life lost. Leveraging a large dataset of administrative patient medical records, we apply our framework to the National Health Service in England and show that an extra 50,750–5,891,608 years of life can be gained compared with prioritization policies that reflect those implemented during the pandemic. Notable health gains are observed for neoplasms, diseases of the digestive system, and injuries and poisoning. Our open-source framework provides a computationally efficient approximation of a large-scale discrete optimization problem that can be applied globally to support national-level care prioritization policies.

When releasing a new version of a durable product, a firm aims to attract new customers as well as persuade its existing customer base to upgrade. This is commonly achieved through a rollover strategy, which comprises the price of the new product as well as the decision to discontinue the sale of the existing product (solo rollover) or to sell the existing product at a discounted price (dual rollover). In this study, we argue that the timing of the new product release is an important—but commonly overlooked—third lever in the design of a successful rollover strategy. The release timing influences the consumers' perception of obsolescence, by which an existing product is considered obsolete merely by reference to a new product. This reinforces the upgrading behavior of existing customers, but it also necessitates deep discounts of the existing product to keep its sale viable in a dual rollover. We analyze the impact of the release timing on solo and dual rollovers in markets for digital goods (i.e., where production costs are negligible) that are composed of naive and sophisticated consumers. Under the assumption that both the old and the new product would offer a similar utility if there was no perceived obsolescence, we show that in both markets a firm selecting the release times from a continuous timeline can induce sufficiently large parts of its existing customer base to upgrade so that a solo rollover is optimal. We also characterize the resulting market segmentation, and we offer managerial as well as policy advice.

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The COVID-19 pandemic has seen dramatic demand surges for hospital care that have placed a severe strain on health systems worldwide. As a result, policy makers are faced with the challenge of managing scarce hospital capacity to reduce the backlog of non-COVID patients while maintaining the ability to respond to any potential future increases in demand for COVID care. In this paper, we propose a nationwide prioritization scheme that models each individual patient as a dynamic program whose states encode the patient’s health and treatment condition, whose actions describe the available treatment options, whose transition probabilities characterize the stochastic evolution of the patient’s health, and whose rewards encode the contribution to the overall objectives of the health system. The individual patients’ dynamic programs are coupled through constraints on the available resources, such as hospital beds, doctors, and nurses. We show that the overall problem can be modeled as a grouped weakly coupled dynamic program for which we determine near-optimal solutions through a fluid approximation. Our case study for the National Health Service in England shows how years of life can be gained by prioritizing specific disease types over COVID patients, such as injury and poisoning, diseases of the respiratory system, diseases of the circulatory system, diseases of the digestive system, and cancer. This paper was accepted by Chung-Piaw Teo, optimization. Funding: G. Forchini acknowledges funding from Jan Wallanders and Tom Hedelius Foundation and the Tore Browaldh Foundation, funding from MRC Centre for Global Infectious Disease Analysis [Reference MR/R015600/1], jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth and Development Office (FCDO), under the MRC/FCDO Concordat agreement, part of the EDCTP2 program supported by the European Union; and acknowledges funding by Community Jameel. D. Rizmie acknowledges partial funding from the MRC Centre for Global Infectious Disease Analysis [Reference MR/R015600/1]. J. C. D’Aeth acknowledges funding from the Wellcome Trust [Reference 102169/Z/13/Z]. S. Moret acknowledges partial support from the Swiss National Science Foundation (SNSF) under [Grant P2ELP2_188028]. S. Ghosal was funded by the Imperial College President’s PhD Scholarship. F. Grimm was funded by the Health Foundation as part of core staff member activity. This research was funded in whole, or in part, by the Wellcome Trust [Grant 102169/Z/13/Z]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4679 .

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