4 results found
DAeth J, Ghosal S, Grimm F, et al., 2023, Optimal hospital care scheduling during the SARS-CoV-2 pandemic, Management Science, Vol: 69, Pages: 5923-5947, ISSN: 0025-1909
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 so as to reduce the backlog of non-COVID patients whilst maintaining the ability to respond to any potential future increases in demand for COVID care. In this paper, we propose a nation-wide 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 & poisoning, diseases of the respiratory system, diseases of the circulatory system, diseases of the digestive system and cancer.
Koca E, Valletti T, Wiesemann W, 2021, Designing digital rollovers: managing perceived obsolescence through release times, Production and Operations Management, Vol: 30, Pages: 3698-3712, ISSN: 1059-1478
When releasing a new version of a durable product, a firm aims to attract new customers as well as persuadeits existing customer base to upgrade. This is commonly achieved through arollover strategy, which comprisesthe price of the new product as well as the decision to discontinue the sale of the existing product (solorollover) or to sell the existing product at a discounted price (dual rollover). In this paper, we argue thatthe timing of the new product release is an important—but commonly overlooked—third lever in the designof a successful rollover strategy. The release timing influences the consumers’ perception of obsolescence, bywhich an existing product is considered obsolete merely by reference to a new product. This reinforces theupgrading behavior of existing customers, but it also necessitates deep discounts of the existing product tokeep its sale viable in a dual rollover. We analyze the impact of the release timing on solo and dual rolloversin markets for digital goods (i.e., where production costs are negligible) that are composed of naive andsophisticated consumers. Under the assumption that both the old and the new product would offer a similarutility if there was no perceived obsolescence, we show that in both markets a firm selecting the release timesfrom a continuous timeline can induce sufficiently large parts of its existing customer base to upgrade so thata solo rollover is optimal. We also characterize the resulting market segmentation, and we offer managerialas well as policy advice.
DAeth J, Ghosal S, Grimm F, et al., 2021, Optimal national prioritization policies for hospital care during the SARS-CoV-2 pandemic, Nature Computational Science, Vol: 1, Pages: 521-531, ISSN: 2662-8457
In response to unprecedent surges in the demand for hospital care during the SARS-CoV-2 pandemic, health systems have prioritized COVID patients 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 System in England and show that an extra 50,750-5,891,608 years of life can be gained in comparison to prioritization policies that reflect those implemented during the pandemic. Significant health gains are observed for neoplasms, diseases of the digestive system, and injuries & 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.
D'Aeth J, Ghosal S, Grimm F, et al., 2020, Report 40: Optimal scheduling rules for elective care to minimize years of life lost during the SARS-CoV-2 pandemic: an application to England
SummaryCountries have deployed a wide range of policies to prioritize patients to hospital care to address unprecedent surges in demand during the course of the pandemic. Those policies included postponing planned hospital care for non-emergency cases and rationing critical care.We develop a model to optimally schedule elective hospitalizations and allocate hospital general and critical care beds to planned and emergency patients in England during the pandemic. We apply the model to NHS England data and show that optimized scheduling leads to lower years of life lost and costs than policies that reflect those implemented in England during the pandemic. Overall across all disease areas the model enables an extra 50,750 - 5,891,608 years of life gained when compared to standard policies, depending on the scenarios. Especially large gains in years of life are seen for neoplasms, diseases of the digestive system, and injuries & poisoning.
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