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Many service systems have servers with different capabilities and customers with varying needs. One common way this occurs is when servers are hierarchical in their skills or in the level of service they can provide. Much of the literature studying such systems relies on an understanding of the relative costs and benefits associated with serving different customer types by the different levels of service. In this work, we focus on estimating these costs and benefits in a complex healthcare setting where the major differentiation among server types is the intensity of service provided. Step Down Units (SDUs) were initially introduced in hospitals to provide an intermediate level of care for semi-critically ill patients who are not sick enough to require intensive care but not stable enough to be treated in the general medical/surgical ward. One complicating factor is that the needs of customers is sometimes uncertain-specifically, it is difficult to know a priori which level of care a particular patient needs. Using data from 10 hospitals from a single hospital network, we take a data-driven approach to classify patients based on severity and empirically estimate the clinical and operational outcomes associated with routing these patients to the SDU. Our findings suggest that an SDU may be a cost-effective way to treat patients when used for patients who are post-ICU. However, the impact of SDU care is more nuanced for patients admitted from the emergency department (ED) and may result in increased mortality risk and hospital LOS for patients who should be treated in the ICU. Our results imply that more study is needed when using SDU care this way.

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Purpose Prediction markets are techniques to aggregate dispersed public opinions via market mechanisms to predict uncertain future events’ outcome. Many experiments have shown that prediction markets outperform other traditional forecasting methods in terms of accuracy. Logarithmic market scoring rules (LMSR) is one of the most simple and widely used market mechanisms; however, market makers have to confront crucial design decisions including the setting of the parameter “b” or the “liquidity parameter” in the price functions. As the liquidity parameter has significant effects on the market performance, this paper aims to provide a comprehensive basis for the setting of the parameter. Design/methodology/approach The analyses include the effects of the liquidity parameter on the forecast standard error and the amount of time for the market price to converge to the true value. These experiments use artificial prediction markets, the proposed simulation models that mimic real prediction markets. Findings The simulation results indicate that prediction market’s forecast standard error decreases as the value of the liquidity parameter increases. Moreover, for any given number of traders in the market, there exists an optimal liquidity parameter value that yields appropriate price adaptability and leads to the fastest price convergence. Originality/value Understanding these tradeoffs, the market makers can effectively determine the liquidity parameter value under various objectives on the standard error, the time to convergence and cost.

Connected healthcare is a form of health delivery that connects patients and providers through connected health devices, allowing providers to monitor patient behavior and proactively intervene before an adverse event occurs. Unlike the costs, the benefits of connected healthcare in improving patient behavior and health outcomes are usually difficult to determine. In this study, we examine the efficacy of a connected health system that aimed to reduce readmissions through improved medication adherence. Specifically, we study 975 patients with heart disease who received electronic pill bottles that tracked medication adherence. Patients who were nonadherent received active social support that involved different types of feedback, such as text messages and calls. By integrating data on adherence, intervention, and readmission, we aim to (1) investigate the efficacy of connected healthcare in promoting medication adherence, (2) examine the relationship between medication adherence and readmission, and (3) develop a dynamic readmission risk-scoring model that considers medication adherence and use the model to better target nonadherent patients. Our findings suggest that patients are more likely to become adherent when they or their partners receive high levels of intervention that involve personalized feedback and when the intervention is escalated quickly and consistently. We also find that long-term adherence to three common heart medications is strongly associated with reduced readmission risk. Lastly, using counterfactual simulation, we apply the dynamic readmission risk-scoring model to our setting and find that, when using an intervention strategy that prioritizes high-risk patients, we obtain 10% fewer readmissions while using the same effort level from the patient support team. This paper was accepted by Jayashankar Swaminathan, operations management. Funding: The randomized, controlled trial was funded by the Center for Medicare & Medicaid Innovation [Healthcare Innovation Award 1C1CMS331009]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4865 .