Knowledge that Transforms

One prevalent assumption in queueing theory is that the number of servers in a queueing model is deterministic. However, randomness in the number of available servers often arises in practice, e.g., in virtual call centers where agents are allowed to set their own schedules. In this paper, we study the problems of staffing and controlling queueing systems with an uncertain number of servers and impatient customers. Because randomness in the number of servers creates congestion in the system, the customer abandonment distribution plays an important role. We characterize how it affects both the optimal staffing policy and the cost incurred by the manager. Because of strong dependence on the abandonment distribution, it is natural to investigate ways of controlling customer abandonment behavior so as to mitigate that cost. Here, we propose doing so by making delay announcements in the system. We characterize how the manager may use three controls in her toolbox, staffing, compensation, and the announcements, to effectively control her system. We show that despite jointly optimizing the usage of those three controls, it may be cost effective for the manager to understaff, overstaff, or match supply and demand in any given shift.

There has been a significant increase in the scale and scope of humanitarian efforts over the last decade. Humanitarian operations need to be—rapid, impactful, sustained, and efficient (RISE). Big data offers many opportunities to enable RISE humanitarian operations. In this study, we introduce the role of big data in humanitarian settings and discuss data streams which could be utilized to develop descriptive, prescriptive, and predictive models to significantly impact the lives of people in need.

This study analyzes subsidy schemes that are widely used in reducing waiting times for public healthcare service. We assume that public healthcare service has no user fee but an observable delay, while private healthcare service has a fee but no delay. Patients in the public system are given a subsidy s to use private service if their waiting times exceed a pre‐determined threshold t. We call these subsidy schemes ( s, t) policies. As two extreme cases, the ( s, t) policy is called an unconditional subsidy scheme if t = 0, and a full subsidy scheme if s is equal to the private service fee. There is a fixed budget constraint so that a scheme with larger s has a larger t. We assess policies using two criteria: total patient cost and serviceability (i.e., the probability of meeting a waiting time target for public service). We prove analytically that, if patients are equally sensitive to delay, a scheme with a smaller subsidy outperforms one with a larger subsidy on both criteria. Thus, the unconditional scheme dominates all other policies. Using empirically derived parameter values from the Hong Kong Cataract Surgery Program, we then compare policies numerically when patients differ in delay sensitivity. Total patient cost is now unimodal in subsidy amount: the unconditional scheme still yields the lowest total patient cost, but the full subsidy scheme can outperform some intermediate policies. Serviceability is unimodal too, and the full subsidy scheme can outperform the unconditional scheme in serviceability when the waiting time target is long.

The United States has the highest rate of obesity in the world. To help address this problem, social support is gaining credibility as a powerful tool to facilitate weight loss because it can affect people's behavior. Although social support has long been recognized for its effectiveness in promoting health, we argue, in this study, that social support may not always lead to good outcomes. Specifically, we differentiate between support providers and support seekers, and examine whether providing and receiving support affect individuals’ weight‐loss outcomes differently. By analyzing a group of individuals participating in an online weight‐loss community, we show that providing and receiving support does affect weight‐loss outcomes in different ways. First, the influences are dynamic. Second, while providing support is positively associated with weight‐loss progress, receiving support could hinder weight‐loss outcome for a person with high self‐efficacy in weight‐loss progress. Third, by categorizing social support into different types, we find evidence suggesting that the match between needed and received social support type also influences individuals’ performance in the weight‐loss process. Furthermore, mismatches of social support could negatively affect weight‐loss outcomes. These findings have implications for maximizing the usefulness of social support for participants in the online environment as well as for clinicians who refer individuals to online weight‐loss communities and for those who design them.

Evaluating and quantifying human suffering in humanitarian operations offers an innovative and potentially powerful way to assess the performance of humanitarian logistics (HL) and help build optimization models. Previous studies have suggested deprivation cost as a metric and have estimated deprivation cost functions for water using willingness‐to‐pay. Our study proposes deprivation levels, defined as the degree of human suffering caused by lack of access to a good or service, and estimates deprivation level functions using a numerical rating scale. Analyzing data collected from respondents with and without disaster experience, we find that individuals in the latter category estimate deprivation differently from the beneficiaries of disaster relief. Our study demonstrates that deprivation levels can be expressed as logistic growth functions with a typical S‐shape, and that these can be integrated into HL optimization models to better account for human suffering.

Patients admitted to a hospital's intensive care unit (ICU) often endure prolonged boarding within the ICU following receipt of care, unnecessarily occupying a critical care bed, and thereby delaying admission for other incoming patients due to bed shortage. Using patient-level data over two years at two major academic medical centers, we estimate the impact of ICU and ward occupancy levels on ICU length of stay (LOS), and test whether simultaneous "surge occupancy" in both areas impacts overall ICU length of stay. In contrast to prior studies that only measure total LOS, we split LOS into two individual periods based on physician requests for bed transfers. We find that "service time" (when critically ill patients are stabilized and treated) is unaffected by occupancy levels. However, the less essential "boarding time" (when patients wait to exit the ICU) is accelerated during periods of high ICU occupancy and, conversely, prolonged when hospital ward occupancy levels are high. When the ICU and wards simultaneously encounter bed occupancies in the top quartile of historical levels-which occurs 5% of the time-ICU boarding increases by 22% compared to when both areas experience their lowest utilization, suggesting that ward bed availability dominates efforts to accelerate ICU discharges to free up ICU beds. We find no adverse effects of high occupancy levels on ICU bouncebacks, in-hospital deaths, or 30-day hospital readmissions, which supports our finding that the largely discretionary boarding period fluctuates with changing bed occupancy levels.

Drug shortages have been a major challenge facing the US pharmaceutical industry and government in recent years. Although the problem has drawn tremendous attention from the government and media, limited academic research has been devoted to this problem, and few solutions have been proposed based on rigorous research. This study addresses the drug shortage problem from a supply chain perspective, a key aspect missing in the literature, and proposes to mitigate shortages through drug purchase contracts. By modeling the drug supply chain, we capture the objectives of various supply chain parties, and investigate Pareto‐improving contracts that mitigate drug shortages, improve drug manufacturer's and group purchasing organization (GPO)'s profits, and cut government spending and healthcare providers’ costs. We explore structural properties of key supply chain decisions and the Pareto‐improving contracts, and conduct scenario analysis with realistic industry data to evaluate shortage mitigation solutions. Our analysis shows that increasing drug prices only, a solution advocated by many, is not very effective in shortage mitigation. Price increases must be paired with strengthened failure‐to‐supply clauses (called the IPS approach) to achieve consistent and significant shortage reduction as well as Pareto improvement. Across all scenarios tested, a 30% price increase under IPS can lead to a minimum, average, and maximum shortage reduction of 25%, 53%, and 70%, respectively. Our analysis also shows the impacts of IPS on different parties in the supply chain and the impacts of various model parameters on shortage mitigation. The IPS approach rewards reliability of drug supply, which is in line with the FDA's strategic plan to reward quality, but is easier to achieve in this regulation‐based industry. Interactions with the government and industry practitioners indicate that IPS also challenges the current mindset in pharmaceutical contracting.

We investigate the scheduling practices of a multidisciplinary, multistage, outpatient health care program. Patients undergo a series of assessments before being eligible for elective surgery. Such systems often suffer from high rates of attrition and appointment no‐shows leading to capacity underutilization and treatment delays. We propose a new scheduling model where the clinic assigns patients to an appointment day but postpones the decision of which assessments patients undergo pending the observation of who arrives. In doing so, the clinic gains flexibility to improve system performance. We formulate the scheduling problem as a Markov decision process and use approximate dynamic programming to solve it. We apply our approach to a dataset collected from a bariatric surgery program at a large tertiary hospital in Toronto, Canada. We examine the quality of our solutions via structural results and compare them with heuristic scheduling practices using a discrete‐event simulation. By allowing multiple assessments, delaying their scheduling, and by optimizing over an appointment book, we show significant improvements in patient throughput, clinic profit, use of overtime, and staff utilization.

The healthcare industry has invested heavily in electronic health records and other clinical information systems in order to improve caregivers' access to information and ability to share information with other care providers. It has been shown that these systems can readily induce in their users a state of information overload, where the volume and complexity of information overwhelms the user, leading to lower decision speed and quality. This research introduces and tests a cognitive technique called “emphasis framing” as an operational tactic to help mitigate the effects of information overload, thereby improving the quality and timeliness of clinical decision‐making. Emphasis framing is the highlighting or stressing of some aspect or component of the information being exchanged in order to make it more easily processed, or more likely to be processed, by the recipient. We conducted a controlled laboratory experiment with emergency department physicians experiencing information overload to measure the effect of emphasis framing on two operational performance metrics: (1) the quality of the physician's clinical evaluation, and (2) the efficiency (timeliness) of the physician's clinical decision‐making. Our findings show that the emphasis frame helped mitigate the effects of information overload and increased the quality of clinical decision‐making. Contrary to expectations, however, we found decision‐making took longer with the emphasis frame, reinforcing the need to consider the impacts of quality/speed trade‐offs. Implications for theory and practice are discussed.

In this issue, Cui et al. ( 2018 ) show how the quantity and quality of user‐generated Facebook data can be used to enhance product forecasts. The intent of this note is to show how another type of user‐generated content—customer search data, specifically one obtained from Google Trends—can be used to reduce out‐of‐sample forecast errors. Based on our work with an online retailer, we bolster Cui et al. ( 2018 ) result by showing that adding customer search data to time series models improves out‐of‐sample forecast errors.