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Although medical research has addressed the clinical management of chronic opioid users, little is known about how operational interventions shortly after opioid initiation can impact a patient's likelihood of long-term opioid use. Using a nationwide U.S. database of medical and pharmaceutical claims, we investigate the care delivery process at the most common entry point to opioid use: the primary care setting. For patients who return to primary care for a follow-up appointment within 30 days of opioid initiation, we ask who should revisit and potentially revise the opioid-based treatment plan: the initial prescriber (provider concordance) or an alternate clinician (provider discordance)? First, using a fully controlled logistic model, we find that provider discordance reduces the likelihood of long-term opioid use 12 months after opioid initiation by 31% (95% Confidence Interval: [18%, 43%]). Both the instrumental variable analysis technique and propensity-score matching (utilizing the minimum-bias estimator approach) account for omitted variable bias and indicate that this is a conservative estimate of the true causal effect. Second, looking at patient activities immediately after the follow-up appointment, we find that this long-term reduction is at least partially explained by an immediate reduction in opioids prescribed after the follow-up appointment. Third, the data suggest that the benefit associated with provider discordance remains significant regardless of whether the patient's initial prescriber was their regular primary care provider or another clinician. Overall, our analysis indicates that systematic, operational changes in the early stages of managing new opioid patients may offer a promising, and hitherto overlooked, opportunity to curb the opioid epidemic. This paper was accepted by David Simchi-Levi, healthcare management. [ABSTRACT FROM AUTHOR]

Existing literature suggests that hospital occupancy matters for quality of care, as measured by various patient outcomes. However, estimating the causal effect of increased hospital busyness on in-hospital mortality remains an elusive task due to statistical power challenges and the difficulty in separating shocks to occupancy from changes in patient composition. Using data from a large public hospital system in Mexico, we estimate the impact of congestion on in-hospital mortality by exploiting the shock in hospitalizations induced by the 2009 H1N1 pandemic, instrumenting hospital admissions due to acute respiratory infections (ARIs) with measures of ARI cases at nearby healthcare facilities as a proxy for the size of the local outbreak. Our instrumental-variables estimates show that a 1% increase in ARI admissions in 2009 led to a 0.25% increase in non-ARI in-hospital mortality. We show that these effects are nonlinear in the size of the local outbreak, consistent with the existence of tipping points. We further show that effects are concentrated at hospitals with limited infrastructure, suggesting that supply-side policies that improve patient assignment across hospitals and strategically increase hospital capacity could mitigate some of the negative impacts. We discuss managerial implications, suggesting that up to 25%–30% of our estimated deaths at small and non-intensive-care-unit hospitals could have been averted by reallocating patients to reduce congestion. This paper was accepted by Carri Chan, healthcare management. [ABSTRACT FROM AUTHOR]

Providing patients with timely care from the appropriate unit involves both correct clinical evaluation of patient needs and making admission decisions to effectively manage a unit with limited capacity in the face of stochastic patient arrivals and lengths of stay. We study human decision behavior in the latter operations management task. Using behavioral models and controlled experiments in which physicians and MTurk workers manage a simulated hospital unit, we identify cognitive and environmental factors that drive systematic admission decision bias. We report on two main findings. First, seemingly innocuous "occupancy information hurdles" (e.g., having to type a password to view current occupancy) can cause a chain of events that leads physicians to maintain systematically lower unit utilization. Specifically, these hurdles cause physicians to make most admission decisions without checking the current unit occupancy. Then—between the times that they do check—physicians underestimate the number of available beds when occupancy increases from admissions are more salient than occupancy decreases from discharges. Second, decision-related random error or "noise" leads to higher- or lower-than-optimal utilization of hospital units in predictable patterns, depending on the system parameters. We provide evidence that these patterns are due to some settings providing more opportunity for physicians to mistakenly admit patients and other settings that provide more opportunity to mistakenly reject patients. These findings help identify when and why clinicians are likely to make inefficient decisions because of human cognitive limitations and suggest mitigation strategies to help hospital units improve their capacity management. This paper was accepted by Charles Corbett, operations management. [ABSTRACT FROM AUTHOR]

Emergency care necessitates adequate and timely treatment, which has unfortunately been compromised by crowding in many emergency departments (EDs). To address this issue, we study patient scheduling in EDs so that mandatory targets imposed on each patient's door-to-provider time and length of stay can be collectively met with the largest probability. Exploiting patient flow data from the ED, we propose a hybrid robust-stochastic approach to formulating the patient scheduling problem, which allows for practical features, such as a time-varying patient arrival process, general consultation time distributions, and multiple heterogeneous physicians. In contrast to the conventional formulation of maximizing the joint probability of target attainment, which is computationally excruciating, the hybrid approach provides a computationally amiable formulation that yields satisfactory solutions to the patient scheduling problem. This formulation enables us to develop a dynamic scheduling algorithm for making recommendations about the next patient to be seen by each available physician. In numerical experiments, the proposed hybrid approach outperforms both the sample average approximation method and an asymptotically optimal scheduling policy. [ABSTRACT FROM AUTHOR]

Secure messaging, or "e-visits," between patients and providers has sharply increased in recent years, and many hope they will help improve healthcare quality, while increasing provider capacity. Using a panel data set from a large healthcare system in the United States, we find that e-visits trigger about 6% more office visits, with mixed results on phone visits and patient health. These additional visits come at the sacrifice of new patients: physicians accept 15% fewer new patients each month following e-visit adoption. Our data set on nearly 100,000 patients spans from 2008 to 2013, which includes the rollout and diffusion of e-visits in the health system we study. Identification comes from difference-in-differences estimates leveraging variation in the timing of e-visit adoption by both patients and providers. We conduct several robustness checks, including matching analyses and an instrumental variable analysis to account for possible time-varying characteristics among patient e-visit adopters. This paper was accepted by Chris Forman, information systems. [ABSTRACT FROM AUTHOR]

Many models in operations management involve dynamic decision making that assumes optimal updating in response to information revelation. However, behavioral theory suggests that rather than updating their beliefs, individuals may persevere in their prior beliefs. In particular, we examine how individuals' prior experiences and the experiences of those around them alter their belief perseverance in operational decisions after the revelation of negative news. We draw on an exogenous announcement of negative news by the Food and Drug Administration and explore how it affects interventional cardiologists deciding between two types of cardiac stents. Analyzing 147,000 choices over six years, we find that individuals do respond to negative news by using the focal production tool less often. However, we find that both individuals' own experiences and others' experiences alter their responses. Moreover, although individual and other experience act as substitutes before negative news, we find that this substitution significantly curtails following the negative announcement. Finally, we find that experience leads doctors to discount negative news more rapidly over time. Two lab studies replicate our main findings and show that behavioral biases due to differences in perceptions of expertise drive the effect. Our research contributes not only to operations research but also to the practice of healthcare and operations. [ABSTRACT FROM AUTHOR]

We study a multistage service process that adapts to system occupancy level. Using operational data from more than 140,000 patient visits to a hospital emergency department, we show that the system-level performance of the emergency department is an aggregation of several simultaneous server-level workload response mechanisms. We identify early task initiation as a between-stage adaptive response mechanism that occurs when an upstream stage initiates tasks that are normally handled by a downstream stage. We show that having some diagnostic tests ordered during the triage process reduces treatment time by 20 minutes, on average. However, ordering too many tests at triage can lead to an increase in the total number of tests performed on the patient. We also demonstrate the presence of other response mechanisms such as queuing delays for tasks such as medication delivery, and rushing as nurses spend less time with their patients when the queue length is high. This paper was accepted by Serguei Netessine, operations management. [ABSTRACT FROM AUTHOR]

We study queue abandonment from a hospital emergency department. We show that abandonment is influenced by the queue length and the observable queue flows during the waiting exposure, even after controlling for wait time. For example, observing an additional person in the queue or an additional arrival to the queue leads to an increase in abandonment probability equivalent to a 25-minute or 5-minute increase in wait time, respectively. We also show that patients are sensitive to being "jumped" in the line and that patients respond differently to people more sick and less sick moving through the system. This customer response to visual queue elements is not currently accounted for in most queuing models. Additionally, to the extent the visual queue information is misleading or does not lead to the desired behavior, managers have an opportunity to intervene by altering what information is available to waiting customers. [ABSTRACT FROM AUTHOR]