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Background/objectives: The race-sex differences in emergency department (ED) disposition decisions have been reported widely. Our objective is to identify demographic and clinical subgroups for which this difference is most pronounced, which will facilitate future targeted research on potential disparities and interventions. Methods: We performed a retrospective analysis of 93 987 White and African-American adults assigned an Emergency Severity Index of 3 at 3 large EDs from January 2019 to February 2020. Using random forests, we identified the Elixhauser comorbidity score, age, and insurance status as important variables to divide data into subpopulations. Logistic regression models were then fitted to test race-sex differences within each subpopulation while controlling for other patient characteristics and ED conditions. Results: In each subpopulation, African-American women were less likely to be admitted than White men with odds ratios as low as 0.304 (95% confidence interval (CI): [0.229, 0.404]). African-American men had smaller admission odds compared to White men in subpopulations of 41+ years of age or with very low/high Elixhauser scores, odds ratios being as low as 0.652 (CI: [0.590, 0.747]). White women were less likely to be admitted than White men in subpopulations of 18 to 40 or 41 to 64 years of age, with low Elixhauser scores, or with Self-Pay or Medicaid insurance status with odds ratios as low as 0.574 (CI: [0.421, 0.784]). Conclusions: While differences in likelihood of admission were lessened by younger age for African-American men, and by older age, higher Elixhauser score, and Medicare or Commercial insurance for White women, they persisted in all subgroups for African-American women. In general, patients of age 64 years or younger, with low comorbidity scores, or with Medicaid or no insurance appeared most prone to potential disparities in admissions. [ABSTRACT FROM AUTHOR]

There is limited evidence on sex, racial, and ethnic disparities in Emergency Department (ED) triage across diverse settings. We evaluated differences in the assignment of Emergency Severity Index (ESI) by patient sex and race/ethnicity, accounting for age, clinical factors, and ED operating conditions. We conducted a multi-site retrospective study of adult patients presenting to high-volume EDs from January 2019–February 2020. Patient-level data were obtained and analyzed from three EDs (academic, metropolitan community, and rural community) affiliated with a large health system in the Southeastern United States. For the study outcome, ESI levels were grouped into three categories: 1–2 (highest acuity), 3, and 4–5 (lowest acuity). Multinomial logistic regression was used to compare ESI categories by patient race/ethnicity and sex jointly (referent = White males), adjusted for patient age, insurance status, ED arrival mode, chief complaint category, comorbidity score, time of day, day of week, and average ED wait time. We identified 186,840 eligible ED visits with 56,417 from the academic ED, 69,698 from the metropolitan community ED, and 60,725 from the rural community ED. Patient cohorts between EDs varied by patient age, race/ethnicity, and insurance status. The majority of patients were assigned ESI 3 in the academic and metropolitan community EDs (61% and 62%, respectively) whereas 47% were assigned ESI 3 in the rural community ED. In adjusted analyses, White females were less likely to be assigned ESI 1–2 compared to White males although both groups were roughly comparable in the assignment of ESI 4–5. Non-White and Hispanic females were generally least likely to be assigned ESI 1–2 in all EDs. Interactions between ED wait time and race/ethnicity-sex were not statistically significant. This retrospective study of adult ED patients revealed sex and race/ethnicity-based differences in ESI assignment, after accounting for age, clinical factors, and ED operating conditions. These disparities persisted across three different large EDs, highlighting the need for ongoing research to address inequities in ED triage decision-making and associated patient-centered outcomes. [ABSTRACT FROM AUTHOR]

In emergency departments (EDs), one of the major reasons behind long waiting times and crowding overall is the time it takes to move admitted patients from the ED to an appropriate bed in the main hospital. In "Using Hospital Admission Predictions at Triage for Improving Patient Length of Stay in Emergency Departments," Chen et al. develop a methodology that can be used to shorten these times by predicting the likelihood of admission for each patient at the time of triage and starting the process of identifying a suitable hospital bed and making preparations for the patient's eventual transfer to the bed right away if the predicted probability of admission is deemed high enough. A simulation study suggests that the proposed methodology, particularly when it takes into account ED census levels, has the potential to shorten average waiting times in the ED without leading to too many false early bed requests. Long boarding times have long been recognized as one of the main reasons behind emergency department (ED) crowding. One of the suggestions made in the literature to reduce boarding times was to predict, at the time of triage, whether a patient will eventually be admitted to the hospital and if the prediction turns out to be "admit," start preparations for the patient's transfer to the main hospital early in the ED visit. However, there has been no systematic effort in developing a method to help determine whether an estimate for the probability of admit would be considered high enough to request a bed early, whether this determination should depend on ED census, and what the potential benefits of adopting such a policy would be. This paper aims to help fill this gap. The methodology we propose estimates hospital admission probabilities using standard logistic regression techniques. To determine whether a given probability of admission is high enough to qualify a bed request early, we develop and analyze two mathematical decision models. Both models are simplified representations and thus, do not lead to directly implementable policies. However, building on the solutions to these simple models, we propose two policies that can be used in practice. Then, using data from an academic hospital ED in the southeastern United States, we develop a simulation model, investigate the potential benefits of adopting the two policies, and compare their performances with that under a simple benchmark policy. We find that both policies can bring modest to substantial benefits, with the state-dependent policy outperforming the state-independent one particularly under conditions when the ED experiences more than usual levels of patient demand. Funding: This work was supported by the National Science Foundation [Grants CMMI-1234212 and CMMI-1635574]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2405. [ABSTRACT FROM AUTHOR]

Abstract. We study the pricing and capacity allocation problem of a service provider who serves two distinct customer classes. Customers in each class are inherently heterogeneous in their willingness to [...]

The most widely used standard for mass-casualty triage, START, relies on a fixed-priority ordering among different classes of patients, and does not explicitly consider resource limitations or the changes in [...]

We consider an appointment-based service system (e.g., an outpatient clinic) for which appointments need to be scheduled before the service session starts. Patients with scheduled appointments may or may not [...]

This paper develops a framework and proposes heuristic dynamic policies for scheduling patient appointments, taking into account the fact that patients may cancel or not show up for their appointments. [...]

In many service systems, customers are not served in the order they arrive, but according to a priority scheme that ranks them with respect to their relative 'importance.' However, it [...]

System throughput is one of the widely used performance measures in manufacturing, communication, and service networks. Although there are exceptions, throughput of such systems typically increases with additional capacity. We investigate how this improvement in throughput depends on the traffic load. More specifically, we consider M/M/1/m, M/G/1/m--P S, and M/G/c/c queues, all of which arise in a variety of contexts. For the M/G/c/c queue, we show that throughput improvement (both nominal and relative) that would be obtained by adding an extra server is increasing in the traffic load. For the M/M/1/m and M/G/1/m--P S queues, we show that throughput improvement (both nominal and relative) that would be obtained by adding an extra buffer space is unimodal in traffic load. In particular, the relative improvement is maximized when the traffic load is one regardless of the buffer size. We also prove a new structural property for the blocking probability of the M/M/1/m and M/G/1/m--PS queues. Index Terms--M/M/1/m, M/G/1/m--P S, and M/G/c/c queues, throughput.

Upselling is offering an additional product to a customer who just made a purchase. Most catalogers and online sellers, in addition to some traditional retailers, use upselling often to clear [...]

For a queueing system with multiple customer types differing in service-time distributions and waiting costs, it is well known that the cµ-rule is optimal if costs for waiting are incurred linearly with time. In this paper, we seek to identify policies that minimize the long-run average cost under nonlinear waiting cost functions within the set of fixed priority policies that only use the type identities of customers independently of the system state. For a single-server queueing system with Poisson arrivals and two or more customer types, we first show that some form of the cµ-rule holds with the caveat that the indices are complex, depending on the arrival rate, higher moments of service time, and proportions of customer types. Under quadratic cost functions, we provide a set of conditions that determine whether to give priority to one type over the other or not to give priority but serve them according to first-come, first-served (FCFS). These conditions lead to useful insights into when strict (and fixed) priority policies should be preferred over FCFS and when they should be avoided. For example, we find that, when traffic is heavy, service times are highly variable, and the customer types are not heterogenous, so then prioritizing one type over the other (especially a proportionally dominant type) would be worse than not assigning any priority. By means of a numerical study, we generate further insights into more specific conditions under which fixed priority policies can be considered as an alternative to FCFS. This paper was accepted by Baris Ata, stochastic models and simulation. [ABSTRACT FROM AUTHOR]

In service systems with heterogeneous customers, prioritization with respect to the relative importance of customers is known to improve certain performance measures. However, in many applications, information necessary to determine the importance level of a customer may not be available immediately but can be revealed only through some preliminary investigation, which is sometimes called triage. This triage process is typically error‐prone and may take substantial amount of time, and hence, it is not always clear if and when it should be implemented for purposes of priority assignment. To provide insights into this question, we study a stylized queueing model with a single server and two types of customers with hidden type identities, which differ in their rates of service and waiting costs. By means of a Markov decision formulation, we first show that the optimal dynamic policy on triage is characterized by a switching curve. The comparison of two state‐independent policies (no‐triage and triage‐all) shows that the information from triage is more beneficial when the traffic intensity is neither too low nor too high. Our numerical results show that the system manager should consider implementing a state‐dependent triage policy when the probability of classifying a customer into the important class and the mean triage time are of moderate size, when the difference between the importance levels of the two classes of customers is large, and/or when the traffic intensity is high. [ABSTRACT FROM AUTHOR]

Abstract Objectives A recent joint report from the Institute of Medicine and the National Academy of Engineering, highlights the benefits of--indeed, the need for--mathematical analysis of healthcare delivery. Tools for such analysis have been developed over decades by researchers in Operations Research (OR). An OR perspective typically frames a complex problem in terms of its essential mathematical structure. This article illustrates the use and value of the tools of operations research in healthcare. It reviews one OR tool, queueing theory, and provides an illustration involving a hypothetical drug treatment facility. Method Queueing Theory (QT) is the study of waiting lines. The theory is useful in that it provides solutions to problems of waiting and its relationship to key characteristics of healthcare systems. More generally, it illustrates the strengths of modeling in healthcare and service delivery. Queueing theory offers insights that initially may be hidden. For example, a queueing model allows one to incorporate randomness, which is inherent in the actual system, into the mathematical analysis. As a result of this randomness, these systems often perform much worse than one might have guessed based on deterministic conditions. Poor performance is reflected in longer lines, longer waits, and lower levels of server utilization. As an illustration, we specify a queueing model of a representative drug treatment facility. The analysis of this model provides mathematical expressions for some of the key performance measures, such as average waiting time for admission. Results We calculate average occupancy in the facility and its relationship to system characteristics. For example, when the facility has 28 beds, the average wait for admission is 4 days. We also explore the relationship between arrival rate at the facility, the capacity of the facility, and waiting times. Conclusions One key aspect of the healthcare system is its complexity, and policy makers want to design and reform the system in a way that affects competing goals. OR methodologies, particularly queueing theory, can be very useful in gaining deeper understanding of this complexity and exploring the potential effects of proposed changes on the system without making any actual changes.