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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]

OBJECTIVE: This study profiles an innovative approach to capture patient satisfaction data from emergency department (ED) patients by implementing an electronic survey method. This study compares responders to nonresponders. BACKGROUND: Our hypothesis is that the cohort of survey respondents will be similar to nonresponders in terms of the key characteristics of age, gender, race, ethnicity, ED disposition, and payor status. METHODS: This study is a cross-sectional design using secondary data from the database and provides an opportunity for univariate analysis of the key characteristics for each group. The data elements will be abstracted from the database and compared with the same key characteristics from a similar sample from the database on nonresponders to the ED satisfaction survey. FINDINGS: Age showed a statistically significant difference between responders and nonresponders. Comparison by disposition status showed no substantial difference between responders and nonresponders. Gender distribution showed a greater number of female than male responders. Race distribution showed a greater number and response by white and Asian patients as compared with African Americans. A review of ethnicity showed fewer Hispanics responded. An evaluation by payor classification showed greater number and response rate by those with a commercial or Workers Comp payor source. The response rate by Medicare recipients was stronger than expected; however, the response rate by Medicaid recipients and self-pay could be a concern for underrepresentation by lower socioeconomic groups. Finally, the evaluation of the method of notification showed that notification by both e-mail and text substantially improved response rates. CONCLUSION: The evaluation of key characteristics showed no difference related to disposition, but differences related to age, gender, race, ethnicity, and payor classification. These results point to a potential concern for underrepresentation by lower socioeconomic groups. The results showed that notification by both e-mail and text substantially improved response rates. [ABSTRACT FROM AUTHOR]

Background: Hospital-based Emergency Departments are struggling to provide timely care to a steadily increasing number of unscheduled ED visits. Dwindling compensation and rising ED closures dictate that meeting this challenge demands greater operational efficiency.Methods: Using techniques from operations research theory, as well as a novel event-driven algorithm for processing priority queues, we developed a flexible simulation platform for hospital-based EDs. We tuned the parameters of the system to mimic U.S. nationally average and average academic hospital-based ED performance metrics and are able to assess a variety of patient flow outcomes including patient door-to-event times, propensity to leave without being seen, ED occupancy level, and dynamic staffing and resource use.Results: The causes of ED crowding are variable and require site-specific solutions. For example, in a nationally average ED environment, provider availability is a surprising, but persistent bottleneck in patient flow. As a result, resources expended in reducing boarding times may not have the expected impact on patient throughput. On the other hand, reallocating resources into alternate care pathways can dramatically expedite care for lower acuity patients without delaying care for higher acuity patients. In an average academic ED environment, bed availability is the primary bottleneck in patient flow. Consequently, adjustments to provider scheduling have a limited effect on the timeliness of care delivery, while shorter boarding times significantly reduce crowding. An online version of the simulation platform is available at http://spark.rstudio.com/klopiano/EDsimulation/.Conclusion: In building this robust simulation framework, we have created a novel decision-support tool that ED and hospital managers can use to quantify the impact of proposed changes to patient flow prior to implementation. [ABSTRACT FROM AUTHOR]