Adjusting Daily Inpatient Bed Allocation to Smooth Emergency Department Occupancy Variation

Optimizing Emergency Department Patient Flow Through Bed Allocation Strategies: A Discrete-Event Simulation Study

Emergency department (ED) overcrowding and prolonged length of stay (LOS) remain critical issues in healthcare systems. This study compared 4 bed allocation strategies to optimize patient flow and resource utilization in a regional teaching hospital in Taiwan. A discrete-event simulation model was developed using 1 year hospital data from January 2022, including 29 718 ED visits. The following strategies were evaluated: (1) intra-departmental bed sharing, (2) optimized bed allocation, (3) cross-departmental bed lending with 5% capacity, and (4) combined optimization with bed borrowing. The model was validated by t-tests comparing the simulation outputs with actual hospital data. Results: All strategies demonstrated improvement compared to current operations. Of these, Strategy 4, combined optimization with bed borrowing, was the most promising: it maintained stable ED nursing utilization at 45.65% with a 95% confidence interval (CI) of 45.60% to 45.71% while reducing the cases of extended LOS. The rates of ED LOS exceeding 6, 12, and 24 h were 2.48%, 0.38%, and 0.12%, respectively, which is a significant improvement compared with the baseline. Optimization alone contributed to a 20% improvement in extended LOS under Strategy 2, while additional bed-sharing policies further improved performance by 10%. Conclusions: Strategic bed allocation combined with controlled bed-sharing policies achieved a 30% reduction in extended ED LOS without increasing nursing workload. The optimal strategy (Strategy 4) reduced cases of ED LOS exceeding 6 h to 2.48% while maintaining stable nursing utilization at 45.65%, demonstrating the effectiveness of combining optimization with resource sharing in ED patient flow management.

Regional Inequality and Associated Factors of Emergency Medicine Beds Distribution in China

Objective: The regional inequality of emergency medicine beds distribution has a great impact on population health as well as the accessibility of emergency services. This study aimed to explore the regional inequality of emergency medicine bed distribution and its influencing factors. Methods: The Gini coefficient and health resource agglomeration were used to analyze the regional inequality of emergency medicine beds distribution by area from 2012 to 2021 in China. Grey correlation models were used to explore the factors influencing the regional inequality of emergency medicine beds distribution. Results: From 2012 to 2021, Gini coefficients of emergency medicine beds distribution by geographic in China showed a worsening trend, rising from 0.6229 to 0.6636. The average HRAD index was 3.43 in the east and 0.44 in the west. Population structure factors have the greatest influence on the regional inequality of emergency medicine beds distribution. Conclusion: Health resources allocation strategy only according to population size should be changed. In formulating policies for emergency medicine beds allocation should take into account population structure, financial structure of expenditure, the inequality of geographical distribution and so on.

Evaluating Different Strategies on the Blood Collection Counter Settings to Improve Patient Waiting Time in Outpatient Units

Long patient waiting time is one of the major problems in the healthcare system and it would decrease patient satisfaction. Previous studies usually investigated how to improve the treatment flow in order to reduce patient waiting time or length of stay. The studies on blood collection counters have received less attention. Therefore, the objective of this study is to reduce the patient waiting time at outpatient clinics for metabolism and nephrology outpatients. A discrete-event simulation is used to analyze the four different strategies for blood collection counter resource allocation. Through analyzing four different strategic settings, the experimental results revealed that the maximum number of patients waiting before the outpatient clinics was reduced from 41 to 33 (20%); the maximum patient waiti-ng time at the outpatient clinics was decreased from 201.6 minutes to 83 minutes (59%). In this study, we found that adjusting the settings of blood collection counters would be beneficial. Assigning one exclusive blood collection counter from 8 to 10 am is the most suitable option with the least impact on the operational process for hospital staff. The results provide managerial insight regarding the cost-effective strategy selection for the hospital operational strategy.

Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review

Discrete-event simulation (DES) is a stochastic modeling approach widely used to address dynamic and complex systems, such as healthcare. In this review, academic databases were systematically searched to identify 231 papers focused on DES modeling in healthcare. These studies were sorted by year, approach, healthcare setting, outcome, provenance, and software use. Among the surveys, conceptual/theoretical studies, reviews, and case studies, it was found that almost two-thirds of the theoretical articles discuss models that include DES along with other analytical techniques, such as optimization and lean/six sigma, and one-third of the applications were carried out in more than one healthcare setting, with emergency departments being the most popular. Moreover, half of the applications seek to improve time- and efficiency-related metrics, and one-third of all papers use hybrid models. Finally, the most popular DES software is Arena and Simul8. Overall, there is an increasing trend towards using DES in healthcare to address issues at an operational level, yet less than 10% of DES applications present actual implementations following the modeling stage. Thus, future research should focus on the implementation of the models to assess their impact on healthcare processes, patients, and, possibly, their clinical value. Other areas are DES studies that emphasize their methodological formulation, as well as the development of frameworks for hybrid models.