A stochastic model to minimize patient waiting time in an emergency department

Daldoul, Dorsaf; Nouaouri, Issam; Bouchriha, Hanen; Allaoui, Hamid

doi:10.1016/j.orhc.2018.01.008

Cited by 41 publications

(39 citation statements)

References 16 publications

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“…Inadequate involvement of staff utilization in ED increases throughput time. Inadequate staff involvement also increases the likelihood of making improper decisions for patients or incorrect diagnoses, which lead to an improper treatment manner and low cost effectiveness with respect to time consumption [72].…”

Section: Discussionmentioning

confidence: 99%

SIM-PFED: A Simulation-Based Decision Making Model of Patient Flow for Improving Patient Throughput Time in Emergency Department

2021

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Healthcare sectors face multiple threats, and the hospital emergency department (ED) is one of the most crucial hospital areas. ED plays a key role in promoting hospitals' goals of enhancing service efficiency. ED is a complex system due to the stochastic behavior of patient arrivals, the unpredictability of the care required by patients, and the department's complex nature. Simulations are effective tools for analyzing and optimizing complex ED operations. Although existing ED simulation models have substantially improved ED performance in terms of ensuring patient satisfaction and effective treatment services, many deficiencies continue to exist in addressing the key challenge in ED, namely, long patient throughput time. The patient throughput time issue is affected by causative factors, such as waiting time, length of stay, and decision-making. This research aims to develop a new simulation model of patient flow for ED (SIM-PFED) to address the reported key challenge of the patient throughput time. SIM-PFED introduces a new process for patient flow in ED on the basis of the newly proposed operational patient flow by combining discrete event simulation and agent-based simulation and applying a multi-attribute decisionmaking method, namely, the technique for order preference by similarity to the ideal solution. Experiments were performed on three actual hospital ED datasets to assess the effectiveness of SIM-PFED. Experimental results revealed the superiority of SIM-PFED over other alternative models in reducing patient throughput time in ED by consuming less patient waiting time and having a shorter length of stay. The findings also demonstrated the effectiveness of SIM-PFED in helping ED decision-makers select the best scenarios to be implemented in ED for ensuring minimal throughput time while being cost effective.

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Section: Discussionmentioning

confidence: 99%

SIM-PFED: A Simulation-Based Decision Making Model of Patient Flow for Improving Patient Throughput Time in Emergency Department

2021

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“…More recently, Acuna et al [132] opted to use a robust approach integrated by mixed integer programming, game theory, and single/bi-objective optimization models for improving ambulance allocation and consequently reducing patients' waiting time in 11 EDs located in Florida. Other examples in the application of integrated OR methods when dealing with lengthy ED waits are provided in Daldoul et al [168], He et al [109], Lau et al [175], Othman et al [178], Sir et al [122], and Umble and Umble [182]. Other combinations aiming at facing the extended waiting times are simplified in Mazzocato et al [177], Othman and Hammadi [179], Perry [180], and Stephens and Broome [181].…”

Section: Papers Focusing On Reducing the Waiting Timementioning

confidence: 99%

Methodological Approaches to Support Process Improvement in Emergency Departments: A Systematic Review

Ortíz‐Barrios

Alfaro-Saiz

2020

IJERPH

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The most commonly used techniques for addressing each Emergency Department (ED) problem (overcrowding, prolonged waiting time, extended length of stay, excessive patient flow time, and high left-without-being-seen (LWBS) rates) were specified to provide healthcare managers and researchers with a useful framework for effectively solving these operational deficiencies. Finally, we identified the existing research tendencies and highlighted opportunities for future work. We implemented the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to undertake a review including scholarly articles published between April 1993 and October 2019. The selected papers were categorized considering the leading ED problems and publication year. Two hundred and three (203) papers distributed in 120 journals were found to meet the inclusion criteria. Furthermore, computer simulation and lean manufacturing were concluded to be the most prominent approaches for addressing the leading operational problems in EDs. In future interventions, ED administrators and researchers are widely advised to combine Operations Research (OR) methods, quality-based techniques, and data-driven approaches for upgrading the performance of EDs. On a different tack, more interventions are required for tackling overcrowding and high left-without-being-seen rates.

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“…[11] Some studies incorporate demand uncertainty in their models, but most of these studies are focused on the emergency department. [10,12] However, there is uncertain demand in the other health care departments as well. [10] In addition, the majority of the studies in the literature view demand as deterministic, which neglects the realistic physician demand profile.…”

Section: Introductionmentioning

confidence: 99%

Provider time allotment tracking tool to effectively manage assignment commitments

Huang

Shahraki

Wallin

et al. 2021

JHA

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Due to the rising demand with limited health service capacity, managing available resources effectively becomes an important task to reduce patient care delays and avoid unnecessary and costly capacity expansions. At the same time, staff satisfaction and/or burnout is a complementary consideration when designing optimal schedules. Deviation from the scheduled plan can cause delays in patient access and may lead to unsatisfaction among providers. Balancing demand management, staff satisfaction and generating optimized schedules quickly reveals the need for a tool that tracks provider time allotment over time, especially for the academic healthcare organization where providers are committed to multiple assignments, clinical and non-clinical. This tracking tool should allow management to proactively adjust allotment to unplanned changes in the schedule and increase participation. In this study, a tool is developed to track monthly provider assignments for the Department of Cardiovascular Medicine at Mayo Clinic, Rochester. The proposed tool produces two key outputs for each provider and assignment: 1) the recommended target workdays and 2) workday upper and lower bounds to accommodate for variability. This tracking tool is successfully implemented with implementation criteria, and the feedback is positive. The tool pulls the data systematically from the Mayo data platform and performs the necessary analysis on the data. It also automatically updates the values for the recommended target as well as upper and lower bounds for the remaining months in a year based on changes in the schedule so that provider commitment can be met at the end of year.

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A stochastic model to minimize patient waiting time in an emergency department

Cited by 41 publications

References 16 publications

SIM-PFED: A Simulation-Based Decision Making Model of Patient Flow for Improving Patient Throughput Time in Emergency Department

SIM-PFED: A Simulation-Based Decision Making Model of Patient Flow for Improving Patient Throughput Time in Emergency Department

Methodological Approaches to Support Process Improvement in Emergency Departments: A Systematic Review

Provider time allotment tracking tool to effectively manage assignment commitments

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