Scheduling inpatient admission under high demand of emergency patients

Mazier, Alexandre; Xie, Xiaolan; Sarazin, Marianne

doi:10.1109/coase.2010.5584679

Cited by 10 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among these works, Diefenbach and Kozan 27 focussed on evaluating six heuristics for scheduling patients in the ED to minimise the total tardiness. Mazier et al 28 addressed a stochastic problem to schedule inpatient admission in a hospital with an uncertain LOS and unexpected patients coming to the ED. Three approaches were proposed: deterministic approach, service ratios, and Monte Carlo optimisation.…”

Section: Literature Reviewmentioning

confidence: 99%

Simulation‐based optimisation approach to improve emergency department resource planning: A case study of Tunisian hospital

Daldoul

Nouaouri²,

Bouchriha

et al. 2022

Health Planning & Management

View full text Add to dashboard Cite

Background: The emergency department (ED) is a gateway to hospitals and is in the centre of hospital management efforts. It is often saturated by a continuous flow of patients, which causes excessive patient waiting time.Aims: This study integrates simulations with optimisation to design planning decision support for an ED. We considered all the processes of the ED, from triage to bed assignment. This study's main objective was to determine the optimal number of doctors, nurses, and beds required to schedule patients with different acuity levels to minimise both the total patient waiting time and the patient average length of stay and balance the resource utilisation rates. The problem is also characterised by multiple uncertainties, such as the patient arrival rate and service times in each stage of the process.Method: We first propose a stochastic mixed-integer programing model that is solved using the sample average approximation approach. The resulting resource sizing is then evaluated using a discrete-event simulation model by comparing different patient scheduling rules.Results: Numerical experiments highlight the performance of the proposed approach using data from a Tunisian ED hospital.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Simulation‐based optimisation approach to improve emergency department resource planning: A case study of Tunisian hospital

Daldoul

Nouaouri²,

Bouchriha

et al. 2022

Health Planning & Management

View full text Add to dashboard Cite

show abstract

“…Different admission strategies such as maximum resource usage that employ waiting lists, booked admissions (determining an admission date at request time) and zero wait (admitting a patient at request time) were compared with respect to several performance measures. Mazier et al [34] discussed the problem of scheduling inpatient admissions in a hospital with a highly uncertain LOS and a significant number of emergency admissions. Their main concern was to assure enough beds are available for unforeseen emergency patients and future elective patients.…”

Section: Related Workmentioning

confidence: 99%

Chance-constrained admission scheduling of elective surgical patients in a dynamic, uncertain setting

Vancroonenburg

Causmaecker

Berghe

2019

Operations Research for Health Care

View full text Add to dashboard Cite

In the present contribution, a chance-constrained scheduling model is presented for determining admission dates of elective surgical patients. The admission scheduling model is defined considering a dynamic, stochastic decision-making environment. The primary aim of the model concerns the minimization of operating theatre costs and patient waiting times, while simultaneously avoiding bed shortages at a fixed certainty level through a chance-constrained formulation. This stochastic model is implemented by means of sample average approximation and is solved by a meta-heuristic algorithm. To illustrate the applicability of the model, the approach is used to implement four admission scheduling policies on this dynamic decision-making setting that are evaluated on different criteria in a computational study using simulation.The results show that the stochastic approach is able to account for the uncertainty in patients' length of stay and surgical procedure duration, enabling it to avoid bed shortages while still optimizing operating theatre costs and patient waiting times.

show abstract

“…Nunes et al (2009) modeled the control of patient admissions as a Markov decision process, and showed its usefulness for sequential admission decision problems with stochastic characteristics and future states. In another study, Mazier et al (2010) used the Monte-Carlo simulation to produce an optimal plan for inpatient admission scheduling under the high demands of emergency wards.…”

Section: Introductionmentioning

confidence: 99%

Implementing a Monte‐Carlo simulation on admission decisions

Ben‐Assuli

Leshno

2013

Journal of Enterprise Information Management

View full text Add to dashboard Cite

Purpose -Although very significant and applicable, there have been no formal justifications for the use of Monte-Carlo models and Markov chains in evaluating hospital admission decisions or concrete data supporting their use. For these reasons, this research was designed to provide a deeper understanding of these models. The purpose of this paper is to examine the usefulness of a computerized Monte-Carlo simulation of admission decisions under the constraints of emergency departments. Design/methodology/approach -The authors construct a simple decision tree using the expected utility method to represent the complex admission decision process terms of quality adjusted life years (QALY) then show the advantages of using a Monte-Carlo simulation in evaluating admission decisions in a cohort simulation, using a decision tree and a Markov chain. Findings -After showing that the Monte-Carlo simulation outperforms an expected utility method without a simulation, the authors develop a decision tree with such a model. real cohort simulation data are used to demonstrate that the integration of a Monte-Carlo simulation shows which patients should be admitted. Research limitations/implications -This paper may encourage researchers to use Monte-Carlo simulation in evaluating admission decision implications. The authors also propose applying the model when using a computer simulation that deals with various CVD symptoms in clinical cohorts. Originality/value -Aside from demonstrating the value of a Monte-Carlo simulation as a powerful analysis tool, the paper's findings may prompt researchers to conduct a decision analysis with a Monte-Carlo simulation in the healthcare environment.

show abstract

Scheduling inpatient admission under high demand of emergency patients

Cited by 10 publications

References 14 publications

Simulation‐based optimisation approach to improve emergency department resource planning: A case study of Tunisian hospital

Simulation‐based optimisation approach to improve emergency department resource planning: A case study of Tunisian hospital

Chance-constrained admission scheduling of elective surgical patients in a dynamic, uncertain setting

Implementing a Monte‐Carlo simulation on admission decisions

Contact Info

Product

Resources

About