Scheduling Emergency Physicians Based on a Multiobjective Programming Approach: A Case Study of West China Hospital of Sichuan University

Tan, Mingying; Junwei, Gan; Ren, Qunrong

doi:10.1155/2019/5647078

Cited by 6 publications

(3 citation statements)

References 25 publications

(23 reference statements)

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“…One important reason that has been reported in other studies is that doctors will tend to keep the patient admitted till morning for further workup [ 23 ]. To ensure uniformity in the length of stay across different shifts there is a need to adopt an appropriate scheduling algorithm to optimize the length of stay as it was demonstrated in a hospital in West China [ 24 ]. Since there are multiple rules and different objectives so in this case study, multi-objectives optimization scenario was created.…”

Section: Discussionmentioning

confidence: 99%

Influence of demographic factors on prolonged length of stay in an emergency department

Alnahari,

A’aqoulah

2024

PLoS ONE

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Background A prolonged length of stay in an emergency department is related to lower quality of care and adverse outcomes, which are often linked with overcrowding. Objective Examine the influence of demographic factors on prolonged length of stay in the emergency department. Methods This study used a cross-sectional design. It used secondary data for all patients admitted during the specific duration at the emergency department of a governmental hospital in Saudi Arabia. The independent variables were gender, age, disposition status, shift time, and clinical acuity (CTAS) level while the dependent variable was prolonged length of stay. Results The study shows that 30% of patients stay at the emergency department for four hours or more. The results also show a significant association between demographic factors which are age, gender, disposition status, shift time, clinical acuity (CTAS) level and prolonged length of stay in an emergency department. Based on the results males are more likely to stay in the emergency department than females (OR = 1.20; 95% CI = 1.04 to 1.38). Patients aged 60 and older are less likely to stay in the emergency department than patients aged 29 or smaller (OR = 0.58; 95% CI = 0.39 to 0.84). According to disposition status discharged patients after examination stays in the emergency department more than admitted patients after the examination (OR = 2.78; 95% CI = 1.67 to 4.99). Patients who come to the night shift are less likely to stay in the emergency department than patients who come in the morning shift (OR = 0.67; 95% CI = 0.56 to 0.81). Patients who are classified in level three of CTAS are less likely to stay in the emergency department than patients who are classified in level one (OR = 0.28; 95% CI = 0.88 to 0.023). Conclusion Demographic factors such as age, gender, shift time, disposition status and clinical acuity (CTAS) were important factors that needed to be considered to reduce the length of stay of patients in the emergency department. it is possible to formulate a machine learning model to predict the anticipated length of stay in the hospital for each patient. This prediction with an accepted margin of uncertainty will help the clinicians to communicate the evidence-based anticipated length of stay with the patient’s caregivers. In addition, hospital managers need to provide the emergency department with enough staff and materials to reduce the length of stay of patients.

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Section: Discussionmentioning

confidence: 99%

Influence of demographic factors on prolonged length of stay in an emergency department

Alnahari,

A’aqoulah

2024

PLoS ONE

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“…Like Carter & Lapierre (2001) that compiled characteristics of a hospital and modeled using MIP, Tan et al (2019) gathered a set of management rules and physicians' preferences between other features of a Chinese hospital to build a mixed-integer pro-gramming model for physician schedules in ERs. Fairness in this problem is very similar to Topaloglu (2006), Gendreau et al (2007, and Topaloglu & Ozkarahan (2011) since all of them aim to schedule fairly and reasonably the number of night shifts.…”

Section: Fairness In the Psp-ermentioning

confidence: 99%

Linear approximations to improve lower bounds of a physician scheduling problem in emergency rooms

Devesse

Akartunalı

Arantes

et al. 2022

Journal of the Operational Research Society

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The physician assignment process consists of coverage of shifts and duties allocated to physicians in a planning period, taking into account work regulations, individual preferences, and organizational rules, which mostly conflict with each other. In this work, we propose a reformulated mixed-integer programming model based on the literature to tackle fairness in physician scheduling in Emergency Rooms. In particular, we propose two mixed-integer quadratic programming formulations that consider quadratic costs and two models with linear costs. Our approaches provide balanced schedules concerning target hours and weekends in terms of fairness. Our models also provide a high degree of demand coverage, providing decision-makers a significant advantage.

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“…The 0-1 integer programming model, mixed integer programming model, and the goal programming model are constructed, and the exact algorithm and heuristic algorithm are used to solve them [12,13]. In order to obtain the scientific and reasonable scheduling shifts, Tan et al consider various management rules in a hospital, physicians' personal preferences, and the time requirements of their personal learning and living and take the minimum deviation variables from the soft constraints as the objective function to construct a mixed integer programming model with the doctor group as the scheduling unit [14]. In addition, some scholars use heuristic algorithms to solve the problem of nurses' scheduling.…”

Section: Introductionmentioning

confidence: 99%

Applying Queuing Theory and Mixed Integer Programming to Blood Center Nursing Schedules of a Large Hospital in China

Luo

Liu

Cui

et al. 2020

Computational and Mathematical Methods in Medicine

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Blood centers in large hospitals in China are facing serious problems, including complex patient queues and inflexible nursing schedules. This study is aimed at developing a flexible scheduling method for blood center nurses. By systematically analyzing the constraints that affect scheduling, a flexible scheduling model is established based on queuing theory and mixed integer programming. This combined model can reasonably determine the number of nurses required during a given working period and flexibly arrange nursing schedules while ensuring sufficient rest periods for individual nurses. Results of numerical studies conducted using data from a large hospital in China show a significant improvement in patient waiting time performance metrics over the hospital’s current practice. In addition, the nurses’ workloads and rest periods are well balanced, indicating that the proposed method can effectively and flexibly arrange nursing shifts in blood centers.

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Scheduling Emergency Physicians Based on a Multiobjective Programming Approach: A Case Study of West China Hospital of Sichuan University

Cited by 6 publications

References 25 publications

Influence of demographic factors on prolonged length of stay in an emergency department

Influence of demographic factors on prolonged length of stay in an emergency department

Linear approximations to improve lower bounds of a physician scheduling problem in emergency rooms

Applying Queuing Theory and Mixed Integer Programming to Blood Center Nursing Schedules of a Large Hospital in China

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