Duty rostering for physicians at a department of orthopedics and trauma surgery

Thielen, Clemens

doi:10.1016/j.orhc.2018.03.004

Cited by 6 publications

(3 citation statements)

References 13 publications

(17 reference statements)

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“…The approximation of time-related arrivals and patient characteristics (e.g., gender, age, and disease) is especially in focus for emergency patients, while no-show rates are interesting for elective patients. Another topic of research interest is to integrate upstream and/or downstream processes in the decision model (see e.g., [ 29 ], such as patient admission scheduling (see e.g., [ 30 ]), operating room scheduling (see e.g., [ 4 , 21 , 38 ]), bed transport services (see e.g., [ 7 , 42 ]) or staff rostering (see e.g., [ 19 , 45 ]). This integration makes it possible to obtain information about conflicts of interests of individual problems.…”

Section: Conclusion and Further Areas Of Researchmentioning

confidence: 99%

Combining machine learning and optimization for the operational patient-bed assignment problem

Schäfer,

Walther,

Grimm

et al. 2023

Health Care Manag Sci

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Assigning inpatients to hospital beds impacts patient satisfaction and the workload of nurses and doctors. The assignment is subject to unknown inpatient arrivals, in particular for emergency patients. Hospitals, therefore, need to deal with uncertainty on actual bed requirements and potential shortage situations as bed capacities are limited. This paper develops a model and solution approach for solving the patient bed-assignment problem that is based on a machine learning (ML) approach to forecasting emergency patients. First, it contributes by improving the anticipation of emergency patients using ML approaches, incorporating weather data, time and dates, important local and regional events, as well as current and historical occupancy levels. Drawing on real-life data from a large case hospital, we were able to improve forecasting accuracy for emergency inpatient arrivals. We achieved up to 17% better root mean square error (RMSE) when using ML methods compared to a baseline approach relying on averages for historical arrival rates. We further show that the ML methods outperform time series forecasts. Second, we develop a new hyper-heuristic for solving real-life problem instances based on the pilot method and a specialized greedy look-ahead (GLA) heuristic. When applying the hyper-heuristic in test sets we were able to increase the objective function by up to 5.3% in comparison to the benchmark approach in [40]. A benchmark with a Genetic Algorithm shows also the superiority of the hyper-heuristic. Third, the combination of ML for emergency patient admission forecasting with advanced optimization through the hyper-heuristic allowed us to obtain an improvement of up to 3.3% on a real-life problem.

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Section: Conclusion and Further Areas Of Researchmentioning

confidence: 99%

Combining machine learning and optimization for the operational patient-bed assignment problem

Schäfer,

Walther,

Grimm

et al. 2023

Health Care Manag Sci

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show abstract

“…Often, researchers either do not disclose how they arrived at their specific weights (Ásgeirsson, 2014;Maenhout and Vanhoucke, 2010) or simply state that they consulted with health-care practitioners (Burke et al, 2008). In addition to nurse rostering, the weighted sum objective is commonly employed when generating rosters for other healthcare professions (Bard et al, 2014;Thielen, 2018) and for short-term rescheduling (Gross et al, 2018). Some researchers have applied alternative methodologies in an attempt to get around defining objective weights.…”

Section: Literature Reviewmentioning

confidence: 99%

Behind-the-Scenes Weight Tuning for applied nurse rostering

Böðvarsdóttir

Smet

Berghe

2020

Operations Research for Health Care

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“…NSP aims to establish a schedule for a number of nurses over a specified term (often two weeks or one month) while providing a certain level of healthcare and complying with hospital management guidelines [15]. More clearly, NSP means assigning a certain number of nurses to different work shifts with a set of hard and soft constraints, including the preferences of staff and the demands of hospitals, to balance the workload of nurses, increase hospital efficiency, reduce operating costs [11,[16][17][18][19], improve staff and patient safety and satisfaction to reduce the administrative workload of hospital administrators [11,[20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Decision Support System For Skill-Based Nurse Scheduling In An Intensive Care Unit

PARILDAR>

Akyürek²,

Akay

2024

Gazi University Journal of Science

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The main goal of health institutions is to provide the health services needed by society at the desired quality with the lowest possible cost. Considering the total number of employees in health institutions, nurse assignment and scheduling have an essential role in increasing efficiency and improving service quality due to the one-to-one interaction of nurses with patients. This study proposes a nurse scheduling model based on nurses’ skill levels incorporated into a decision support system. The skill level of nurses is assessed using Analytic Hierarchy Process and Technique for Order Preference by Similarity to Ideal Solution method based on eight criteria. The nurse scheduling problem is then modeled with 0-1 Goal Programming, considering the skill assessment as a constraint. The applicability of the proposed model is examined for the assignment and scheduling conditions of nurses at the 3rd level of surgical intensive care in a general hospital, and the valuable aspects of the proposed approach are discussed. When the proposed solution is compared with the current situation, it is realized that one nurse is saved without worsening the constraints.

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Duty rostering for physicians at a department of orthopedics and trauma surgery

Cited by 6 publications

References 13 publications

Combining machine learning and optimization for the operational patient-bed assignment problem

Combining machine learning and optimization for the operational patient-bed assignment problem

Behind-the-Scenes Weight Tuning for applied nurse rostering

A Decision Support System For Skill-Based Nurse Scheduling In An Intensive Care Unit

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