Staff optimization for time-dependent acute patient flow

Andersen, Anders Reenberg; Nielsen, Bo Friis; Reinhardt, Line Blander; Stidsen, Thomas Jacob Riis

doi:10.1016/j.ejor.2018.06.015

Cited by 19 publications

(9 citation statements)

References 32 publications

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“…It can be concluded that most studies are related to the scheduling and routing of the caregivers, and that there is no study considering the districting, staff dimensioning, scheduling, and routing decisions simultaneously. Driver Scheduling Problem (Perumal et al, 2018), staff dimensioning in an emergency department (Andersen et al, 2019), pricing problem (Oliveira et al, 2019), a patient to bed assignment problem (Guido et al, 2018), and a vehicle reservation assignment (Oliveira et al, 2014).…”

Section: Scheduling and Routing Of Caregiversmentioning

confidence: 99%

A matheuristic algorithm for stochastic home health care planning

Nikzad

Bashiri

Abbasi

2021

European Journal of Operational Research

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Section: Scheduling and Routing Of Caregiversmentioning

confidence: 99%

A matheuristic algorithm for stochastic home health care planning

Nikzad

Bashiri

Abbasi

2021

European Journal of Operational Research

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“…Simulation modeling in the healthcare context covers ED healthcare management. Although ED simulation models have been extensively developed for various contexts, only a few studies have concentrated on operational patient flow with respect to patient throughput time in ED [35][36][37]. Abo-Hamad and Arisha [3] proposed a DES model to improve patient flow in ED [9,18].…”

Section: Related Workmentioning

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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“…These waiting times of patients are spread over different phases of treatment such as diagnostic tests, physicians' treatments, appointments with a specialist and surgeries (Kruger et al, 2020). An integrated monitoring system of all the phases of treatment along with organized patient flow management in each phase will reduce the patients' waiting time at each of these phases (Andersen et al, 2019). For efficient patient flow management, identifying the number of incoming patients is crucial.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Healthcare Resource Management Planning Using Advanced Machine Learning Methods

Mizan

2024

Preprint

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The purpose of this study is to develop predictive and optimization models to reduce patients' waiting time for diagnostic tests and medical treatments. Long wait times for receiving medical care is a pressing issue in the Canadian healthcare system. These wait times are spread over different phases of treatment, such as diagnostic tests, physicians' treatments, and appointments with specialists and surgeons. An integrated system to monitor all the phases of treatment along with efficient workload distribution in each phase can reduce patients' waiting time at each of these phases. This study investigates the impact of efficient resource allocation and workload distribution in the medical system. This research also explores the effect of optimized resource allocation on the patients' waiting time in Medicare settings. Resource allocation planning is directly related to the number of patient-arrival, and it is hard to predict such uncertain parameters in the future time frame. The number of patient-arrival also varies across different medical departments and different timeframes which makes the patient-arrival prediction challenging. The goal of this study is to investigate the forecasting effect on patients' waiting time and physicians' workload. To achieve this goal, advanced machine learning technique is integrated with the optimization model. The machine learning technique is used to predict the uncertain parameters of the optimization model for a shorter time span. To predict time-dependent uncertain parameters, such as patient arrival is a major issue, as the prediction may suffer from the concept drift problem. Besides, real-time data are commonly prone to errors due to irregular fluctuations, seasonal biases, and missing values in the data. On the other hand, predicting for shorter intervals requires lower execution time combined with higher accuracy. The developed predictive ensemble model in this research has addressed these issues legitimately with four research contributions. In the first contribution (Chapter 2), we have investigated methodologies for predicting Radiologists’ workload in a short time interval by adopting a machine learning technique. An ensemble model is proposed with the fixed batch training method in this part. To excel in the execution time, a fixed batch training method is used. Secondly, in Chapter 3, an Adaptive Batched-Ranked Ensemble (ABRE) model that reduces the effect of fluctuation using the time-variant windowing technique. Besides, a data aggregation technique is developed and integrated with the offline training phase of the proposed model to tackle the concept drift problem. In the third contribution (Chapter 4), a novel Ensemble of Pruned Regressor Chain (EPRC) method is developed and trained offline to predict uncertain parameters, such as patients’ arrival. Finally, the fourth contribution in Chapter 4, the EPRC method is integrated with a novel multi-objective optimization model to reduce patients’ waiting time, and to determine workload allocation for future timespan. This research enables enhanced decision-making with effective resource allocation and workload scheduling, as well as assists in reducing healthcare expenditure.

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Staff optimization for time-dependent acute patient flow

Cited by 19 publications

References 32 publications

A matheuristic algorithm for stochastic home health care planning

A matheuristic algorithm for stochastic home health care planning

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

Real-Time Healthcare Resource Management Planning Using Advanced Machine Learning Methods

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