Dynamic multi-appointment patient scheduling for radiation therapy

Sauré, Antoine; Patrick, Jonathan; Tyldesley, Scott; Puterman, Martin L.

doi:10.1016/j.ejor.2012.06.046

Cited by 155 publications

(131 citation statements)

References 33 publications

Supporting

Mentioning

129

Contrasting

Unclassified

Order By: Relevance

“…Could the overtime hours be improved? An interesting article by Sauré and colleagues 18 proposes a computer algorithm, developed in collaboration with our team, that integrates overtime costs into the calculation of optimal scheduling decisions in a radiation oncology department and takes into account necessary treatment delays. A simulation of that algorithm was used to generate treatment delays, which were then compared with the delays obtained with our post-transition approach.…”

Section: Discussionmentioning

confidence: 99%

“…A significant challenge is the unpredictable arrival of urgent cases that must be rapidly introduced into the treatment schedule-even though, if resources are to be maximally scheduled at all times, those resources should already be at full capacity. Complex data-processing algorithms have been proposed [17][18][19][20][21] and might eventually be integrated into applications to optimize scheduling. For the time being, the daily decision-making process is the responsibility of clerical staff, who have to weigh the potential of double bookings against the impact of wasting precious time slots.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Making Molehills out of a Mountain: Experience with a New Scheduling Strategy to Diminish Workload Variations in Response to Increased Treatment Demands

et al. 2016

View full text Add to dashboard Cite

Purpose A new scheduling strategy was implemented. Before implementation, treatments and planning computed tomography (ct) imaging were both scheduled at the same time. Maximal wait times for treatment are defined by the Quebec Ministry of Health's plan of action according to treatment aim and site. After implementation, patients requiring rapid treatment (priorities 0-3) continued to have their treatments scheduled at the same time as their planning ct; treatments for priority 4 (P4) patients were scheduled only after the treatment plan was approved. That approach aims to compensate for unexpected increases in planning workload by relocating less delay-sensitive cases to other time slots. We evaluated the impact on the patient experience, workload in various sectors, the care team's perception of care delivery, access to care, and the department's efficiency in terms of hours worked per treatment delivered.Methods Three periods were defined for analysis: the pre-transitional phase, for baseline evaluation; the transitional phase, during which there was an overlap in the way patients were being scheduled; and the post-transitional phase. Wait times were calculated from the date that patients were ready to treat to the date of their first treatment. Surveys were distributed to pre-and post-transitional phase patients. Care team members were asked to complete a survey evaluating their perception of how the change affected workload and patient care. Operational data were analyzed. ResultsWe observed a 24% increase in the number of treatments delivered in the post-transitional phase. Before implementation, priority 0-3 patients waited a mean of 7.9 days to begin treatments (n = 241); afterward, they waited 6.3 days (n = 340, p = 0.006). Before implementation, P4 patients waited a mean 15.1 days (n = 233); after implementation, they waited 16.1 days (n = 368, p = 0.22). Surveys showed that patients felt that the time it took to inform them of treatment appointments was acceptable in both phases. No significant change in overtime hours occurred in dosimetry (p = 0.7476) or globally (p = 0.4285) despite the increased number of treatments. However, departmental efficiency improved by 16% (p = 0.0001). ConclusionsThis new scheduling strategy for P4 cases resulted in improved access to care for priority 0-3 patients.Departmental efficiency was improved, and overtime hours did not increase. Patient satisfaction remained high.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Making Molehills out of a Mountain: Experience with a New Scheduling Strategy to Diminish Workload Variations in Response to Increased Treatment Demands

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Schütz and Kolisch (2012) modelled the problem of determining whether or not to accept requests for the service company as a continuous-time MDP and solved using simulation-based approximate dynamic programming combined with a discrete event simulation of the service period. Sauré et al (2012) formulated a discounted infinite-horizon MDP for scheduling cancer treatments in radiation therapy units. Li et al (2010) propose a MDP model for solving the problem of dynamic emergency task allocation.…”

Section: Mdp Applied In Medical Resourcesmentioning

confidence: 99%

A Markov decision process model of allocating emergency medical resource among multi-priority injuries

Wang

Zhao

2017

IJMOR

View full text Add to dashboard Cite

Abstract:This article studies how to allocate a scare emergency medical resource at the beginning of large-scale disaster. One salient problem in emergency rescue is how to coordinate the resource among injuries of different degrees in different periods. The injuries are classified into three levels and given different priorities. Waiting cost with resources shortage, deteriorated cost with delay and transferring cost to other hospitals are introduced into the decision-making process. Basing on incorporating the costs into revenue function, a Markov decision process (MDP) model is proposed to establish the optimal action policies in different periods. A dynamic algorithm is proposed to derive the optimal solution. With a numerical experiment, the improvement of MDP is displayed and some managerial suggestions are also provided.Keywords: emergency management; medical resource allocation; Markov decision process; multi-priority injuries; operational research.

show abstract

“…An elegant way to find the optimal control actions for each state is provided by the classical value or policy iteration algorithms [1][2][3][4][5][6][7][8][9][10][11]. The value iteration (VI) algorithm is arguably the most popular algorithm, in part because of its simplicity and ease of implementation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Strategies for Accelerating the Convergence of Average Cost Markov Decision Processes Using a Moving Average Digital Filter

Arruda¹,

Ourique²

2013

AJOR

View full text Add to dashboard Cite

This paper proposes a technique to accelerate the convergence of the value iteration algorithm applied to discrete average cost Markov decision processes. An adaptive partial information value iteration algorithm is proposed that updates an increasingly accurate approximate version of the original problem with a view to saving computations at the early iterations, when one is typically far from the optimal solution. The proposed algorithm is compared to classical value iteration for a broad set of adaptive parameters and the results suggest that significant computational savings can be obtained, while also ensuring a robust performance with respect to the parameters.

show abstract

Dynamic multi-appointment patient scheduling for radiation therapy

Cited by 155 publications

References 33 publications

Making Molehills out of a Mountain: Experience with a New Scheduling Strategy to Diminish Workload Variations in Response to Increased Treatment Demands

Making Molehills out of a Mountain: Experience with a New Scheduling Strategy to Diminish Workload Variations in Response to Increased Treatment Demands

A Markov decision process model of allocating emergency medical resource among multi-priority injuries

Adaptive Strategies for Accelerating the Convergence of Average Cost Markov Decision Processes Using a Moving Average Digital Filter

Contact Info

Product

Resources

About