Using a genetic algorithm to solve a non-linear location allocation problem for specialised children’s ambulances in England and Wales

Kung, Enoch; Seaton, Sarah E; Ramnarayan, Padmanabhan; Pagel, Christina

doi:10.1080/20476965.2021.1908176

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…The results indicated that adding a floating station could significantly improve the performance of existing EMS systems. Kung et al 24 developed an optimization framework to solve the location-allocation problem of pediatric EMS stations in the U.K. The model aimed to locate EMS stations, determine the number of allocated teams to each station, and identify the hospitals each station serves.…”

Section: Literature Reviewmentioning

confidence: 99%

A Multi-objective Mathematical Programing Model for the Problem of P-envy Emergency Medical Service Location

Khalilzadeh,

Bahari

2023

Health�Serv�Insights

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To prevent the great dangers caused by emergency situations, providing rapid and high-quality emergency aid highly depends on the location of emergency medical centers. The purpose of this research is to present a multi-objective mathematical programing model based on the minimum P-envy algorithm to locate and construct emergency medical services (EMS). Maximizing the coverage in order to increase the probability of survival of different categories of patients, minimizing the costs of constructing EMS and optimizing the ratio of regions having the right to emergency medical services is one of the fundamental challenges in the health care system of countries. In this paper, a model for maximum utilization of EMS considering budget limitations is presented. In this study, since the problem is NP-Hard, the Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm were used to solve this problem. The parameters of the metaheuristic algorithms were tuned using the Taguchi method. Several instance problems were solved to compare the performance of 2 algorithms. The results demonstrate that the validity of the proposed model. Also, the mean of the solutions obtained by GA for small, medium, and large-size problems are better than the SA algorithm. Also, the GA algorithm obtained more efficient solutions than the SA algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

A Multi-objective Mathematical Programing Model for the Problem of P-envy Emergency Medical Service Location

Khalilzadeh,

Bahari

2023

Health�Serv�Insights

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“…We then worked with clinical partners to define scenarios of interest to explore with more sophisticated modelling, relaxing key assumptions about team availability, travel times and demand. Note that the methods described in this chapter have been published in peer-reviewed journals by King et al 68 and Kung et al 69…”

Section: Overview Of Approachmentioning

confidence: 99%

“…winter vs. non-winter). Note that the methods described in the rest of this section have been published in Kung et al 69…”

Section: Extending the Model By Allowing Stochastic Journey Times And...mentioning

confidence: 99%

Evaluation of timeliness and models of transporting critically ill children for intensive care: the DEPICT mixed-methods study

Ramnarayan

Seaton

Evans

et al. 2022

Health Soc Care Deliv Res

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Background Centralisation of paediatric intensive care has increased the need for specialist critical care transport teams to transfer sick children from general hospitals to tertiary centres. National audit data show variation in how quickly transport teams reach the patient’s bedside and in the models of care provided during transport; however, the impact of this variation on clinical outcomes and the experience of patients, families and clinicians is unknown. Objectives We aimed to understand if and how clinical outcomes and experience of children transported for intensive care are affected by timeliness of access to a transport team and different models of transport care. Methods We used a mixed-methods approach with a convergent triangulation study design. There were four study workstreams: a retrospective analysis of linked national clinical audit data (2014–16) (workstream A), a prospective questionnaire study to collect experience data from parents of transported children and qualitative analysis of interviews with patients, families and clinicians (workstream B), health economic evaluation of paediatric transport services (workstream C) and mathematical modelling evaluating the potential impact of alternative service configurations (workstream D). Results Transport data from over 9000 children were analysed in workstream A. Transport teams reached the patient bedside within 3 hours of accepting the referral in > 85% of transports, and there was no apparent association between time to bedside and 30-day mortality. Similarly, the grade of the transport team leader or stabilisation approach did not appear to affect mortality. Patient-related critical incidents were associated with higher mortality (adjusted odds ratio 3.07, 95% confidence interval 1.48 to 6.35). In workstream B, 2133 parents completed experience questionnaires pertaining to 2084 unique transports of 1998 children. Interviews were conducted with 30 parents and 48 staff. Regardless of the actual time to bedside, parent satisfaction was higher when parents were kept informed about the team’s arrival time and when their expectation matched the actual arrival time. Satisfaction was lower when parents were unsure who the team leader was or when they were not told who the team leader was. Staff confidence, rather than seniority, and the choice for parents to travel with their child in the ambulance were identified as key factors associated with a positive experience. The health economic evaluation found that team composition was variable between transport teams, but not significantly associated with cost and outcome measures. Modelling showed marginal benefit in changing current transport team locations, some benefit in reallocating existing teams and suggested where additional transport teams could be allocated in winter to cope with the expected surge in demand. Limitations Our analysis plans were limited by the impact of the pandemic. Unmeasured confounding may have affected workstream A findings. Conclusions There is no evidence that reducing the current 3-hour time-to-bedside target for transport teams will improve patient outcomes, although timeliness is an important consideration for parents and staff. Improving communication during transport and providing parents the choice to travel in the ambulance with their child are two key service changes to enhance patient/family experience. Future work More research is needed to develop suitable risk-adjustment tools for paediatric transport and to validate the short patient-related experience measure developed in this study. Trial registration This trial is registered as ClinicalTrials.gov NCT03520192. Funding This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme and will be published in full in Health and Social Care Delivery Research; Vol. 10, No. 34. See the NIHR Journals Library website for further project information.

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“…The association found in this work has the potential for improving allocation of additional resource across the country. As part of the DEPICT study on paediatric critical care retrieval [41], an optimisation framework has been developed that can be used to allocate different numbers of teams to different retrieval locations depending on season and time of day to maximise the availability of teams and minimise time from child referral to the team arriving at the bedside [42]. In that work it was shown that the addition of even one or two teams in key locations could significantly reduce times to bedside.…”

Section: Resource Planningmentioning

confidence: 99%

Modelling the association between weather and short-term demand for children’s intensive care transport services during winter in the South East of England

Livingstone¹,

Pagel²,

Shao³

et al. 2021

Operations Research for Health Care

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Using a genetic algorithm to solve a non-linear location allocation problem for specialised children’s ambulances in England and Wales

Cited by 8 publications

References 21 publications

A Multi-objective Mathematical Programing Model for the Problem of P-envy Emergency Medical Service Location

A Multi-objective Mathematical Programing Model for the Problem of P-envy Emergency Medical Service Location

Evaluation of timeliness and models of transporting critically ill children for intensive care: the DEPICT mixed-methods study

Modelling the association between weather and short-term demand for children’s intensive care transport services during winter in the South East of England

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