ArticleInternational journal of health geographics2010
Using genetic algorithms to optimise current and future health planning--the example of ambulance locations.
Article in International journal of health geographics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- A Long-Term Projection for Emergency Ambulance Services Demand in Taipei and the Related Effects by Temperature.Emergency medicine international · 2026Article
- Delivery of public health interventions by the ambulance sector: a scoping review.BMC public health · 2023Article
- Reorganization of an Emergency Medical System in a Mixed Urban-Rural Area.International journal of environmental research and public health · 2022Article
- Coverage versus response time objectives in ambulance location.International journal of health geographics · 2021Article
- Workplace Employees' Annual Physical Checkup and During Hire on the Job to Increase Health-care Awareness Perception to Prevent Disease Risk: A Work for Policy-Implementable Option Globally.Safety and health at work · 2019Review
- A two-stage spatial allocation model for elderly healthcare facilities in large-scale affordable housing communities: a case study in Nanjing City.International journal for equity in health · 2018Article
- Does the edge effect impact on the measure of spatial accessibility to healthcare providers?International journal of health geographics · 2017Article
- Impact of geographic accessibility on utilization of the annual health check-ups by income level in Japan: A multilevel analysis.PloS one · 2017Article
- A Review and Framework for Categorizing Current Research and Development in Health Related Geographical Information Systems (GIS) Studies.Yearbook of medical informatics · 2014Review
- Optimal installation locations for automated external defibrillators in Taipei 7-Eleven stores: using GIS and a genetic algorithm with a new stirring operator.Computational and mathematical methods in medicine · 2014Article
- The aging population and future demand for emergency ambulances in Japan.Internal and emergency medicine · 2013Article
- A spatial analysis of variations in health access: linking geography, socio-economic status and access perceptions.International journal of health geographics · 2011Article
- Optimal Planning of Health Services through Genetic Algorithm and Discrete Event Simulation: A Proposed Model and Its Application to Stroke Rehabilitation Care.MDM policy & practiceArticle
- The demographic impact on the demand for emergency medical services in the urban and rural regions of Bavaria, 2012-2032.Zeitschrift fur Gesundheitswissenschaften = Journal of public healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAmbulance response time is a crucial factor in patient survival. The number of emergency cases (EMS cases) requiring an ambulance is increasing due to changes in population demographics. This is decreasing ambulance response times to the emergency scene. This paper predicts EMS cases for 5-year intervals from 2020, to 2050 by correlating current EMS cases with demographic factors at the level of the census area and predicted population changes. It then applies a modified grouping genetic algorithm to compare current and future optimal locations and numbers of ambulances. Sets of potential locations were evaluated in terms of the (current and predicted) EMS case distances to those locations.
resultsFuture EMS demands were predicted to increase by 2030 using the model (R2 = 0.71). The optimal locations of ambulances based on future EMS cases were compared with current locations and with optimal locations modelled on current EMS case data. Optimising the location of ambulance stations locations reduced the average response times by 57 seconds. Current and predicted future EMS demand at modelled locations were calculated and compared.
conclusionsThe reallocation of ambulances to optimal locations improved response times and could contribute to higher survival rates from life-threatening medical events. Modelling EMS case 'demand' over census areas allows the data to be correlated to population characteristics and optimal 'supply' locations to be identified. Comparing current and future optimal scenarios allows more nuanced planning decisions to be made. This is a generic methodology that could be used to provide evidence in support of public health planning and decision making.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.