Location of Repository

Using genetic algorithms to optimise current and future health planning - the example of ambulance locations

By Satoshi Sasaki, Alexis J. Comber, Hiroshi Suzuki and Chris Brunsdon

Abstract

Background: Ambulance 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.\ud Results: Future 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.\ud Conclusions: The 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

Publisher: BioMed Central
Year: 2010
DOI identifier: 10.1186/1476-072X-9-4
OAI identifier: oai:lra.le.ac.uk:2381/8026
Journal:

Suggested articles

Preview

Citations

  1. (2008). A multiperiod set covering location model for a dynamic redeployment of ambulances. doi
  2. (1993). A review of covering problems in facility location. Location Science
  3. (2001). Adèr ME: Can paramedics using guidelines accurately triage patients?. Ann Emerg Med doi
  4. (2005). AG: Integration of genetic algorithms and GIS for optimal location search. doi
  5. (2003). Ambulance location and relocation models. doi
  6. (1982). Application of an expected covering model to emergency medical service system design. Decision Sciences doi
  7. (1999). Appropriateness of ambulance transportation to a suburban pediatric emergency department. Prehosp Emerg Care doi
  8. (1999). Carpenter TE: Spatial analytical methods and geographic information systems: use in health research and epidemiology. Epidemiol Rev doi
  9. (2006). Current practices in spatial analysis of cancer data: mapping health statistics to inform policymakers and the public.
  10. (2007). DB: Outcome prediction for guidance of initial resuscitation protocol: Shock first or CPR first. Resuscitation doi
  11. (2006). Defining rational hospital catchments for non-urban areas based on travel-time.
  12. (2007). Determining geographic areas and populations with timely access to cardiac catheterization facilities for acute myocardial infarction care in Alberta, Canada. doi
  13. (1995). Does out-of-hospital time affect trauma survival?. doi
  14. (1999). Driesbock KR: Regulation of ambulance response times in California. rehosp Emerg Care doi
  15. (2008). E: Using a GIS-based network analysis to determine urban greenspace accessibility for different ethnic and religious groups. Landsc Urban Plan doi
  16. (2002). Emergency ambulance dispatch: is there a case for triage?. doi
  17. (1999). Ferrall SJ: Inappropriate use of emergency medical services transport: comparison of provider and patient perspectives. Acad Emerg Med doi
  18. (2008). Fire Fighting Annual Report.
  19. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning doi
  20. (1997). Gesler WM: Normative Models and Healthcare Planning: Network-Based Simulations Within a Geographic Information System Environment. Health Services Research
  21. (2005). Immigration and Geographic Access to Prenatal Clinics in Brooklyn, NY: A Geographic Information Systems Analysis. Am J Public Health doi
  22. (2009). Impact of drainage networks as social infrastructure on cholera outbreaks in an inland urban city in Zambia. doi
  23. (2009). Internal Affairs and Communications Statistics Bureau: Population by Age.http://www.stat.go.jp/english/data/kokusei/2005/kihon1/ 00/02.htm,
  24. (1998). JL: Measuring access to primary medical care: some examples of the use of geographical information systems. Health and Place doi
  25. (2008). KG: Mobile phones, in combination with a computer locator system, improve the response times of emergency medical services in central London. Ann R Coll Surg Engl doi
  26. (2005). Markovchick VJ: Paramedic response time: does it affect patient survival?. Acad Emerg Med doi
  27. (2009). MG: A method for statistically comparing spatial distribution maps. doi
  28. (1995). Mittelmark MB: Emergency medical transport of the elderly: a population-based study.
  29. (1999). Modifiable factors associated with improved cardiac arrest survival in a multicenter BLS-D system: OPALS study phase I results. Ann Emerg Med doi
  30. (2004). NC: Field triage systems: methodologies from the literature. Prehosp Emerg Care
  31. (1999). Older people’s use of ambulance services: a population based analysis. doi
  32. (2004). Pliskin JS: A geographic information system simulation model of EMS: reducing ambulance response time. doi
  33. (2008). Population Projections for Japan: 2006-2055 Outline of Results, Methods, and Assumptions. The Japanese Journal of Population
  34. (2003). R: Is it possible to safely triage callers to EMS dispatch centers to alternative resources?. Prehosp Emerg Care
  35. (2002). Response time effectiveness: comparison of response time and survival in an urban emergency medical services system. Acad Emerg Med doi
  36. (1998). RW: The demand for prehospital emergency services in an aging society. Soc Sci Med doi
  37. (2008). Saydam C: A hypercube queueing model embedded into a genetic algorithm for ambulance deployment on highways. doi
  38. (2009). Saydam C: An optimization approach for ambulance location and the districting of the response segments on highways. doi
  39. (1984). Seeberg C: A practical methodology for ambulance location. Simulation doi
  40. (2008). Spatial analysis of risk factor of cholera outbreak for 2003-2004 in a peri-urban area of Lusaka, Zambia. Am J Trop Med Hyg
  41. (2006). Tanaka K: Emergency medical service systems in Japan: Past, present, and future. Resuscitation doi
  42. (2009). Using a GIS-based network analysis and optimisation routines to evaluate service provision: a case study of the UK Post Office. Applied Spatial Analysis and Policy doi
  43. (2009). Using hospitalization for ambulatory care sensitive conditions to measure access to primary health care: an application of spatial structural equation modeling. doi
  44. (2004). YS: GIS and genetic algorithms for HAZMAT route planning with security considerations. doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.