Emergency medical service response system performance in an urban South African setting: a computer simulation model

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dc.contributor.advisorWallis, Leeen_ZA
dc.contributor.advisorAdetunji, Olufemien_ZA
dc.contributor.authorStein, Christopher Owen Alexanderen_ZA
dc.date.accessioned2014-11-11T06:51:55Z
dc.date.available2014-11-11T06:51:55Z
dc.date.issued2014en_ZA
dc.descriptionIncludes bibliographical references.en_ZA
dc.description.abstractThis study investigated the effects of different response strategies, vehicle location strategies and vehicle numbers on response times in a simulated Emergency Medical Services system. The simulation was a computer model using discrete-event simulation and the model was based on Western Cape Emergency Medical Services operations in Cape Town. The study objectives were to (i) create the simulation model, (ii) determine the best-performing combination of explanatory factors and (iii) determine the effect of increasing vehicle numbers on response time performance. The simulation model took into account incident arrival rates, incident and hospital spatial distributions, vehicle numbers and dispatch practices in the modelled system. Verification and validation of the simulation model utilised a combination of quantitative and qualitative methods. The validated simulation model was changed in two ways: (i) the response strategy was changed to either single or two-tier (the response model factor) and (ii) the vehicle location strategy was changed to either dynamic or static (the vehicle location factor). This yielded four individual models each representing one combination of these factors. Each simulation model was run for a simulated period of seven days. Output data were analysed using multivariate analysis of variance in order to identify differences in response time between the factor combinations. A single-tier model using dynamic vehicle locations produced the best response performance. This model was run repeatedly, increasing vehicle numbers incrementally with each run to assess the effect of increased vehicle numbers on response time performance. A doubling of vehicle numbers resulted in an 14% increase in the number of responses meeting the national performance target for high acuity incidents, while a seven-fold increase in vehicle numbers increased this to 15%. No further performance increases were seen beyond this with increased vehicle numbers. A 2% performance increase for lower acuity incidents was seen with the same increase in vehicle numbers. In the system modelled, increasing vehicle numbers should not be expected to realise anything more than small improvements in response time performance, at a high operational cost. Fine-grained dynamic deployment of vehicles in anticipation of system demand appears to be a more important determinant of response performance than vehicle numbers alone.en_ZA
dc.identifier.apacitationStein, C. O. A. (2014). <i>Emergency medical service response system performance in an urban South African setting: a computer simulation model</i>. (Thesis). University of Cape Town ,Faculty of Health Sciences ,Division of Emergency Medicine. Retrieved from http://hdl.handle.net/11427/9523en_ZA
dc.identifier.chicagocitationStein, Christopher Owen Alexander. <i>"Emergency medical service response system performance in an urban South African setting: a computer simulation model."</i> Thesis., University of Cape Town ,Faculty of Health Sciences ,Division of Emergency Medicine, 2014. http://hdl.handle.net/11427/9523en_ZA
dc.identifier.citationStein, C. 2014. Emergency medical service response system performance in an urban South African setting: a computer simulation model. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Stein, Christopher Owen Alexander AB - This study investigated the effects of different response strategies, vehicle location strategies and vehicle numbers on response times in a simulated Emergency Medical Services system. The simulation was a computer model using discrete-event simulation and the model was based on Western Cape Emergency Medical Services operations in Cape Town. The study objectives were to (i) create the simulation model, (ii) determine the best-performing combination of explanatory factors and (iii) determine the effect of increasing vehicle numbers on response time performance. The simulation model took into account incident arrival rates, incident and hospital spatial distributions, vehicle numbers and dispatch practices in the modelled system. Verification and validation of the simulation model utilised a combination of quantitative and qualitative methods. The validated simulation model was changed in two ways: (i) the response strategy was changed to either single or two-tier (the response model factor) and (ii) the vehicle location strategy was changed to either dynamic or static (the vehicle location factor). This yielded four individual models each representing one combination of these factors. Each simulation model was run for a simulated period of seven days. Output data were analysed using multivariate analysis of variance in order to identify differences in response time between the factor combinations. A single-tier model using dynamic vehicle locations produced the best response performance. This model was run repeatedly, increasing vehicle numbers incrementally with each run to assess the effect of increased vehicle numbers on response time performance. A doubling of vehicle numbers resulted in an 14% increase in the number of responses meeting the national performance target for high acuity incidents, while a seven-fold increase in vehicle numbers increased this to 15%. No further performance increases were seen beyond this with increased vehicle numbers. A 2% performance increase for lower acuity incidents was seen with the same increase in vehicle numbers. In the system modelled, increasing vehicle numbers should not be expected to realise anything more than small improvements in response time performance, at a high operational cost. Fine-grained dynamic deployment of vehicles in anticipation of system demand appears to be a more important determinant of response performance than vehicle numbers alone. DA - 2014 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2014 T1 - Emergency medical service response system performance in an urban South African setting: a computer simulation model TI - Emergency medical service response system performance in an urban South African setting: a computer simulation model UR - http://hdl.handle.net/11427/9523 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/9523
dc.identifier.vancouvercitationStein COA. Emergency medical service response system performance in an urban South African setting: a computer simulation model. [Thesis]. University of Cape Town ,Faculty of Health Sciences ,Division of Emergency Medicine, 2014 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/9523en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDivision of Emergency Medicineen_ZA
dc.publisher.facultyFaculty of Health Sciencesen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.titleEmergency medical service response system performance in an urban South African setting: a computer simulation modelen_ZA
dc.typeDoctoral Thesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnamePhDen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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