The Application of Information Systems to Improve Ambulance Response Times in the UK
Source: By:Author(s)
DOI: https://doi.org/10.30564/jeis.v5i2.5881
Abstract:Emergency ambulance services in the UK are tasked with providing pre-hospital patient care and clinical services with a target response time between call connect to on-scene attendance. In 2017, NHS England introduced four new response time categories based on patient needs. The most challenging is to be on-scene for a life-threatening situation within seven minutes of the call being connected when such calls are random in terms of time and place throughout a large territory. Recent evidence indicates emergency ambulance services regularly fall short of achieving the target ambulance response times set by the National Health Service (NHS). To achieve these targets, they need to undertake transformational change and apply statistical, operations research and artificial intelligence techniques in the form of five separate modules covering demand forecasting, plus locate, allocate, dispatch, monitoring and re-deployment of resources. These modules should be linked in real-time employing a data warehouse to minimise computational data and generate accurate, meaningful and timely decisions ensuring patients receive an appropriate and timely response. A simulation covering a limited geographical area, time and operational data concluded that this form of integration of the five modules provides accurate and timely data upon which to make decisions that effectively improve ambulance response times.
References:[1] Cook, M., 2011. An introduction to the new ambulance clinical quality indicators. Ambulance Today. 7(5), 35-36. [2] Heath, G., Radcliffe, J., 2007. Performance measurement and the English ambulance service. Public Money and Management. 27(3), 223-228. [3] Heath, G., Radcliffe, J., 2010. Exploring the utility of current performance measures for changing roles and practices of ambulance paramedics. Public Money & Management. 30(3), 151-158. [4] Gething,V (2015) .Written Statement – Clinical Review of Ambulance Response Time Targets' Cabinet Statement, Welsh Government, Health Board. https://www.gov.wales/written-statement-clinical-review-ambulance-response-time-targets [5] Price, L., 2006. Treating the clock and not the patient: Ambulance response times and risk. BMJ Quality & Safety. 15(2), 127-130. [6] Wankhade, P., 2011. Performance measurement and the UK emergency ambulance service: Unintended consequences of the ambulance response time targets. International Journal of Public Sector Management. 24(5), 384-402. [7] Turner J (2017) 'How we remodelled Ambulance Services in England' Research Features, University of Sheffield, England. https://www.sheffield.ac.uk/research/features/remodelled-ambulance-service [8] On the Day Briefing: Ambulance Response Time Programme [Internet]. NHS Providers. Available from: https://nhsproviders.org/resources/briefings/on-the-day-briefing-ambulance-response-programme# [9] Toregas, C., Swain, R., ReVelle, C., et al., 1971. The location of emergency service facilities. Operations Research. 19(6), 1363-1373. [10] Church, R., ReVelle, C., 1974. The maximal covering location problem. Regional Science. 32(1), 101-118. [11] Repede, J.F., Bernardo, J.J., 1994. Developing and validating a decision support system for locating emergency medical vehicles in Louisville, Kentucky. European Journal of Operational Research. 75(3), 567-581. [12] Gendreau, M., Laporte, G., Semet, F., 1997. Solving an ambulance location model by tabu search. Location Science. 5(2), 75-88. [13] Doerner, K.F., Gutjahr, W.J., Hartl, R.F., et al., 2005. Heuristic solution of an extended double-coverage ambulance location problem for Austria. Central European Journal of Operations Research. 13(4), 325-340. [14] Daskin, M.S., 1983. A maximum expected covering location model: Formulation, properties and heuristic solution. Transportation Science. 17(1), 48-70. [15] Daskin, M., 1987. Location, Dispatching and Routing Model for Emergency Services with Stochastic Travel Times [Internet]. Available from: https://www.semanticscholar.org/paper/LOCATION%2C-DISPATCHING-AND-ROUTING-MODELS-FOR-WITH-Daskin/5bc91cb1642f023fc1958eaf31c228b291f4cde4 [16] Carter, G.M., Chaiken, J.M., Ignall, E., 1972. Response areas for two emergency units. Operations Research. 20(3), 571-594. [17] Larson, R.C., 1974. A hypercube queuing model for facility location and redistricting in urban emergency services. Computers & Operations Research. 1(1), 67-95. [18] Lubicz, M., Mielczarek, B., 1987. Simulation modelling of emergency medical services. European Journal of Operational Research. 29(2), 178-185. [19] Savas, E.S., 1969. Simulation and cost-effectiveness analysis of New York’s emergency ambulance service. Management Science. 15(12), B-608. [20] Fitzsimmons, J.A., 1973. A methodology for emergency ambulance deployment. Management Science. 19(6), 627-636. [21] Swoveland, C., Uyeno, D., Vertinsky, I., et al., 1973. Ambulance location: A probabilistic enumeration approach. Management Science. 20, 686-698. [22] Erkut, E., Ingolfsson, A., Budge, S., 2007. Maximum Availability Models for Selecting Ambulance Station and Vehicle Locations: A Critique [Internet]. Available from: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=965f00e8ec5caa3d08ab916b6c71528f5908a311 [23] Inakawa, K., Furuta, T., Suzuki, A. (editors), 2010. Effect of ambulance station locations and number of ambulances to the quality of the emergency service. The Ninth International Symposium on Operations Research and Its Applications (ISORA' 10); 2010 Aug 19-23; Chengdu, China. p. 340-347. [24] Brotcorne, L., Laporte, G., Semet, F., 2003. Ambulance location and relocation models. European Journal of Operational Research. 147(3), 451-463. [25] Fitch, J., 2005. Response times: Myths, measurement and management. Journal of Emergency Medical Services. 30, 46-56. [26] Blackwell, T.H., Kaufman, J.S., 2002. Response time effectiveness: Comparison of response time and survival in an urban emergency medical services system. Academic Emergency Medicine. 9(4), 288-295. [27] Henderson, S.G., Mason, A.J., 2004. Ambulance service planning: Simulation and data visualisation. Operations research and health care: A handbook of methods and applications. Springer: Berlin. pp. 77-102. [28] Carson, Y.M., Batta, R., 1990. Locating an ambulance on the Amherst campus of the State University of New York at Buffalo. Interfaces. 20(5), 43-49. [29] Kolesar, P., 1975. A model for predicting average fire engine travel times. Operations Research. 23(4), 603-613. [30] Naoum-Sawaya, J., Elhedhli, S., 2013. A stochastic optimization model for real-time ambulance redeployment. Computers & Operations Research. 40(8), 1972-1978. [31] Carter, L., 2018. Operational Productivity and Performance in English Ambulance Trusts: Unwanted Variations [Internet]. Available from: https://www.england.nhs.uk/wp-content/uploads/2019/09/Operational_productivity_and_performance_NHS_Ambulance_Trusts_final.pdf [32] Royal Mail (2015) ‘History of UK Postcodes’ Royal Mail Group, London. https://www.poweredbypaf.com/the-history-of-uk-postcodes/ [33] Pidd, M., De Silva, F.N., Eglese, R.W., 1996. A simulation model for emergency evacuation. European Journal of Operational Research. 90(3), 413-419. [34] Eglese, R., Maden, W., Slater, A., 2006. A road timetableTM to aid vehicle routing and scheduling. Computers & Operations Research. 33(12), 3508-3519. [35] Hamet, P., Tremblay, J., 2017. Artificial intelligence in medicine. Metabolism. 69, S36-S40.