Dynamic ambulance reallocation for the reduction of ambulance response times using system status management.

OBJECTIVES Dynamically reassigning ambulance deployment locations throughout a day to balance ambulance availability and demands can be effective in reducing response times. The objectives of this study were to model dynamic ambulance allocation plans in Singapore based on the system status management (SSM) strategy and to evaluate the dynamic deployment plans using a discrete event simulation (DES) model. METHODS The geographical information system-based analysis and mathematical programming were used to develop the dynamic ambulance deployment plans for SSM based on ambulance calls data from January 1, 2011, to June 30, 2011. A DES model that incorporated these plans was used to compare the performance of the dynamic SSM strategy against static reallocation policies under various demands and travel time uncertainties. RESULTS When the deployment plans based on the SSM strategy were followed strictly, the DES model showed that the geographical information system-based plans resulted in approximately 13-second reduction in the median response times compared to the static reallocation policy, whereas the mathematical programming-based plans resulted in approximately a 44-second reduction. The response times and coverage performances were still better than the static policy when reallocations happened for only 60% of all the recommended moves. CONCLUSIONS Dynamically reassigning ambulance deployment locations based on the SSM strategy can result in superior response times and coverage performance compared to static reallocation policies even when the dynamic plans were not followed strictly.

[1]  A. Getis,et al.  Comparative Spatial Filtering in Regression Analysis , 2002 .

[2]  J. Estes,et al.  Geographic Information Systems: An Introduction , 1990 .

[3]  J. Pell,et al.  Effect of reducing ambulance response times on deaths from out of hospital cardiac arrest: cohort study , 2001, BMJ : British Medical Journal.

[4]  H. Morita,et al.  An analysis of time factors in out-of-hospital cardiac arrest in Osaka Prefecture. , 2002, Resuscitation.

[5]  D J Roe,et al.  Estimating effectiveness of cardiac arrest interventions: a logistic regression survival model. , 1997, Circulation.

[6]  Z. Vekerdy.  Spatial analysis: modelling in a GIS environment , 1998 .

[7]  J. Hedges,et al.  Does out-of-hospital EMS time affect trauma survival? , 1995, The American journal of emergency medicine.

[8]  A. Mitchell.  The ESRI guide to GIS analysis , 1999 .

[9]  Lark,et al.  OUTCOMES OF RAPID DEFIBRILLATION BY SECURITY OFFICERS AFTER CARDIAC ARREST IN CASINOS , 2000 .

[10]  Cummins Ro,et al.  The "chain of survival" concept: how it can save lives. , 1992 .

[11]  J. Sampalis,et al.  Direct transport to tertiary trauma centers versus transfer from lower level facilities: impact on mortality and morbidity among patients with major trauma. , 1997, The Journal of trauma.

[12]  Gilbert Laporte,et al.  Application of the Double Standard Model for Ambulance Location , 2009 .

[13]  A. Getis,et al.  Constructing the Spatial Weights Matrix Using a Local Statistic , 2004 .

[14]  J. Harvey,et al.  The Emergency Medical Service Systems Act of 1973. , 1974, JAMA.

[15]  E. Lerner,et al.  Epidemiology and outcomes of out-of-hospital cardiac arrest in Rochester, New York. , 2007, Resuscitation.

[16]  Karl F. Doerner,et al.  Ambulance location and relocation problems with time-dependent travel times , 2010, Eur. J. Oper. Res..

[17]  J. Goldberg.  Operations Research Models for the Deployment of Emergency Services Vehicles , 2004 .

[18]  Armann Ingolfsson,et al.  A Markov Chain Model for an EMS System with Repositioning , 2013 .

[19]  S. Al-Shaqsi.  Response time as a sole performance indicator in EMS: Pitfalls and solutions , 2010, Open access emergency medicine : OAEM.

[20]  Syi Su,et al.  Resource reallocation in an emergency medical service system using computer simulation. , 2002, The American journal of emergency medicine.

[21]  R. Cummins.  The "chain of survival" concept: how it can save lives. , 1992, Heart disease and stroke : a journal for primary care physicians.

[22]  Erhan Erkut,et al.  Computational Comparison of Five Maximal Covering Models for Locating Ambulances , 2009 .

[23]  Tor Bernhardsen,et al.  Geographic Information Systems: An Introduction , 1999 .

[24]  Melvyn Sim,et al.  Satisficing Measures for Analysis of Risky Positions , 2009, Manag. Sci..

[25]  Kevin P. Hwang,et al.  Using a discrete-event simulation to balance ambulance availability and demand in static deployment systems. , 2009, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[26]  J. Ornato,et al.  Improving survival from sudden cardiac arrest , 1991 .

[27]  Marcus Eng Hock Ong,et al.  Reducing ambulance response times using geospatial-time analysis of ambulance deployment. , 2010, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[28]  B. King.  Chain of Survival , 2009 .

[29]  A. Kuehl.  Prehospital systems and medical oversight : National Association of EMS Physicians , 1994 .

[30]  Joseph S. Pliskin,et al.  A geographic information system simulation model of EMS: reducing ambulance response time. , 2004, The American journal of emergency medicine.

[31]  G. Lombardi,et al.  Outcome of Out-of-Hospital Cardiac Arrest in New York City: The Pre-Hospital Arrest Survival Evaluation (PHASE) Study , 1994 .

[32]  I. Stiell,et al.  Improved Out-of-Hospital Cardiac Arrest Survival Through the Inexpensive Optimization of an Existing Defibrillation Program: OPALS Study Phase II , 1999 .

[33]  A. EsriMitchel.  The ESRI Guide to GIS analysis, Volume 2: Spartial measurements and statistics , 2005 .

[34]  Lynn W. Zimmerman,et al.  The Efficacy of Advanced Life Support: A Review of the Literature , 1998, Prehospital and Disaster Medicine.

[35]  Laura A McLay,et al.  Evaluating emergency medical service performance measures , 2010, Health care management science.

[36]  Jon Nicholl,et al.  Role of ambulance response times in the survival of patients with out-of-hospital cardiac arrest , 2010, Emergency Medicine Journal.

[37]  M. Ong,et al.  Geographic-time distribution of ambulance calls in Singapore: utility of geographic information system in ambulance deployment (CARE 3). , 2009, Annals of the Academy of Medicine, Singapore.

[38]  Gilbert Laporte,et al.  Ambulance location and relocation models , 2000, Eur. J. Oper. Res..

[39]  Melvyn Sim,et al.  Multiple Objectives Satisficing Under Uncertainty , 2013, Oper. Res..

[40]  Michel Gendreau,et al.  A dynamic model and parallel tabu search heuristic for real-time ambulance relocation , 2001, Parallel Comput..

[41]  J. Ornato,et al.  Improving survival from sudden cardiac arrest: the "chain of survival" concept. A statement for health professionals from the Advanced Cardiac Life Support Subcommittee and the Emergency Cardiac Care Committee, American Heart Association. , 1991, Circulation.

[42]  Gilbert Laporte,et al.  Solving an ambulance location model by tabu search , 1997 .

[43]  R. Tilgreen,et al.  One-year survival after out-of-hospital cardiac arrest in Copenhagen according to the 'Utstein style'. , 2000, Resuscitation.

[44]  D. I. Heywood,et al.  An Introduction to Geographical Information Systems , 2002 .

[45]  H. Oh,et al.  Reducing Ambulance Response Times Using Discrete Event Simulation , 2014, Prehospital emergency care : official journal of the National Association of EMS Physicians and the National Association of State EMS Directors.