Optimizing Emergency Medical Assistance Coordination in After-Hours Urgent Surgery Patients

This paper treats the coordination of Emergency Medical Assistance (EMA) and hospitals for after-hours surgeries of urgent patients arriving by ambulance. A standard hospital approach during night-shifts is to have standby surgery teams come to hospital after alert to cover urgent cases that cannot be covered by the in-house surgery teams. This approach results in a considerable decrease in staffing costs in respect to having sufficient permanent in-house staff. Therefore, coordinating EMA and the hospitals in a region with their outhouse staff with the objective to have as fast urgent surgery treatments as possible with minimized cost is a crucial parameter of the medical system efficiency and as such deserves a thorough investigation. In practice, the process is manual and the process management is case-specific, with great load on human phone communication. In this paper, we propose a decision support system for the automated coordination of hospitals, surgery teams on standby from home, and ambulances to decrease the time to surgery of urgent patients. The efficiency of the proposed model is proven over simulation experiments.

[1]  Abdur Rais,et al.  Operations Research in Healthcare: a survey , 2011, Int. Trans. Oper. Res..

[2]  Bozena Mielczarek,et al.  Application of computer simulation modeling in the health care sector: a survey , 2012, Simul..

[3]  Maurice Queyranne,et al.  Dynamic Multipriority Patient Scheduling for a Diagnostic Resource , 2008, Oper. Res..

[4]  Erik Demeulemeester,et al.  Operating room planning and scheduling: A literature review , 2010, Eur. J. Oper. Res..

[5]  D. Hu,et al.  Patients with acute myocardial infarction using ambulance or private transport to reach definitive care: which mode is quicker? , 2010, Internal medicine journal.

[6]  Franklin Dexter,et al.  Operating Room Leadership and Management: Influence of staffing and scheduling on operating room productivity , 2012 .

[7]  Dídac Busquets,et al.  A Multiagent System for Coordinating Ambulances for Emergency Medical Services , 2008, IEEE Intelligent Systems.

[8]  J. Ottervanger,et al.  Prehospital triage in the ambulance reduces infarct size and improves clinical outcome. , 2011, American heart journal.

[9]  Marin Lujak,et al.  Coordinating Emergency Medical Assistance , 2013 .

[10]  Matthew S. Maxwell,et al.  Approximate Dynamic Programming for Ambulance Redeployment , 2010, INFORMS J. Comput..

[11]  Ewout W Steyerberg,et al.  A Simulation Model for Determining the Optimal Size of Emergency Teams on Call in the Operating Room at Night , 2008, Anesthesia and analgesia.

[12]  F. Guerriero,et al.  Operational research in the management of the operating theatre: a survey , 2011, Health care management science.

[13]  Maria E. Mayorga,et al.  Optimal dispatching strategies for emergency vehicles to increase patient survivability , 2012 .

[14]  Huijun Hu,et al.  An Optimization Model for Real-Time Emergency Vehicle Dispatching and Routing , 2003 .

[15]  Armann Ingolfsson,et al.  EMS Planning and Management , 2013 .

[16]  Shane G. Henderson,et al.  Ambulance Service Planning: Simulation and Data Visualisation , 2005 .

[17]  S. Henderson.  Operations Research Tools for Addressing Current Challenges in Emergency Medical Services , 2011 .

[18]  D. Bertsekas.  The auction algorithm: A distributed relaxation method for the assignment problem , 1988 .

[19]  Letizia Leonardi,et al.  Coordination Issues in an Agent-Based Approach for Territorial Emergence Management , 2011, 2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications.