<p>Smart and connected technology solutions have emerged as a promising way to enhance EMS services, particularly in areas where access to professional services is limited. However, a significant challenge for improving their implementation is determining which technologies to use and how they will change current logistic operations to enhance service efficiencies and expand access to care. In this context, this thesis explores opportunities for the smart and connected technology solutions.</p>
<p>The first study explores the use of medically trained volunteers in the community, known as Citizen Responders (CRs). These individuals can be quickly notified of an EMS request upon its arrival via a mobile alert receiver, which allows them to provide timely and potentially life-saving assistance before an ambulance arrives. However, traditional EMS logistic decision platforms are not equipped to effectively leverage the sharing of the real-time CR information enabled by connected technologies, such as their location and availability. To improve coordination between CRs and ambulances, this study proposes two decision tools that incorporate real-time CR information: one for redeploying ambulances after they complete service and another for dispatching ambulances in response to calls. The redeployment procedure uses mixed-integer linear programming (MILP) to maximize patient survival, while the dispatch procedure enhances a locally optimal dispatch procedure by integrating real-time CR information for priority-differentiated emergencies.</p>
<p>In the second study, a third decision tool was developed to take advantage of the increasing availability of feature information provided by connected technologies: an AI-enabled dispatch rule recommendation model that is more usable for dispatchers than black-box decision models. This is a model based on supervised learning that outputs a “promising” metric-based dispatch rule for the human decision-maker. The model maintains the usability of rules while enhancing the system’s performance and alleviating the cognitive burden of dispatchers. A set of experiments were performed on a self-developed simulator to assess the performance of all the decision tools. The findings suggest they have the potential to significantly enhance the EMS system performance. </p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/23110409 |
| Date | 31 May 2023 |
| Creators | Juan Camilo Paz Roa (15853232) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY-NC-SA 4.0 |
| Relation | https://figshare.com/articles/thesis/_strong_Operational_Decision_Tools_for_SMART_Emergency_Medical_Services_strong_/23110409 |
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