Physiology-Aware Rural Ambulance Routing

In emergency patient transport from rural medical facility to center tertiary hospital, real-time monitoring of the patient in the ambulance by a physician expert at the tertiary center is crucial. While telemetry healthcare services using mobile networks may enable remote real-time monitoring of transported patients, physiologic measures and tracking are at least as important and requires the existence of high-fidelity communication coverage. However, the wireless networks along the roads especially in rural areas can range from 4G to low-speed 2G, some parts with communication breakage. From a patient care perspective, transport during critical illness can make route selection patient state dependent. Prompt decisions with the relative advantage of a longer more secure bandwidth route versus a shorter, more rapid transport route but with less secure bandwidth must be made. The trade-off between route selection and the quality of wireless communication is an important optimization problem which unfortunately has remained unaddressed by prior work. In this paper, we propose a novel physiology-aware route scheduling approach for emergency ambulance transport of rural patients with acute, high risk diseases in need of continuous remote monitoring. We mathematically model the problem into an NP-hard graph theory problem, and approximate a solution based on a trade-off between communication coverage and shortest path. We profile communication along two major routes in a large rural hospital settings in Illinois, and use the traces to manifest the concept. Further, we design our algorithms and run preliminary experiments for scalability analysis. We believe that our scheduling techniques can become a compelling aid that enables an always-connected remote monitoring system in emergency patient transfer scenarios aimed to prevent morbidity and mortality with early diagnosis treatment.Comment: 6 pages, The Fifth IEEE International Conference on Healthcare Informatics (ICHI 2017), Park City, Utah, 201

Centralized versus market-based approaches to mobile task allocation problem: State-of-the-art

Centralized approach has been adopted for finding solutions to resource allocation problems (RAPs) in many real-life applications. On the other hand, market-based approach has been proposed as an alternative to solve the problem due to recent advancement in ICT technologies. In spite of the existence of some efforts to review the pros and cons of each approach in RAPs, the studies cannot be directly applied to specific problem domains like mobile task allocation problem which is characterised with high level of uncertainty on the availability of resources (workers). This paper aims to review existing studies on task allocation problems(TAPs) focusing on those two approaches and their comparison and identify major issues that need to be resolved for comparing the two approaches in mobile task allocation problems. Mobile Task Allocation Problem (MTAP) is defined and its problematic structures are explained in relation with task allocation to mobile workers. Solutions produced by each approach to some applications and variations of MTAP are also discussed and compared. Finally, some future research directions are identified in order to compare both approaches in function of uncertainty emerging from the mobile nature of the MTAP

Exploring Knowledge Engineering Strategies in Designing and Modelling a Road Traffic Accident Management Domain

Formulating knowledge for use in AI Planning engines is currently something of an ad-hoc process, where the skills of knowledge engineers and the tools they use may significantly influence the quality of the resulting planning application. There is little in the way of guidelines or standard procedures, however, for knowledge engineers to use when formulating knowledge into planning domain languages such as PDDL. This paper seeks to investigate this process using as a case study a road traffic accident management domain. Managing road accidents requires systematic, sound planning and coordination of resources to improve outcomes for accident victims. We have derived a set of requirements in consultation with stakeholders for the resource coordination part of managing accidents. We evaluate two separate knowledge engineering strategies for encoding the resulting planning domain from the set of requirements: (a) the traditional method of PDDL experts and text editor, and (b) a leading planning GUI with built in UML modelling tools. These strategies are evaluated using process and product metrics, where the domain model (the product) was tested extensively with a range of planning engines. The results give insights into the strengths and weaknesses of the approaches, highlight lessons learned regarding knowledge encoding, and point to important lines of research for knowledge engineering for planning

Optimizing emergency preparedness and resource utilization in mass-casualty incidents

This paper presents a response model for the aftermath of a Mass-Casualty Incident (MCI) that can be used to provide operational guidance for regional emergency planning as well as to evaluate strategic preparedness plans. A mixed integer programming (MIP) formulation is proposed for the combined ambulance dispatching, patient-to-hospital assignment, and treatment ordering problem. T he goal is to allocate effectively the limited resources during the response so as to improve patient outcomes, while the objectives are to minimize the overall response time and the total flow time required to treat all patients, in a hierarchical fashion. The model is solved via exact and MIP-based heuristic solution methods. The applicability of the model and the performance of the new methods are challenged on realistic MCI scenarios. We consider the hypothetical case of a terror attack at the New York Stock Exchange in Lower Manhattan with up to 150 trauma patients. We quantify the impact of capacity-based bottlenecks for both ambulances and available hospital beds. We also explore the trade-off between accessing remote hospitals for demand smoothing versus reduced ambulance transportation times

How Resilient Are Our Societies? Analyses, Models, and Preliminary Results

Traditional social organizations such as those for the management of healthcare and civil defence are the result of designs and realizations that matched well with an operational context considerably different from the one we are experiencing today: A simpler world, characterized by a greater amount of resources to match less users producing lower peaks of requests. The new context reveals all the fragility of our societies: unmanageability is just around the corner unless we do not complement the "old recipes" with smarter forms of social organization. Here we analyze this problem and propose a refinement to our fractal social organizations as a model for resilient cyber-physical societies. Evidence to our claims is provided by simulating our model in terms of multi-agent systems.Comment: Paper submitted for publication in the Proc. of SERENE 2015 (http://serene.disim.univaq.it/2015/

Online optimization of casualty processing in major incident response: An experimental analysis

When designing an optimization model for use in mass casualty incident (MCI) response, the dynamic and uncertain nature of the problem environment poses a significant challenge. Many key problem parameters, such as the number of casualties to be processed, will typically change as the response operation progresses. Other parameters, such as the time required to complete key response tasks, must be estimated and are therefore prone to errors. In this work we extend a multi-objective combinatorial optimization model for MCI response to improve performance in dynamic and uncertain environments. The model is developed to allow for use in real time, with continuous communication between the optimization model and problem environment. A simulation of this problem environment is described, allowing for a series of computational experiments evaluating how model utility is influenced by a range of key dynamic or uncertain problem and model characteristics. It is demonstrated that the move to an online system mitigates against poor communication speed, while errors in the estimation of task duration parameters are shown to significantly reduce model utility