39,826 research outputs found
Risk based Optimization for Improving Emergency Medical Systems
In emergency medical systems, arriving at the incident locationa few seconds early can save a human life. Thus, this paper is motivated by the need to reduce the response timeā time taken to arrive at the incident location after receivingthe emergency call ā of Emergency Response Vehicles, ERVs(ex: ambulances, fire rescue vehicles) for as many requests as possible. We expect to achieve this primarily by positioning the ārightā number of ERVs at the ārightā places and at the ārightā times. Given the exponentially large action space(with respect to number of ERVs and their placement) and the stochasticity in location and timing of emergency incidents,this problem is computationally challenging. To that end, ourcontributions building on existing data-driven approaches are three fold:1. Based on real world evaluation metrics, we provide a riskbased optimization criterion to learn from past incident data. Instead of minimizing expected response time, we minimize the largest value of response time such that the risk of finding requests that have a higher value is bounded(ex: Only 10% of requests should have a response time greater than 8 minutes).2. We develop a mixed integer linear optimization formulation to learn and compute an allocation from a set of inputrequests while considering the risk criterion.3. To allow for āliveā reallocation of ambulances, we provide a decomposition method based on Lagrangian Relaxation to significantly reduce the run-time of the optimization formulation.Finally, we provide an exhaustive evaluation on real-world datasets from two asian cities that demonstrates the improvement provided by our approach over current practice and the best known approach from literature
A Patient Risk Minimization Model for Post-Disaster Medical Delivery Using Unmanned Aircraft Systems
The purpose of this research was to develop a novel routing model for delivery of medical supplies using unmanned aircraft systems, improving existing vehicle routing models by using patient risk as the primary minimization variable.
The vehicle routing problem is a subset of operational research that utilizes mathematical models to identify the most efficient route between sets of points. Routing studies using unmanned aircraft systems frequently minimize time, distance, or cost as the primary objective and are powerful decision-making tools for routine delivery operations. However, the fields of emergency triage and disaster response are focused on identifying patient injury severity and providing the necessary care. This study addresses the misalignment of priorities between existing routing models and the emergency response industry by developing an optimization model with injury severity to measure patient risk.
Model inputs for this study include vehicle performance variables, environmental variables, and patient injury variables. These inputs are used to construct a multi-objective mixed-integer nonlinear programming (MOMINLP) optimization model with the primary objective of minimizing total risk for a set of patients. The model includes a secondary aim of route time minimization to ensure optimal fleet deployment but is constrained by the risk minimization value identified in the first objective. This multi-objective design ensures risk minimization will not be sacrificed for route efficiency while still ensuring routes are completed as expeditiously as possible.
The theoretical foundation for quantifying patient risk is based on mass casualty triage decision-making systems, specifically the emergency severity index, which focuses on sorting patients into categories based on the type of injury and risk of deterioration if additional assistance is not provided. Each level of the Emergency Severity Index is assigned a numerical value, allowing the model to search for a route that prioritizes injury criticality, subject to the appropriate vehicle and environmental constraints.
An initial solution was obtained using stochastic patient data and historical environmental data validated by a Monte Carlo simulation, followed by a sensitivity analysis to evaluate the generalizability and reliability of the model. Multiple what-if scenarios were built to conduct the sensitivity analysis. Each scenario contained a different set of variables to demonstrate model generalizability for various vehicle limitations, environmental conditions, and different scales of disaster response.
The primary contribution of this study is a flexible and generalizable optimization model that disaster planning organizations can use to simulate potential response capabilities with unmanned aircraft. The model also improves upon existing optimization tools by including environmental variables and patient risk inputs, ensuring the optimal solution is useful as a real-time disaster response tool
Safety and security management through an integrated multidisciplinary model and related integrated technological framework
The purpose of this paper is to illustrate a multidisciplinary model for safety and security management (IMMSSM) which can be implemented by means of a suitable Integrated Technological System Framework (ITSF) that can be based on Internet of Things (IoT)/Internet of Everything (IoE), showing also the significant role played by the integration of the elements that compose the model itself, thanks to a proper genetic algorithm studied for the specific context
Research Directions in Information Systems for Humanitarian Logistics
This article systematically reviews the literature on using IT (Information Technology) in humanitarian logistics focusing on disaster relief operations. We first discuss problems in humanitarian relief logistics. We then identify the stage and disaster type for each article as well as the articleās research methodology and research contribution. Finally, we identify potential future research directions
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
Ambulance Emergency Response Optimization in Developing Countries
The lack of emergency medical transportation is viewed as the main barrier to
the access of emergency medical care in low and middle-income countries
(LMICs). In this paper, we present a robust optimization approach to optimize
both the location and routing of emergency response vehicles, accounting for
uncertainty in travel times and spatial demand characteristic of LMICs. We
traveled to Dhaka, Bangladesh, the sixth largest and third most densely
populated city in the world, to conduct field research resulting in the
collection of two unique datasets that inform our approach. This data is
leveraged to develop machine learning methodologies to estimate demand for
emergency medical services in a LMIC setting and to predict the travel time
between any two locations in the road network for different times of day and
days of the week. We combine our robust optimization and machine learning
frameworks with real data to provide an in-depth investigation into three
policy-related questions. First, we demonstrate that outpost locations
optimized for weekday rush hour lead to good performance for all times of day
and days of the week. Second, we find that significant improvements in
emergency response times can be achieved by re-locating a small number of
outposts and that the performance of the current system could be replicated
using only 30% of the resources. Lastly, we show that a fleet of small
motorcycle-based ambulances has the potential to significantly outperform
traditional ambulance vans. In particular, they are able to capture three times
more demand while reducing the median response time by 42% due to increased
routing flexibility offered by nimble vehicles on a larger road network. Our
results provide practical insights for emergency response optimization that can
be leveraged by hospital-based and private ambulance providers in Dhaka and
other urban centers in LMICs
Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS
We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making
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