521 research outputs found
Three essays on optimization of the intensive care unit (ICU) management decisions
The intensive care unit (ICU) is one of the most crucial resources in the hospital. Improper ICU management causes many negative effects in the ICU itself and in other connected departments along the patient care path. This dissertation presents three papers on optimizing the ICU management decisions. The first paper provides the first structured and comprehensive review of ICU problems in OR/MS. The relevant papers are discussed based on a new framework. The second paper proposes a discrete-time MDP model to find admission and early discharge policies that minimize these negative consequences. By minimizing the medical consequences, the approach demonstrated significantly outperforms a myopic policy as applied by most hospitals in practice. The third paper compares eleven different management policies based on different KPIs by a simulation study. In comparison to the baseline case running on a FCFS rule, any management policy is superior regardless of the evaluation criteria
A derivative-free approach for a simulation-based optimization problem in healthcare
Hospitals have been challenged in recent years to deliver high quality care with limited resources. Given the pressure to contain costs,developing procedures for optimal resource allocation becomes more and more critical in this context. Indeed, under/overutilization of emergency room and ward resources can either compromise a hospital's ability to provide the best possible care, or result in precious funding going toward underutilized resources. Simulation--based optimization tools then help facilitating the planning and management of hospital services, by maximizing/minimizing some specific indices (e.g. net profit) subject to given clinical and economical constraints.
In this work, we develop a simulation--based optimization approach for the resource planning of a specific hospital ward. At each step, we first consider a suitably chosen resource setting and evaluate both efficiency and satisfaction of the restrictions by means of a discrete--event simulation model. Then, taking into account the information obtained by the simulation process, we use a derivative--free optimization algorithm to modify the given setting. We report results for a real--world problem coming from the obstetrics ward of an Italian hospital showing both the effectiveness and the efficiency of the proposed approach
Big Data and the Precision Medicine Revolution
The big data revolution is making vast amounts of information available in all sectors of the economy including health care. One important type of data that is particularly relevant to medicine is observational data from actual practice. In comparison to experimental data from clinical studies, observational data offers much larger sample sizes and much broader coverage of patient variables. Properly combining observational data with experimental data can facilitate precision medicine by enabling detection of heterogeneity in patient responses to treatments and tailoring of health care to the specific needs of individuals. However, because it is high-dimensional and uncontrolled, observational data presents unique methodological challenges. The modeling and analysis tools of the production and operations management field are well-suited to these challenges and hence POM scholars are critical to the realization of precision medicine with its many benefits to society.https://deepblue.lib.umich.edu/bitstream/2027.42/145441/1/1386_Hopp.pd
Drone-Delivery Network for Opioid Overdose -- Nonlinear Integer Queueing-Optimization Models and Methods
We propose a new stochastic emergency network design model that uses a fleet
of drones to quickly deliver naxolone in response to opioid overdoses. The
network is represented as a collection of M/G/K queuing systems in which the
capacity K of each system is a decision variable and the service time is
modelled as a decision-dependent random variable. The model is an
optimization-based queuing problem which locates fixed (drone bases) and mobile
(drones) servers and determines the drone dispatching decisions, and takes the
form of a nonlinear integer problem, which is intractable in its original form.
We develop an efficient reformulation and algorithmic framework. Our approach
reformulates the multiple nonlinearities (fractional, polynomial, exponential,
factorial terms) to give a mixed-integer linear programming (MILP) formulation.
We demonstrate its generalizablity and show that the problem of minimizing the
average response time of a network of M/G/K queuing systems with unknown
capacity K is always MILP-representable. We design two algorithms and
demonstrate that the outer approximation branch-and-cut method is the most
efficient and scales well. The analysis based on real-life overdose data
reveals that drones can in Virginia Beach: 1) decrease the response time by
78%, 2) increase the survival chance by 432%, 3) save up to 34 additional lives
per year, and 4) provide annually up to 287 additional quality-adjusted life
years
Operational Research: Methods and Applications
Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes
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