Applications of Stochastic Optimization Models in Patient Screening and Blood Inventory Management PDF Download

Author: Alireza Bagh Abbas Sabouri
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Author: Joel Goh
Publisher:
ISBN:
Category :
Languages : en
Pages :

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This dissertation studies how mathematical modeling can be used in conjunction with empirical data to provide insight into health policy and medical decision-making. We consider three specific questions. First, how should drug safety regulators implement a postmarketing drug surveillance system that accounts for multiple adverse events? Second, what is the aggregate contribution of workplace stressors toward poor health outcomes and health spending in the U.S.? Third, how should rigorous cost-effectiveness analyses be conducted for medical innovations, when data are scarce and unreliable? These are important questions that have thus far eluded definitive answers because existing data sources and models cannot be directly applied to answer these questions satisfactorily. Therefore, we try to address these questions by developing new data-driven mathematical models, which draw ideas from stochastic analysis and optimization theory. In Chapter 1, we develop a new method for postmarketing surveillance of a drug, in order to detect any adverse side effects that were not uncovered during pre-approval clinical trials. Because of the recent proliferation of electronic medical records, regulators can now observe person-level data on drug usage and adverse event incidence in a population. Potentially, they can use these data to monitor the drug, and flag it as unsafe if excessive adverse side effects are observed. There are two key features of this problem that make it challenging. First, the data are accumulated in time, which complicates the regulators' decision process. Second, adverse events that occur in the past can affect the risk that other adverse events occur in the future. We propose a drug surveillance method, called QNMEDS, which simultaneously addresses these two issues. QNMEDS is based on the paradigm of sequential hypothesis testing, and it works by continuously monitoring a vector-valued test-statistic process until it crosses a stopping boundary. Our analysis focuses on prescribing how this boundary should be designed. We use a queueing network to model the occurrence of events in patients, which also allows us to capture the correlations between adverse events. Exact analysis of the model is intractable, and we develop an asymptotic diffusion approximation to characterize the approximate distribution of the test-statistic process. We then use mathematical optimization to design the stopping boundary to control the false alarm rate below an exogenously-specified value and minimize the expected detection time. We conduct simulations to demonstrate that QNMEDS works as designed and has advantages over a heuristic that is based on the classical Sequential Probability Ratio Test. In Chapter 2, we describe a model-based approach to quantify the relationship between workplace stressors and health outcomes and cost. We considered ten stressors: Unemployment, lack of health insurance, exposure to shift work, long working hours, job insecurity, work-family conflict, low job control, high job demands, low social support at work, and low organizational justice. There is widespread empirical evidence that individual stressors are associated with poor health outcomes, but the aggregate health effect of the combination of these stressors is not well understood. Our goal was to estimate the overall contribution of these stressors toward (a) annual healthcare spending, and (b) annual mortality in the U.S. The central difficulty in deriving these estimates is the absence of a single, longitudinal dataset that records workers' exposure to various workplace stressors as well as their health outcomes and spending. Therefore, we developed a model-based approach to tackle this problem. The model has four input parameters which were estimated from separate data sources: (a) the joint distribution of workplace exposures in the U.S., which we estimated from the General Social Survey (GSS); (b) the relative risk of each outcome associated with each exposure, which we estimated from an extensive meta-analysis of the epidemiological literature; (c) the status-quo prevalence of each health outcome; and (d) the incremental cost of each health outcome, which were both estimated using the Medical Panel Expenditure Survey (MEPS). The model separately derives optimistic and conservative estimates of the effect of multiple workplace exposures on health, and uses an optimization-based approach to calculate upper and lower bounds around each estimate to account for the correlation between exposures. We find that more than 120,000 deaths per year and approximately 5-8% of annual healthcare costs are associated with and may be attributable to how U.S. companies manage their work force. Our results suggest that more attention should be paid to management practices as important contributors to health outcomes and costs in the U.S. In Chapter 3, we study the problem of assessing the cost-effectiveness of a medical innovation when data are scarce or highly uncertain. Models based on Markov chains are typically used for medical cost-effectiveness analyses. However, if such models are used for innovations, many elements of the chain's transition matrix may be very imprecise due to data scarcity. While sensitivity analyses can be used to assess the effect of a small number of uncertain parameters, they quickly become computationally intractable as the number of uncertainties grows. At present, only ad-hoc methods exist for performing such analyses when there are a large number of uncertain parameters. Our analysis focuses on an abstraction of this problem, which is how to calculate the best and worst discounted value of a Markov chain over an infinite horizon with respect to a vector of state-wise rewards, when many of its transition elements are only known up to an uncertainty set. We prove the following sharp result: If the uncertainty set has a row-wise property, which is a reasonable assumption for most applied problems, then these values can be tractably computed by iteratively solving certain convex optimization problems. However, in the absence of this row-wise property, evaluating these values is computationally intractable (NP-hard). We apply our method to the evaluate the cost-effectiveness of a new screening method for colorectal cancer, annual fecal immunochemical testing (FIT) for persons over the age of 55. Our results suggest that FIT is a highly cost-effective alternative to the current guidelines, which prescribe screening by colonoscopy at 10-year intervals.

Author: Pradip Kumar Ray
Publisher: Springer
ISBN: 9811056315
Category : Medical
Languages : en
Pages : 137

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This edited volume focuses on research conducted in the area of healthcare systems management. Chapters are extensions of works presented at the International Conference on Management of Ergonomic Design, Industrial Safety and Healthcare Systems. The book addresses the need to have the knowledge of technological and resource management, clinical performances and quality of healthcare delivery systems in order to make hospital systems well and adequately designed and operationally effective ensuring the quality of healthcare to patients. It is a useful resource for students, researchers, industrial professionals and design engineers.

Author: Brian T. Denton
Publisher: Springer Science & Business Media
ISBN: 1461458854
Category : Business & Economics
Languages : en
Pages : 542

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From the Preface: Collectively, the chapters in this book address application domains including inpatient and outpatient services, public health networks, supply chain management, and resource constrained settings in developing countries. Many of the chapters provide specific examples or case studies illustrating the applications of operations research methods across the globe, including Africa, Australia, Belgium, Canada, the United Kingdom, and the United States. Chapters 1-4 review operations research methods that are most commonly applied to health care operations management including: queuing, simulation, and mathematical programming. Chapters 5-7 address challenges related to inpatient services in hospitals such as surgery, intensive care units, and hospital wards. Chapters 8-10 cover outpatient services, the fastest growing part of many health systems, and describe operations research models for primary and specialty care services, and how to plan for patient no-shows. Chapters 12 – 16 cover topics related to the broader integration of health services in the context of public health, including optimizing the location of emergency vehicles, planning for mass vaccination events, and the coordination among different parts of a health system. Chapters 17-18 address supply chain management within hospitals, with a focus on pharmaceutical supply management, and the challenges of managing inventory for nursing units. Finally, Chapters 19-20 provide examples of important and emerging research in the realm of humanitarian logistics.

Author: Margaret L. Brandeau
Publisher: Springer Science & Business Media
ISBN: 1402080662
Category : Medical
Languages : en
Pages : 870

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In both rich and poor nations, public resources for health care are inadequate to meet demand. Policy makers and health care providers must determine how to provide the most effective health care to citizens using the limited resources that are available. This chapter describes current and future challenges in the delivery of health care, and outlines the role that operations research (OR) models can play in helping to solve those problems. The chapter concludes with an overview of this book – its intended audience, the areas covered, and a description of the subsequent chapters. KEY WORDS Health care delivery, Health care planning HEALTH CARE DELIVERY: PROBLEMS AND CHALLENGES 3 1.1 WORLDWIDE HEALTH: THE PAST 50 YEARS Human health has improved significantly in the last 50 years. In 1950, global life expectancy was 46 years [1]. That figure rose to 61 years by 1980 and to 67 years by 1998 [2]. Much of these gains occurred in low- and middle-income countries, and were due in large part to improved nutrition and sanitation, medical innovations, and improvements in public health infrastructure.

Author: Cengiz Kahraman
Publisher: Springer
ISBN: 3319654551
Category : Medical
Languages : en
Pages : 596

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Book Description
This book offers a comprehensive reference guide to operations research theory and applications in health care systems. It provides readers with all the necessary tools for solving health care problems. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts of operations research for the management of operating rooms, intensive care units, supply chain, emergency medical service, human resources, lean health care, and procurement. To foster a better understanding, the chapters include relevant examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on health care management problems. The book presents a dynamic snapshot on the field that is expected to stimulate new directions and stimulate new ideas and developments.

Author: Mohammad Hessam Olya
Publisher:
ISBN:
Category : Computer science
Languages : en
Pages : 133

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The results of this study show that feature representation and training related instances jointly increase the performance of patient workload prediction. Moreover, we have addressed two critical issues in team-based healthcare strategic and tactical planning. The first issue is to determine the optimal number of providers for multiple facilities and eligible patients for pay-to-travel incentives where the demand and location of patients are unknown. The second issue is to minimize the number of different healthcare teams and balance their workload within every single facility. We have developed a stochastic workforce and workload optimization model under various scenarios to address this issue. The result of prescriptive analysis suggests considering the randomness rather than replacing the stochastic variables by their expected value significantly contributes in reducing the overall cost of healthcare and practically enhancing access to care.

Author: Thaisiri Watewai
Publisher:
ISBN: 9780542826979
Category :
Languages : en
Pages : 462

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Traditional stochastic optimization paradigm typically assumes that the parameters describing a stochastic model are available to decision markers from an accurate estimation procedure or they can be learned using a Bayesian approach. In many situations, however, non-stationarities and the lack of data make calibration and Bayesian learning impossible, and we must make decision with an imperfect stochastic model.

Author: J. Christian Lang
Publisher: Springer Science & Business Media
ISBN: 3642042473
Category : Business & Economics
Languages : en
Pages : 271

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Book Description
Quantitativeapproachesforsolvingproductionplanningandinventorymanagement problems in industry have gained growing importance in the past years. Due to the increasinguse of AdvancedPlanningSystems, a widespreadpracticalapplicationof the sophisticated optimization models and algorithms developed by the Production Management and Operations Research community now seem within reach. The possibility that productscan be replaced by certain substitute productsexists in various application areas of production planning and inventory management. Substitutions can be useful for a number of reasons, among others to circ- vent production and supply bottlenecks and disruptions, increase the service level, reduce setup costs and times, and lower inventories and thereby decrease ca- tal lockup. Considering the current trend in industry towards shorter product life cycles and greater product variety, the importance of substitutions appears likely to grow. Closely related to substitutions are ?exible bills-of-materials and recipes in multi-level production systems. However, so far, the aspect of substitutions has not attracted much attention in academic literature. Existing lot-sizing models matching complex requirements of industrial optimization problems (e.g., constrained capacities, sequence-dependent setups, multiple resources) such as the Capacitated Lot-Sizing Problem with Sequence-Dependent Setups (CLSD) and the General Lot-Sizing and Scheduling Problem for Multiple Production Stages (GLSPMS) do not feature in substitution options.

Author: Nita H. Shah
Publisher: Springer Nature
ISBN: 981162156X
Category : Business & Economics
Languages : en
Pages : 228

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Book Description
This book presents a collection of mathematical models that deals with the real scenario in the industries. The primary objective of this book is to explore various effective methods for inventory control and management using soft computing techniques. Inventory control and management is a very tedious task faced by all the organizations in any sector of the economy. It makes decisions for policies, activities, and procedures in order to make sure that the right amount of each item is held in stock at any time. Many industries suffer from indiscipline while ordering and production mismatch. It is essential to provide best ordering policy to control such kind of mismatch in the industries. All the mathematical model solutions are provided with the help of various soft computing optimization techniques to determine optimal ordering policy. This book is beneficial for practitioners, educators, and researchers. It is also helpful for retailers/managers for improving business functions and making more accurate and realistic decisions.