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Decision making for bioterror preparedness involves estimating the consequences of different attack scenarios paired with alternative preparedness and response policies, and selecting an appropriate strategy to minimize deaths, disease, and costs to society. Smallpox vaccination policy provides an excellent case study of these concepts in action. We review both the smallpox vaccination policy debate in the United States circa 2002, and the successful use of operations research methods to influence policy in this arena. [ABSTRACT FROM AUTHOR]

A discrete event simulation describes the screening and natural history of eye disease in patients with diabetes, using the POST simulation software. Discrete event simulation, unlike other modeling techniques, can show the interaction between screening and the two main diabetic eye disease processes. Results show that there is a tradeoff between screening frequency, screening sensitivity and patient compliance. The extent to which screening is cost effective is not clear cut. We have little data on the cost of blindness and different views on the appropriate quality-of-life values to assume. The model can be extended to evaluate prevention and treatment for all the complications of diabetes. [ABSTRACT FROM AUTHOR]

Motivated by recent therapeutic initiatives for Alzheimer’s disease, we developed a mathematical model of the accumulation of amyloid β-protein (Aβ) in the brain. The model incorporates the production and clearance of Aβ monomers, and the elongation and fragmentation of polymers by monomer aggregation and break-off, respectively. Our analysis suggests that Aβ dynamics are dictated by a single unitless measure referred to as the polymerization ratio, which is the product of the production and elongation rates divided by the product of the clearance and fragmentation rates. Cerebral Aβ burden (i.e., the total number of Aβ molecules, whether they exist as monomers or polymers) attains a finite steady-state level if this ratio is less than one, and undergoes sustained growth if this ratio is greater than one. The highly nonlinear relationship between the polymerization ratio and the steady-state Aβ burden implies that a modest reduction in the polymerization ratio achieves a significant decrease in the Aβ burden. Our model also predicts that after initiation or discontinuation of treatment, it may take months to reach a new steady-state Aβ burden. Taken together, our findings suggest that the research community should focus on developing agents that provide a modest reduction of the polymerization ratio while avoiding long-term toxicity. Finally, our model can be used to indirectly estimate several crucial parameters that are difficult to measure directly: the production rate, the fragmentation rate and the strength of treatment. [ABSTRACT FROM AUTHOR]

The effects of immunity stimulated by natural colonization with Nontypeable Haemophilus influenzae (NTHi) were assessed using population models of transmission and data from the literature on NTHi colonization prevalence by age, NTHi acute otitis media (AOM) incidence by age, NTHi antibody levels, and colonization duration. The models allowed both contact patterns and immunity to influence colonization and disease patterns by age. To fit the data, the models required colonization to stimulate immunity affecting both transmission (susceptibility and contagiousness) and pathogenicity (AOM given colonization). Model analysis demonstrated that immunity affecting transmission influenced AOM incidence in the first year of life from 4.6 to 39.5 times as much as immunity reducing pathogenicity. This differential decreased with age until age three and then rose again. It was important, however, across all age groups. The conclusion that immunity affecting transmission had larger effects on AOM incidence than immunity affecting pathogenicity was robust to model form and to reasonable variation in the data. Because sensitivity to NTHi strain interactions and age patterns of infection by strain could not be assessed and because data on the distribution of NTHi strains across all ages are deficient, this conclusion must still be viewed as tentative. Nonetheless, these results make it imperative that trials of potential NTHi vaccines be designed to insure accurate assessment of effects on transmission. The models presented here provide the basis for the construction of discrete individual simulation models for use in designing the most informative and powerful vaccine trials. [ABSTRACT FROM AUTHOR]

This chapter describes a suite of optimization tools for radiation treatment planning within the Matlab programming environment. The data included with these tools was computed for real patient cases using a Monte Carlo dose engine. The formulation of a series of optimization models is described that utilizes this data within a modeling system. Furthermore, visualization techniques are provided that assist in validating the quality of each solution. The versatility and utility of the tools are shown using a sequence of optimization techniques designed to generate a practical solution. These tools and the associated data are available for download from www.cs.wisc.edu/~ferris/3dcrt. [ABSTRACT FROM AUTHOR]

Intensity modulated radiotherapy treatment (IMRT) design is the process of choosing how beams of radiation will travel through a cancer patient to treat the disease, and although optimization techniques have been suggested since the 1960s, they are still not widely used. Instead, the vast majority of treatment plans are designed by clinicians through trial-and-error. Modern treatment facilities have the technology to treat patients with extremely complicated plans, and designing plans that take full advantage of the technology is tedious. The increased technology found in modern treatment facilities makes the use of optimization paramount in the design of successful treatment plans. The goals of this work are to 1) present a concise description of the linear models that are under current investigation, 2) develop the analysis certificates that these models allow, and 3) suggest future research avenues. [ABSTRACT FROM AUTHOR]

This chapter describes treatment planning optimization in brachytherapy and the design of a clinical decision support system. Brachytherapy refers to the placement of radioactive sources (seeds) inside a tumor site. The fundamental problem in treatment planning for brachytherapy is to determine where to place sources so as to deliver a sufficient radiation dose to kill the cancer, while limiting exposure of healthy tissue. We first present the sequence of steps that are involved in brachytherapy treatment planning. State-of-the-art mixed integer programming models are then described and some algorithmic approaches are outlined. The automated clinical decision support system allows for real-time generation of optimal seed configurations using ultrasound images acquired prior to seed implantation, and dynamic dose correction during the implantation process. [ABSTRACT FROM AUTHOR]

Mammography is a vital screening test for breast cancer because early diagnosis is the most effective means of decreasing the death rate from this disease. However, interpreting the mammographic images and rendering the correct diagnosis is challenging. The diagnostic accuracy of mammography varies with the expertise of the radiologist interpreting the images, resulting in significant variability in screening performance. Radiologists interpreting mammograms must manage uncertainties arising from a multitude of findings. We believe that much of the variability in mammography diagnostic performance arises from heuristic errors that radiologists make in managing these uncertainties. We developed a Bayesian network that models the probabilistic relationships between breast diseases, mammographic findings and patient risk factors. We have performed some preliminary evaluations in test cases from a mammography atlas and in a prospective series of patients who had biopsy confirmation of the diagnosis. The model appears useful for clarifying the decision about whether to biopsy abnormalities seen on mammography, and also can help the radiologist correlate histopathologic findings with the mammographic abnormalities observed. Our preliminary experience suggests that this model may help reduce variability and improve overall interpretive performance in mammography. [ABSTRACT FROM AUTHOR]

Despite many proven advances in patient care over the last 10 years, asthma continues to impose a large and growing burden on society. Persistent clinician non-adherence to recommended practice is well documented, but little is known about the clinical impact and economic costs of alternative approaches to asthma patient care. In this chapter, we introduce the Asthma Policy Model, a state-transition simulation that we have developed to forecast asthma-related symptoms, acute exacerbations, quality-adjusted life expectancy, health care costs, and cost-effectiveness. We begin with a detailed survey of the epidemiological, clinical, and policy context that motivates our work. With a modeling audience in mind, we then describe the considerations that produced the current analytic structure and input datasets. We illustrate the policy relevance of the model by describing our recent work on the cost-effectiveness of inhaled corticosteroid therapy in a population of adult patients with mild-to-moderate disease. We close the chapter with a discussion of plans for future refinements and applications. [ABSTRACT FROM AUTHOR]

This chapter presents an analytical framework based on Bayesian analysis that was used to evaluate human and management risk factors for patients undergoing anesthesia, and the effects of a variety of proposed measures for mitigating those risks. More specifically, the analysis considers the frequency and the effects of various risk factors, the extent to which safety measures based on management improvements can decrease the chances that they occur, and the effects of these safety measures on patient risk. The analysis demonstrates that the accident sequences that had received the most attention because they had made the headlines were not the largest contributors to the overall patient risk. The analysis finds that most of the problems are not caused by rare events, but by more mundane factors such as fatigue and poor supervision of residents. Closer supervision of residents, periodic re-certification and simulator training appeared to be among the most potentially effective measures for reducing patient risk. A similar model can be applied to other medical problems involving risk, such as assessing the performance of surgeons or early assessment of medical devices (before comprehensive testing on large populations). [ABSTRACT FROM AUTHOR]

Influence diagrams are now a well established tool for modeling in decision analysis. Recently, dynamic influence diagrams have been applied to help structure stochastic processes. This chapter discusses dynamic influence diagrams for structuring continuous-time Markov chains, with particular focus on medical decision modeling. We describe our freely available Excel-based software package StoTree, in which dynamic influence diagram models may be readily formulated and solved. We present medical applications as examples, including a previously published cost-effectiveness analysis for total hip replacement. [ABSTRACT FROM AUTHOR]

Medical treatment decisions are often sequential and uncertain. Markov decision processes (MDPs) are an appropriate technique for modeling and solving such stochastic and dynamic decisions. This chapter gives an overview of MDP models and solution techniques. We describe MDP modeling in the context of medical treatment and discuss when MDPs are an appropriate technique. We review selected successful applications of MDPs to treatment decisions in the literature. We conclude with a discussion of the challenges and opportunities for applying MDPs to medical treatment decisions. [ABSTRACT FROM AUTHOR]

There was a broad measure of convergence among health care professionals in a central London Health Authority that changes in patterns of care delivery and specialist staffing required a reduction in the number of inpatient units, a substitution of ambulatory care units, and an extension of community care provision. Strategic Choice was used in a series of workshops with intervening analysis to convert this ‘in principle’ agreement into a specific proposal that achieved consensus among stakeholders. This process is analysed in terms of the opportunities provided by sequential workshops and the difficulties presented by inter-organizational working and absent stakeholders. [ABSTRACT FROM AUTHOR]

The continued shortage of organs implies that the organ allocation policy determines who lives and who dies. This creates one of medicine’s most vexing dilemmas, and the crux of this dilemma is the tradeoff between clinical efficiency and equity. This chapter describes OR models that have been used to study the problem and the related tradeoffs. A taxonomy of the literature is developed, and a description of the key analytical and computational models is provided. Directions for future research are also presented. [ABSTRACT FROM AUTHOR]

Infectious microbes can be transmitted through the drinking water supply. Recent research indicates that infection transmission dynamics influence the public health benefit of water treatment interventions, although some risk assessments currently in use do not fully account for those dynamics. This chapter models the public health benefit of two interventions: improvements to centralized water treatment facilities, and localized point-of-use treatments in the homes of particularly susceptible individuals. A sensitivity analysis indicates that the best option is not as obvious as that suggested by an analysis that ignores infection dynamics suggests. Deterministic and stochastic dynamic systems models prove to be useful tools for assessing the dynamics of risk exposure. [ABSTRACT FROM AUTHOR]

How can decision makers best choose among competing epidemic control programs and populations? The problem of resource allocation for epidemic control is complex, and differs in a number of significant ways from traditional resource allocation problems. A variety of OR-based methods have been applied to the problem, including standard cost-effectiveness analysis, linear and integer programming, simulation, numerical procedures, optimal control methodologies, nonlinear optimization, and heuristic approaches. This chapter reviews a number of these models. This chapter does not aim to be an exhaustive review of the literature; rather, we discuss an illustrative subset of existing models. We conclude with discussion of promising areas for further research. [ABSTRACT FROM AUTHOR]

Several challenges have arisen in childhood immunization programs as vaccine manufacturers have become more successful in developing new vaccines for childhood diseases. Their success has created a combinatorial explosion of choices for health-care providers and other purchasers of pediatric vaccines, which in turn has created a new set of economic problems and issues related to determining which vaccines should be combined into single injections and how to design lowest overall cost formularies for pediatric immunization. This chapter provides a review of how operations research modeling and analysis tools can be used to address a variety of economic issues surrounding pediatric vaccine formulary design and pricing. A summary is presented of the pediatric immunization problems that have been studied using integer programming models, as well as the assumptions used to model such problems. A description of the methodologies used is provided. A summary of the results obtained with these models for a particular pentavalent combination vaccine that recently gained Food and Drug Administration (FDA) approval for pediatric immunization is presented. Concluding comments and directions for future research are also discussed. [ABSTRACT FROM AUTHOR]

Development of a vaccine remains the best hope for curtailing the worldwide pandemic caused by human immunodeficiency virus (HIV) infection. Due to the complex biology of HIV infection, there is increasing concern that an HIV vaccine may provide incomplete protection from infection. In addition to reducing susceptibility to disease, an HIV vaccine may also prolong life in people who acquire HIV despite vaccination, and may reduce HIV transmission. We evaluated how varying degrees of vaccine efficacy for susceptibility, progression of disease, and infectivity influence the costs and benefits of a vaccine program in a population of men who have sex with men, We found that the health benefits, and thus cost effectiveness, of HIV vaccines were strikingly dependent on each of the types of vaccine efficacy. We also found that vaccines with even modest efficacy provided substantial health benefits and were cost effective or cost saving. Although development of an HIV vaccine has been extremely difficult, even a partially effective HIV vaccine could dramatically change the course of the HIV epidemic. [ABSTRACT FROM AUTHOR]

Operations research has contributed to the control of blood-borne epidemics among injection drug users. The analysis of random-mixing models has led to a deeper understanding of both syringe exchange programs and substance abuse treatment in the control of HIV/AIDS and hepatitis. This chapter presents some of these results, and analyzes illustrative models to show how simplified, but empirically pertinent mathematical models can assist policymakers evaluate public health interventions. [ABSTRACT FROM AUTHOR]

In this chapter we discuss recent operations research advances in modeling drug treatment programs for injection drug users, in particular maintenance treatment programs for opioid addicts. We focus on four main questions for which operations research techniques have proven beneficial: How effective are opioid maintenance programs? Do the benefits of methadone maintenance treatment justify its costs? Are alternative forms of maintenance treatment cost effective? If opioid maintenance treatment programs are expanded, how many new treatment slots are needed? We discuss a number of methodological issues and highlight directions for future research. [ABSTRACT FROM AUTHOR]

Illicit drugs create serious health problems whose management is complicated by illegality, poor data, and market dynamics. Quantitative analysis can and does play a key role in clarifying implications of strategic choices concerning collective response to these problems. This chapter summarizes key arguments and findings concerning the effectiveness of various prevention and treatment strategies, including supply control measures. Among them are that conventional prevention programs are not very effective in an absolute sense, but they are so cheap that they are cost-effective. Likewise, treatment programs can be cost-effective despite very high relapse rates, in part because periods of heavy use impose such enormous costs on society. Enforcement can play a key role in diffusing the positive feedback loop created by contagious spread of initiation during the early phase of new drug epidemics because of its unique ability among diverse drug control interventions to focus its impact on the present. [ABSTRACT FROM AUTHOR]

Operations research (OR) provides an excellent set of tools for decision makers who regulate the use of new treatments or medications. The decision about whether to use a new treatment must typically be made well before long-term trials or database studies can be conducted. However, large amounts of information about new treatments are available from the clinical trials required for drug registration. OR models can synthesize this information and use it to predict expected costs and benefits of long-term treatment use within a given population. Such analysis provides valuable additional information for the decision maker when a novel treatment is initially being considered. These analyses are like duct tape for the decision maker: they are designed to make use of the best currently available information to help current decisions, thereby bridging the gap until better information becomes available. [ABSTRACT FROM AUTHOR]

Measuring health outcomes is critical for individual and societal decision making. This chapter briefly reviews the field of health outcomes modeling in general and provides detailed theoretical background for one specific class of such models, the Quality-Adjusted Life Years model, which is primarily grounded in operations research and utility theory. The chapter describes methodological issues and concludes with a discussion of promising areas for further research. [ABSTRACT FROM AUTHOR]

Risks to public health arise from infectious disease, exposure to toxic substances such as asbestos, environmental insults, and from lifestyle risks such as smoking. The risk assessment that must precede healthcare interventions or legislation requires probabilistic, statistical and computational methodologies. The introduction to this chapter discusses how our perception of the risks to public health is changing, and identifies some trends in the methodologies used for risk analysis. Risk assessment is largely characterised by likelihood-based statistical inference, using point-process models of disease intensity as a function of position in space and time. Conditional likelihoods such as Cox’s partial likelihood and matched-pairs logistic regression are widely used to eliminate confounding variables. Two examples of the use of such conditional likelihoods are given. In the first, new tests for the space-time clustering of cases characteristic of infectious disease are derived and exemplified. In a second application of conditional likelihood, some research on risks of Shigella infection to schoolchildren arising at school or from playmates is presented. The original content of this chapter is two new tests of space-time clustering, and a case-study using an unusual conditional likelihood. [ABSTRACT FROM AUTHOR]

Simulation, as it is typically taught, is a rather mechanical process. Students are taught to follow a recipe: analyze a system, design a model, convert the model to computer code, collect data, verify, validate, and analyze the output. In practice, many analysts find that simulation is an odd combination of art, science, and marketing. Using this technique appropriately, in any industry, involves more than simply following the text book. In our experience, health care provides some rather unique challenges for the modeler. This chapter describes four different practical examples of using simulation to analyze a problem in an acute care hospital. The specific examples are not described in detail, since the applications have appeared in other publications. The emphasis here is to present some of the obstacles that were encountered and the lessons learned. [ABSTRACT FROM AUTHOR]

This chapter uses windows and cone ratio analysis - a longitudinal and weight- restriction application of Data Envelopment Analysis (DEA) - to develop a methodology for analyzing organizational performance of community mental health centers (CMHCs); the chapter also develops measures of efficiency as a basis for improving productivity in behavioral health care. Specifically, non-hospital services provided by CMHCs were studied. Data limitations are noted in relation to use of the method and to the results. The model is shown to capture the impact of managed care on CMHC efficiency. The cone ratio version of the model, using weight restrictions, identified six super-efficient CMHCs, which had been consistently efficient since the implementation of managed behavioral care. The potential usefulness of this method for public and private mental health systems and for managed care companies is discussed. [ABSTRACT FROM AUTHOR]

Elective surgery typically generates 40 percent or more of a hospital’s total revenue, and individual surgeons almost always have a net positive contribution margin. Perioperative services include surgical operations, preoperative care of patients, and post-operative care. This chapter presents a method to identify best practices among hospitals’ perioperative services using Data Envelopment Analysis (DEA). This analysis included 44,033 procedures performed by 3,502 surgeons at 53 non-metropolitan Pennsylvania hospitals. Eight procedures, each performed by one surgical specialty, were selected. For each hospital, DEA 1) identifies untapped markets for surgery; 2) identifies relatively high and low procedure volumes among specialties; and 3) suggests a strategy for increasing surgical volume for inefficient hospitals. Findings may be used by managers of perioperative services to aid in resource allocation decisions, such as hiring and recruitment among different surgical specialties. [ABSTRACT FROM AUTHOR]

The chapter starts with a strategic overview of the blood banking supply chain. We then proceed to ask and answer questions concerning (i) the blood banking functions that should be performed and at what locations, (ii) which donor areas and transfusion services should be assigned to which community blood centers, (iii) how many community blood centers should be in a region, (iv) where they should be located and (v) how supply and demand should be coordinated. Then the many tactical operational issues involved in collecting blood, producing multiple products, setting and controlling inventory levels, allocating blood to hospitals, delivery to multiple sites, and making optimal decisions about issuing, crossmatching, and crossmatch releasing blood and blood products are presented. The chapter concludes with areas for future research. [ABSTRACT FROM AUTHOR]

The ambulance-planning problem includes operational decisions such as choice of dispatching policy, strategic decisions such as where ambulances should be stationed and at what times they should operate, and tactical decisions such as station location selection. Any solution to this problem requires careful balancing of political, economic and medical objectives. Quantitative decision processes are becoming increasingly important in providing public accountability for the resource decisions that have to be made. This chapter discusses a simulation and analysis software tool ‘BartSim’ that was developed as a decision support tool for use within the St. John Ambulance Service (Auckland Region) in New Zealand (St. Johns). The novel features incorporated within this study include the use of a detailed time-varying travel model for modelling travel times in the simulation,methods for reducing the computational overhead associated with computing time-dependent shortest paths in the travel model,the direct reuse of real data as recorded in a database (trace-driven simulation), andthe development of a geographic information sub-system (GIS) within BartSim that provides spatial visualisation of both historical data and the results of what-if simulations. Our experience with St. Johns, and discussions with emergency operators in Australia, North America, and Europe, suggest that emergency services do not have good tools to support their operations management at all levels (operational, strategic and tactical). Our experience has shown that a customized system such as BartSim can successfully combine GIS and simulation approaches to provide a quantitative decision support tool highly valued by management. Further evidence of the value of our system is provided by the recent selection of BartSim by the Metropolitan Ambulance Service for simulation of their operations in Melbourne, Australia. This work has led to the development of BartSim’s successor, Siren (Simulation for Improving Response times in Emergency Networks), which includes many enhancements to handle the greater complexities of the Melbourne operations. [ABSTRACT FROM AUTHOR]

This chapter reviews the location set covering model, maximal covering model and P-median model. These models form the heart of the models used in location planning in health care. The health care and related location literature is then classified into one of three broad areas: accessibility models, adaptability models and availability models. Each class is reviewed and selected formulations are presented. A novel application of the set covering model to the analysis of cytological samples is then discussed. The chapter concludes with directions for future work. [ABSTRACT FROM AUTHOR]

Faced with diminishing government subsidies, competition, and the increasing influence of managed care, hospitals are under enormous pressure to cut costs. In response to these pressures, many hospitals have made drastic changes including downsizing beds, cutting staff, and merging with other hospitals. These critical capacity decisions generally have been made without the help of OR model-based analyses, routinely used in other service industries, to determine their impact. Not surprisingly, this has often resulted in diminished patient access without any significant reductions in costs. Moreover, payers and patients are increasingly demanding improved clinical outcomes and service quality. These factors, combined with their complex dynamics, make hospitals an important and rich area for the development and use of OR/MS tools and frameworks to help identify capacity needs and ways to use existing capacity more efficiently and effectively. In this chapter we describe the general background and issues involved in hospital capacity planning, provide examples of how OR models can be used to provide important insights into operational strategies and practices, and identify opportunities and challenges for future research. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]