CM6 The use of Patient Self-Reports to Collect Health Care Resource Utilization Data

The conversion of hospital bill charges using Uniform Bill (UB)-92s and Medicare Ratios of Cost of Charges (RCCs) to costs in economic analyses is one of the most efficient, accurate and accessible ways of measuring resource consumption for US hospitalizations.
OBJECTIVES: We used the hospital bill charges to cost conversion method in our analysis of over 3,000 patients with 4,700 hospital bills in the recently completed PURSUIT pharmacoeconomic substudy. Using the PURSUIT trial experience as a model, this workshop will provide a detailed explanation of the cost to charge conversion methodolgy. We will begin with a detailed procedure for incorporating this method of cost collection into a clinical trial's overall protocol and case repot form. We will explain how to ensure collection of bills for all patient hospitalizations and procedures; how to interpret the UB-92; how to extract charges from the UB-92 and convert those charges into costs using each hospital's Medicare Ratio of Cost to Charges. This workshop will also explore mechanisms for ensuring successful compliance across sites in large, randomized clinical trails including inservice training for coordinators and common obstacles to successful and complete collection. In addition, the workshop will explore the limitations and strengths of this method compared with other cost collection methods. This workshop will be particularly useful to project leaders, clinical trial coordinators, and database managers interested in performing economic analyses as part of larger clinical trials or as stand-alone endeavors.     

Analysis of cost data in randomized trials: an application of the non-parametric bootstrap

Health economic evaluations are now more commonly being included in pragmatic randomized trials. However a variety of methods are being used for the presentation and analysis of the resulting cost data, and in many cases the approaches taken are inappropriate. In order to inform health care policy decisions, analysis needs to focus on arithmetic mean costs, since these will reflect the total cost of treating all patients with the disease. Thus, despite the often highly skewed distribution of cost data, standard non-parametric methods or use of normalizing transformations are not appropriate. Although standard parametric methods of comparing arithmetic means may be robust to non-normality for some data sets, this is not guaranteed. While the randomization test can be used to overcome assumptions of normality, its use for comparing means is still restricted by the need for similarly shaped distributions in the two groups. In this paper we show how the non-parametric bootstrap provides a more flexible alternative for comparing arithmetic mean costs between randomized groups, avoiding the assumptions which limit other methods. Details of several bootstrap methods for hypothesis tests and confidence intervals are described and applied to cost data from two randomized trials. The preferred bootstrap approaches are the bootstrap-t or variance stabilized bootstrap-t and the bias corrected and accelerated percentile methods. We conclude that such bootstrap techniques can be recommended either as a check on the robustness of standard parametric methods, or to provide the primary statistical analysis when making inferences about arithmetic means for moderately sized samples of highly skewed data such as costs.     

On the use of survival analysis techniques to estimate medical care costs.

Measurement of treatment costs is important in the evaluation of medical interventions. Accurate cost estimation is problematic, when cost records are incomplete. Methods from the survival analysis literature have been proposed for estimating costs using available data. In this article, we clarify assumptions necessary for validity of these techniques. We demonstrate how assumptions needed for valid survival analysis may be violated when these methods are applied to cost estimation. Our observations are confirmed through simulations and empirical data analysis. We conclude that survival analysis approaches are not generally appropriate for the analysis of medical costs and review several valid alternatives.     

MM3 Development of Drug Use Performance Models as Measures of Patient Care

Results from economic analyses of the effectiveness of new therapeutic innovations determine whether a new product will be reimbursed by a managed care organization or government agency. Often, the results of these economic analyses are presented as formal empirical analyses in scientific journal articles. With the pace of medical innovations submitted for approval on a payers fee schedule or formulary list ever increasing, it is important to convey the results of analysis as effectively and efficiently as possible. In response, interactive computer models have been developed to present the key findings of an economic analysis. Ideally, these models allow a potential buyer to customize a scientific analysis to determine their own reservation price for a new product. The quality and costs of these software applications vary-geatly. Given the resources expended to develop these models and time to produce them, it useful to examine the features of cost-effective "laptop model" design. This workshop will review an inventory of the features of laptop models. Participants will gain an understanding of the development process and costs for developing these models from the conceptual development phase to production of a stand-alone software application. A checklist of critical ingredients for software development will be reviewed with a special focus on role of a multidisciplinary development team and the capital resources required. A review of the discordance between scientists, biomedical manufactrures, software applications developers and potential clients and methods to gain consensus to build the application will be discussed. Examples from Project HOPE's and other firms' software development initiatives will be demonstrated as successful applications currently in use. Participants with a basic knowledge of computer applications, cost-effectiveness methods, and systems analysis will likely gain the most from this workshop.     

On the equivalence of some medical cost estimators with censored data

In clinical trials comparing different treatments and in health economics and outcomes research, medical costs are frequently analysed to evaluate the economical impacts of new treatment options and economic values of health-care utilization. Since Lin et al.'s first finding in the problem of applying the survival analysis techniques to the cost data, many new methods have been proposed. In this report, we establish analytic relationships among several widely adopted medical cost estimators that are seemingly different. Specifically, we report the equivalence among various estimators that were introduced by Lin et al., Bang and Tsiatis, and Zhao and Tian. Lin's estimators are formerly known to be asymptotically unbiased in some discrete censoring situations and biased otherwise, whereas all other estimators discussed here are consistent for the expected medical cost. Thus, we identify conditions under which these estimators become identical and, consequently, the biased estimators achieve consistency. We illustrate these relationships using an example from a clinical trial examining the effectiveness of implantable cardiac defibrillators in preventing death among people who had prior myocardial infarctions.     

The distribution of health care costs and their statistical analysis for economic evaluation

OBJECTIVE: Where patient level data are available on health care costs, it is natural to use statistical analysis to describe the differences in cost between alternative treatments. Health care costs are, however, commonly considered to be skewed, which could present problems for standard statistical tests. This review examines how authors report the distributional form of health care cost data and how they have analysed their results. METHOD: A review of cost-effectiveness studies that collected patient-level data on health care costs. To supplement the review, five datasets on health care costs are examined. Consideration is given to the use of parametric methods on the transformed scale and to non-parametric methods of analysing skewed cost data. RESULTS: Since economic analysis requires estimation in monetary units, the usefulness of transformation-based methods is limited by the inability to retransform cost differences to the original scale. Non-parametric rank sum methods were also found to be of limited use for economic analysis, partly due to the focus on hypothesis testing rather than estimation. Overall, the non-parametric approach of bootstrapping was found to offer a useful test of the appropriateness of parametric assumptions and an alternative method of estimation where those assumptions were found not to hold. CONCLUSIONS: Guidelines for the analysis of skewed health care cost data are offered.     

Conceptual framework for estimating the social cost of drug abuse

Drug abuse imposes costs on individuals and society. Researchers have produced several studies on a subset of tangible costs of drug abuse and other illnesses, but key tangible costs sometimes have been overlooked and, even when recognized, rarely have been estimated. An assortment of intangible costs also have received very little research attention. This study outlines a comprehensive conceptual framework for estimating the social cost of drug abuse. We address both the tangible and intangible costs for the drug-abusing and non-drug-abusing population. Our conceptual framework is based on critical reviews of new and traditional methods for estimating the costs of illness and disease including cost-of-illness methods, averting behavior methods, and utility valuation techniques. We show how the proposed methods can be combined with existing data to estimate the total social cost of drug abuse. Using social cost estimates will enable policymakers to more accurately assess the total burden of drug abuse and related problems on society.     

Consensus methods: characteristics and guidelines for use

Consensus methods are being used increasingly to solve problems in medicine and health. Their main purpose is to define levels of agreement on controversial subjects. Advocates suggest that, when properly employed, consensus strategies can create structured environments in which experts are given the best available information, allowing their solutions to problems to be more justifiable and credible than otherwise. This paper surveys the characteristics of several major methods (Delphi, Nominal Group, and models developed by the National Institutes of Health and Glaser) and provides guidelines for those who want to use the techniques. Among the concerns these guidelines address are selecting problems, choosing members for consensus panels, specifying acceptable levels of agreement, properly using empirical data, obtaining professional and political support, and disseminating results.     

An experiment shows that a well-designed report on costs and quality can help consumers choose high-value health care

Advocates of health reform continue to pursue policies and tools that will make information about comparative costs and resource use available to consumers. Reformers expect that consumers will use the data to choose high-value providers-those who offer higher quality and lower prices-and thus contribute to the broader goal of controlling national health care spending. However, communicating this information effectively is more challenging than it might first appear. For example, consumers are more interested in the quality of health care than in its cost, and many perceive a low-cost provider to be substandard. In this study of 1,421 employees, we examined how different presentations of information affect the likelihood that consumers will make high-value choices. We found that a substantial minority of the respondents shied away from low-cost providers, and even consumers who pay a larger share of their health care costs themselves were likely to equate high cost with high quality. At the same time, we found that presenting cost data alongside easy-to-interpret quality information and highlighting high-value options improved the likelihood that consumers would choose those options. Reporting strategies that follow such a format will help consumers understand that a doctor who provides higher-quality care than other doctors does not necessarily cost more.     

A five-point checklist to help performance reports incentivize improvement and effectively guide patients

Public reports of provider performance on measures of the quality, costs, and outcomes of health care can spur improvement and help patients find the best providers. However, the likelihood that these benefits will materialize depends on the methods underlying each performance report. This paper presents a five-point methodological checklist to guide those who want to improve their performance reporting methods. The central goal is to help report makers minimize the frequency and severity of provider performance misclassification and avoid adverse unintended consequences of reporting. We believe that if those who produce the reports publicly explain how they address each checklist item, this increased transparency will encourage more rigorous methods and improve the chances that reports will lead to better, more efficient care.     

The analysis of incomplete cost data due to dropout

Incomplete data due to premature withdrawal (dropout) constitute a serious problem in prospective economic evaluations that has received only little attention to date. The aim of this simulation study was to investigate how standard methods for dealing with incomplete data perform when applied to cost data with various distributions and various types of dropout. Selected methods included the product-limit estimator of Lin et al. the expectation maximisation (EM-) algorithm, several types of multiple imputation (MI) and various simple methods like complete case analysis and mean imputation. Almost all methods were unbiased in the case of dropout completely at random (DCAR), but only the product-limit estimator, the EM-algorithm and the MI approaches provided adequate estimates of the standard error (SE). The best estimates of the mean and SE for dropout at random (DAR) were provided by the bootstrap EM-algorithm, MI regression and MI Monte Carlo Markov chain. These methods were able to deal with skewed cost data in combination with DAR and only became biased when costs also included the costs of expensive events. None of the methods were able to deal adequately with informative dropout. In conclusion, the EM-algorithm with bootstrap, MI regression and MI MCMC are robust to the multivariate normal assumption and are the preferred methods for the analysis of incomplete cost data when the assumption of DCAR is not justified.     

Cost-accounting techniques for health care providers

The author reviews cost-accounting techniques and systems used by manufacturing companies. Some of the concepts and techniques used by for-profit companies can be implemented for health care institutions. Nurse executives can learn many lessons in product cost accounting from these for-profit companies. Understanding the various cost-accounting methodologies and techniques that are available can help nurse executives design, implement, and use a cost accounting system that will identify the costs associated with products and services provided. The author also reviews and explains standard costing systems. These systems can serve as valuable tools for budgeting, evaluating, and controlling departmental costs. When used in these instances, they can prove useful, and they furnish important information that is necessary for pricing products, determining alternatives or substitute services, and controlling costs.     

Increasing the relevance of research to health care managers: hospital CEO imperatives for improving quality and lowering costs

BACKGROUND: Evidence-based management assumes that available research evidence is consistent with the problems and decision-making conditions faced by those who will utilize this evidence in practice. PURPOSE: This article attempts to identify how hospital leaders view key determinants of hospital quality and costs, as well as the fundamental ways these leaders "think" about solutions to quality and cost issues in their organizations. The objective of this analysis is to better inform the research agenda and approaches pursued by health services research so that this research reflects the "realities" of practice in hospitals. METHODS: We conducted a series of semistructured interviews with a convenience sample of eight hospital and three health system leaders. Questions focused on current and future challenges facing hospitals as they relate to hospital quality, costs, and efficiency, and potential solutions to those challenges. FINDINGS: Nine major organizational and managerial factors emerged from the interviews, including staffing, evidence-based practice, information technology, data availability and benchmarking, and leadership. Hospital leaders tend to think about these factors systemically and consider process-related factors as the important drivers of cost and quality. PRACTICE IMPLICATIONS: The results suggest a need to expand the methods utilized by health services researchers to make their research more relevant to health care managers. Expanding research methods to reflect the systemic way that managers view the challenges and solutions facing their organizations may enhance the application of research findings into management practice. Finally, better communication is needed between the research and practice communities. Researchers must learn to think more like managers if their research is to be relevant, and managers must learn to more effectively communicate their issues with the research community and frame their problems in researchable terms.     

The dilemma of managing value

"Value" is an elusive term, but talk of how to manage value raises the specter of chaos. Value is defined as a working equation involving appropriateness of care, quality of outcomes and service, and cost. One hypothesis is that purchasers and providers will ultimately recognize that the value of health care services provided is critical because value, properly managed, directly addresses the volatile issue of containing health care costs. But to achieve success in managing value, providers must initiate new strategies and transform organizational and clinical priorities and practices. As the contest intensifies between society's infinite demand for health care services and finite resources, the need to truly subscribe to the management of value will become ever more important. Those providing care will learn to appreciate that long-term success will depend on much more than mere cost cutting.     

Assessing the Economic Value of Clinical Artificial Intelligence: Challenges and Opportunities

ObjectivesClinical artificial intelligence (AI) is a novel technology, and few economic evaluations have focused on it to date. Before its wider implementation, it is important to highlight the aspects of AI that challenge traditional health technology assessment methods.MethodsWe used an existing broad value framework to assess potential ways AI can provide good value for money. We also developed a rubric of how economic evaluations of AI should vary depending on the case of its use.ResultsWe found that the measurement of core elements of value—health outcomes and cost—are complicated by AI because its generalizability across different populations is often unclear and because its use may necessitate reconfigured clinical processes. Clinicians’ productivity may improve when AI is used. If poorly implemented though, AI may also cause clinicians’ workload to increase. Some AI has been found to exacerbate health disparities. Nevertheless, AI may promote equity by expanding access to medical care and, when properly trained, providing unbiased diagnoses and prognoses. The approach to assessment of AI should vary based on its use case: AI that creates new clinical possibilities can improve outcomes, but regulation and evidence collection may be difficult; AI that extends clinical expertise can reduce disparities and lower costs but may result in overuse; and AI that automates clinicians’ work can improve productivity but may reduce skills.ConclusionsThe potential uses of clinical AI create challenges for health technology assessment methods originally developed for pharmaceuticals and medical devices. Health economists should be prepared to examine data collection and methods used to train AI, as these may impact its future value.     

MM4 Reconciling Decision Models with the Real World: The Cost-Effectiveness of Erythropoietin for Esrd-Related Anemia

The choice of data used in decision modeling of health care interventions divides analysis into two groups: those who favor randomized clinical trial (RCT) data and those who prefer "real world" data. This decision may have serious consequences if the end result is to inform health care policy. This workshop employs a case study to (1) show how differences in the reality of clinical practice and the rigor of RCTs can lead to biases when decision models use RCT data to evaluate policy issues and (2) provide a method of updating decision models with claims/outcomes data to overcome this bias. We highlight three specific problems associated with the use of RCT data which may create misleading results: randomization and sample selection bias, clinically appropriate comparator groups, and indirect treatment effects. These issues are illustrated with a decision model analyzing Medicare's coverage of erythropoietin (EPO) for patients with End-Stage Renal Disease (ESRD). We show how logistic and multiple regression can be used to estimate branch probabilities and payoffs for each treatment group. The incorporation of additional data from the United States Renal Data System into the model enables us to update probabilities and payoffs when patients are not randomly assigned to treatment modalities. To highlight the potential bias that exists when models rely solely on RCT data, we compare our results to a previous study in which the authors employed a computerized decision model to estimate the net costs to Medicare of EPO coverage at 1 and 5 years. This exercise will offer policy analysts and others a method of updating RCT-based decision models to more accurately reflect clinical practice and predict policy effects.     

Propensity score and doubly robust methods for estimating the effect of treatment on censored cost

The estimation of treatment effects on medical costs is complicated by the need to account for informative censoring, skewness, and the effects of confounders. Because medical costs are often collected from observational claims data, we investigate propensity score (PS) methods such as covariate adjustment, stratification, and inverse probability weighting taking into account informative censoring of the cost outcome. We compare these more commonly used methods with doubly robust (DR) estimation. We then use a machine learning approach called super learner (SL) to choose among conventional cost models to estimate regression parameters in the DR approach and to choose among various model specifications for PS estimation. Our simulation studies show that when the PS model is correctly specified, weighting and DR perform well. When the PS model is misspecified, the combined approach of DR with SL can still provide unbiased estimates. SL is especially useful when the underlying cost distribution comes from a mixture of different distributions or when the true PS model is unknown. We apply these approaches to a cost analysis of two bladder cancer treatments, cystectomy versus bladder preservation therapy, using SEER‐Medicare data. Copyright © 2015 John Wiley & Sons, Ltd.     

Assessing and comparing costs: how robust are the bootstrap and methods based on asymptotic normality?

This article addresses and challenges some common perceptions in the statistical assessment of costs and cost-effectiveness in health economics. Cost data typically exhibit highly skew distributions. Two techniques whose validity does not depend on any specific form of underlying distribution are the bootstrap and methods based on asymptotic normality of sample means. These methods are generally thought to be appropriate for the analysis of cost data.We argue that, even when these methods are technically valid, they may often lead to inefficient and even misleading inferences. It is important to apply methods that recognise the skewness in cost data.We further demonstrate that it may also be important to incorporate relevant prior information in a Bayesian analysis.     

MM1 Monte Carlo Simulation in Health Care Models

In this workshop we will focus on Monte Carlo disease simulations and how they can be used to perform economic evaluations of health care interventions. Monce Carlo disease simulation is a modeling technique that operates on a patient level basis, explicitly estimating the effect of variability among patients in both underlying disease progression patterns and in individual responsiveness to treatments. Typical outputs from these simulations are patient functional status, life years, quality-adjusted life years, and associated costs, all of which can be appropriately discounted. The output information is presented in the form of distributions which can be used to estimate mean or median values and confidence intervals for the outcomes of interest. These results can be used to compute cost-effectiveness ratios and other drug value measures. Monte Carlo disease simulation also allows decision makers to address the question of risk associated with smaller populations that may not tend to the "average" results generated by Markov models or simulations of large populations. In this workshop, we describe how to create a Monte Carlo simulation model and how different types of uncertainly can be incorporated into the model. We will briefly compare and contrast Monte Carlo and Markov simulation techniques. Discussion topics will be illustrated and motivated by an HIV/AIDS model of the effect of combination antiretroviral therapy on viral load and CD4 progression. This workshop should be beneficial to outcomes researchers and health care decision makers who need to incorporate uncertainty about the natural history of a disease and the impact of alternative disease management strategies for individual patients into their drug value analyses.