DRG-Based Cost Minimization Models: Applications in a Hospital Environment

The primary objective of this article is to investigate the feasibility of the application of cost minimization analysis in a teaching hospital environment. The investigation is concerned with the development of cost per admission and cost per patient day models. These models are further used for determining the value of the length of stay that would minimize cost per patient day (projected length of stay) and for estimating the costs. This study is based on total of 94,500 observations (1999 and 2000), obtained from a teaching hospital in South Florida. The top ten Diagnosis Related Groups (DRGs) with the highest volume are selected and classified into four insurance categories: Medicaid, Medicare, commercial, and self-pay. The cost models are fitted to the data for an average R2 value of 79%, and a MAPE value of 15%. The result demonstrates that if a hospital can control the length of stay at the projected level, on average, the cost per admission and the cost per patient day will decrease. Based on 6,367 admissions for the selected DRGs in 2000, the total cost per year and the cost per patient day decreased by approximately 11.58 and 10.35%, respectively. Overall, these results confirm that the concept of cost minimization analysis in economic theory can be applied to healthcare industries for the purpose of reducing of costs. In addition, this research offers a decision support instrument for healthcare administrators.     

时间驱动作业成本法在医院中的应用

时间驱动作业成本法在传统作业成本法的基础上，将时间因素作为成本分配的驱动因素，从而简化了成本核算过程，同时其提供的时间信息又可以将时间管理纳入到成本管理体系中去。     

Efficient allocation of resources to prevent HIV infection among injection drug users: the Prevention Point Philadelphia (PPP) needle exchange program

The objective of this study is to determine the allocation of resources within a multi-site needle exchange program (NEP) that achieves the largest possible reduction in new HIV infections at minimum cost. We present a model that relates the number of injection drug user (IDU) clients and the number of syringes exchanged per client to both the costs of the NEP and the expected reduction in HIV infections per unit time. We show that cost-effective allocation within a multi-site NEP requires that sites be located where the density of IDUs is highest, and that the number of syringes exchanged per client be equal across sites. We apply these optimal allocation rules to a specific multi-site needle exchange program, Prevention Point Philadelphia (PPP). This NEP, we find, needs to add 2 or 3 new sites in neighborhoods with the highest density of IDU AIDS cases, and to increase its total IDU client base by about 28%, from approximately 6400 to 8200 IDU clients. The case-study NEP also needs to increase its hours of operation at two existing sites, where the number of needles distributed per client is currently sub-optimal, by 50%. At the optimal allocation, the estimated cost per case of HIV averted would be dollar 2800 (range dollar 2300-dollar 4200). Such a favorable cost-effectiveness ratio derives primarily from PPP's low marginal costs per distributed needle.     

Analysing malaria drug trials on a per‐individual or per‐clone basis: a comparison of methods

There are a variety of methods used to estimate the effectiveness of antimalarial drugs in clinical trials, invariably on a per‐person basis. A person, however, may have more than one malaria infection present at the time of treatment. We evaluate currently used methods for analysing malaria trials on a per‐individual basis and introduce a novel method to estimate the cure rate on a per‐infection (clone) basis. We used simulated and real data to highlight the differences of the various methods. We give special attention to classifying outcomes as cured, recrudescent (infections that never fully cleared) or ambiguous on the basis of genetic markers at three loci. To estimate cure rates on a per‐clone basis, we used the genetic information within an individual before treatment to determine the number of clones present. We used the genetic information obtained at the time of treatment failure to classify clones as recrudescence or new infections. On the per‐individual level, we find that the most accurate methods of classification label an individual as newly infected if all alleles are different at the beginning and at the time of failure and as a recrudescence if all or some alleles were the same. The most appropriate analysis method is survival analysis or alternatively for complete data/per‐protocol analysis a proportion estimate that treats new infections as successes. We show that the analysis of drug effectiveness on a per‐clone basis estimates the cure rate accurately and allows more detailed evaluation of the performance of the treatment. Copyright © 2012 John Wiley & Sons, Ltd.     

Cost‐effectiveness Analysis and Insurance Coverage: Solving a Puzzle

The conventional model for the use of cost‐effectiveness analysis for health programs involves determining whether the cost per unit of effectiveness of the program is lower than some socially determined maximum acceptable cost per unit of effectiveness. If a program is better by this criterion, the policy implication is that it should be implemented by full coverage of its cost by insurance; if not, the program should not be implemented. This paper examines the unanswered question of how cost‐effectiveness analysis should be performed and interpreted when insurance coverage may involve cost sharing. It explores the question of how cost sharing should be related to the magnitude of a cost‐effectiveness ratio. A common view that cost sharing should vary inversely with program cost‐effectiveness is shown to be incorrect. A key issue in correct analysis is whether there is heterogeneity in marginal effectiveness of care that cannot be perceived by the social planner but is known by the demander. It is possible that some programs that would fail the social efficiency test at full coverage will be acceptable with positive cost sharing. Combining individual and social preferences affects both the choice of programs and the extent of cost sharing. Copyright © 2014 John Wiley & Sons, Ltd.     

Cost-Effectiveness of Baseline Low-Dose Computed Tomography Screening for Lung Cancer: The Israeli Experience

ObjectiveReduced mortality with low-dose computed tomography (LDCT) lung cancer screening was demonstrated in a large randomized controlled study of high-risk individuals. Cost-effectiveness must be assessed before routine LDCT screening is considered. We aimed to evaluate the cost-effectiveness of LDCT lung cancer screening in Israel.MethodsA decision analytic framework was used to evaluate the decision to screen or not screen from the health system perspective. The screening arm included 842 moderate-to-heavy smokers aged 45 years or older, screened at Hadassah-Hebrew University Medical Center from 1998 to 2004. In the usual-care arm, stage distribution and stage-specific life expectancy were obtained from the Israel National Cancer Registry data for 1994 to 2006. Lifetime stage-specific costs were estimated from medical records of patients diagnosed and treated at Hadassah Medical Center in the period 2003 to 2004. The analysis considered possible biases—lead time, overdiagnosis, and self-selection. Cost per quality-adjusted-life-year (QALY) gained by screening was estimated.ResultsBase-case incremental cost per QALY gained was $1464 (2011 prices). Extensive sensitivity analysis affirmed the low cost per QALY gained. The cost per QALY gained is lower than $10,000 with probability 0.937 and is lower than $20,000 with probability 0.978.ConclusionsOur analysis suggests that baseline LDCT lung cancer screening in Israel presents a good value for the money and should be considered for inclusion in the National List of Health Services financed publicly.     

论“医疗保健价格指数”与“看病贵”

无论是居民感受还是学者研究，多年来我国持续存在看病贵这一社会热点问题。但现行的医疗保健价格指数披露出相反的结论。本文提出用门诊病人次均医药费、出院病人人均医药费指标，来计算医疗保健类价格指数，以更科学、准确地反映患者医药费用负担水平及其变动趋势。     

病例组合指数法在医院住院成本分析中的应用

目的：探讨住院病人成本的影响因素，并对其进行定量测量。方法：近照国际疾病ICD－9分类编码，采用病例组合指数法医院产出组成进行测量；彩和STAT，SPSS软件用最小二乘法，龄回归方法分别建立人均住院成本的回归模型并进行比较；对人均住院成本影响因素做相关分析；采用岭回归方法建立了引入病例组合指数等6个变量的人均住院成本的多元回归计量模型，结果：由于引入病例组合指数，考虑到了医院产出组成对人均住院成本的影响，使该成本分析更趋于合理；采用岭回归方法建立的回归模型相对于最小二乘法建立的回归模型而言，改善了自变量间多元共线性的效应，降低了回归系数的标准误，回归模型结果更稳定。结果：定量地测量了病例组合指数因素对人均住院成本的影响。     

2015—2018年某医院住院死亡病人死因及费用分析

目的了解2015—2018年某医院住院病人的死亡原因及死亡病人人均费用的变化,以加强疾病的防治,降低住院病人病死率。方法根据ICD-10国际疾病分类原则对某院近4年的住院病案首页进行疾病分类,对住院病人的前10位死因进行顺位,分析前10位死亡病人人均费用的变化。结果近4年中收治病人数增加3.64%,病死率上升0.09个百分点,住院死亡病人人均费用增加11.78%;男性死亡人数明显高于女性死亡人数,60岁以上年龄组病人死亡人数最多。死因顺位中,循环系统疾病居第1位,恶性肿瘤疾病居第2位,呼吸系统疾病及损伤和中毒依次排在第3和第4位。结论加强循环系统疾病、恶性肿瘤的防治,降低住院病人的病死率。     

The impact of NHS expenditure on health outcomes in England: Alternative approaches to identification in all‐cause and disease specific models of mortality

Several recent studies have estimated the responsiveness of mortality to English National Health Service spending. Although broadly similar, the studies differ in how they identify the outcome equation. One approach uses conventional socio‐economic variables as instruments for endogenous health care expenditure, whereas the other exploits exogenous elements in the resource allocation formula for local budgets. The former approach has usually been applied to specific disease areas (e.g., for cancer and circulatory disease), whereas the other has only been applied to all‐cause mortality. In this letter, we compare the two approaches by using them to estimate the direct all‐cause elasticity as well as disease‐specific elasticities. We also calculate the implied all‐cause elasticity associated with the disease‐specific results. We find that the “funding rule” approach to identification can be successfully replicated and applied to disease area models. This is important because disease area models reduce the danger of aggregation bias present in all‐cause analysis, and they offer the opportunity to link estimated mortality effects to more complete measures of health outcome that reflect what is currently known about the survival and morbidity disease burden in different programmes.     

Economic evaluation of border closure for a generic severe pandemic threat using New Zealand Treasury methods

Objective: To perform a comprehensive economic evaluation of border closure for an island nation in the face of severe pandemic scenarios. Methods: The costing tool developed by the New Zealand (NZ) Treasury (CBAx) was used for the analyses. Pandemic scenarios were as per previous work; 1 epidemiological data were from past New Zealand influenza pandemics. Results: The net present value of successful border closure was NZ$7.86 billion for Scenario A (half the mortality rate of the 1918 influenza pandemic) and $144 billion for preventing a more severe pandemic (10 times the mortality of scenario A). Cost–utility analyses found border closure was relatively cost‐effective, at $14,400 per QALY gained in Scenario A, and cost‐saving for Scenario B (taking the societal perspective). Conclusions: This work quantifies the economic benefits and costs from border closure for New Zealand under specific assumptions in a generic but severe pandemic threat (e.g. influenza, synthetic bioweapon). Preparing for such a pandemic response seems wise for an island nation, although successful border closure may only be feasible if planned well ahead. Implications for public health: Policy makers responsible for generic pandemic planning should explore how border closure could be implemented, including practical and legal frameworks.     

Why cost-effectiveness should trump (clinical) effectiveness: the ethical economics of the South West quadrant

In many health decision making situations there is a requirement that the effectiveness of interventions, usually their 'clinical' effectiveness, be established, as well as their cost-effectiveness. Often indeed this is effectively a prior requirement for their cost-effectiveness being investigated. If, however, one accepts the ethical argument for using a threshold incremental cost-effectiveness ratio (ICER) for interventions that are more effective but more costly (i.e. fall in the NE quadrant of the cost-effectiveness plane), one should apply the same decision rule in the SW quadrant, where the intervention is less effective but less costly. This implication is present in most standard treatments of cost-effectiveness analysis, including recent stochastic versions, and had gone relatively unquestioned within the discipline until the recent suggestion that the ICER threshold might be 'kinked'. A kinked threshold would, O'Brien et al. argue, better reflect the asymmetrical individual preferences found in empirical studies of consumer's willingness to pay and willingness to accept and justify different decision rules in the NE and SW quadrants. We reject the validity of such asymmetric preferences in the context of public health care decisions and consider and counter the two main 'ethical' objections that probably underlie the asymmetry in this case--the objection to 'taking away' and the objection to being required to undergo treatment that is less effective than no treatment at all.     

Valuing Reductions in Fatal Illness Risks: Implications of Recent Research

The value of mortality risk reductions, conventionally expressed as the value per statistical life, is an important determinant of the net benefits of many government policies. US regulators currently rely primarily on studies of fatal injuries, raising questions about whether different values might be appropriate for risks associated with fatal illnesses. Our review suggests that, despite the substantial expansion of the research base in recent years, few US studies of illness‐related risks meet criteria for quality, and those that do yield similar values to studies of injury‐related risks. Given this result, combining the findings of these few studies with the findings of the more robust literature on injury‐related risks appears to provide a reasonable range of estimates for application in regulatory analysis. Our review yields estimates ranging from about $4.2 million to $13.7 million with a mid‐point of $9.0 million (2013 dollars). Although the studies we identify differ from those that underlie the values currently used by Federal agencies, the resulting estimates are remarkably similar, suggesting that there is substantial consensus emerging on the values applicable to the general US population. Copyright © 2015 John Wiley & Sons, Ltd.     

Longevity bias in cost-effectiveness analysis

We use a simple lifetime utility maximization model to study the problem of medical resource allocation. This model leads to a welfare specification with a QALY (quality-adjusted life-year) component that captures an individual's preferences over both life expectancy and health status. The goal of medical cost-effectiveness analysis (CEA) is characterized as maximizing the QALY measure for a given total medical expenditure. We show that the CEA with such a goal has a longevity bias: the CEA-based division of a given total medical expenditure between extending life and improving health gives the former a larger share than is called for by welfare maximization.     

浦东新区医疗资源配置效率探究

用数据包络分析方法,选取卫生技术人员数、医疗房屋建筑面积、新区财政卫生事业经费和平均每诊疗人次费用为投入,病床使用率和年门诊总人次为产出,对浦东的医疗资源配置效率进行分析,并用投影方法对无效的原因进行探索,认为提高病床使用率和降低患者的平均每诊疗人次费用是提高医疗资源配置效率的主要途径。     

Cost-effectiveness acceptability curves--facts, fallacies and frequently asked questions

Cost-effectiveness acceptability curves (CEACs) have been widely adopted as a method to quantify and graphically represent uncertainty in economic evaluation studies of health-care technologies. However, there remain some common fallacies regarding the nature and shape of CEACs that largely result from the 'textbook' illustration of the CEAC. This 'textbook' CEAC shows a smooth curve starting at probability 0, with an asymptote to 1 for higher money values of the health outcome (lambda). But this familiar 'ogive' shape which makes the 'textbook' CEAC look like a cumulative distribution function is just one special case of the CEAC. The reality is that the CEAC can take many shapes and turns because it is a graphic transformation from the cost-effectiveness plane, where the joint density of incremental costs and effects may 'straddle' quadrants with attendant discontinuities and asymptotes. In fact CEACs: (i) do not have to cut the y-axis at 0; (ii) do not have to asymptote to 1; (iii) are not always monotonically increasing in lambda; and (iv) do not represent cumulative distribution functions (cdfs). Within this paper we present a 'gallery' of CEACs in order to identify the fallacies and illustrate the facts surrounding the CEAC. The aim of the paper is to serve as a reference tool to accompany the increased use of CEACs within major medical journals.     

Bayesian cost-effectiveness analysis with two measures of effectiveness: the cost-effectiveness acceptability plane

Cost-effectiveness analysis (CEA) compares the costs and outcomes of two or more technologies. However, there is no consensus about which measure of effectiveness should be used in each analysis. Clinical researchers have to select an appropriate outcome for their purpose, and this choice can have dramatic consequences on the conclusions of their analysis. In this paper we present a Bayesian cost-effectiveness framework to carry out CEA when more than one measure is considered. In particular, we analyse the case in which two measures of effectiveness, one binary and the other continuous, are considered. Decision-making measures, such as the incremental cost-effectiveness ratio, incremental net-benefit and cost-effectiveness acceptability curves, are used to compare costs and one measure of outcome. We propose an extension of cost-acceptability curves, namely the cost-effectiveness acceptability plane, as a suitable measure for decision taking. The models were validated using data from two clinical trials. In the first one, we compared four highly active antiretroviral treatments applied to asymptomatic HIV patients. As measures of effectiveness, we considered the percentage of patients with undetectable levels of viral load, and changes in quality of life, measured according to EuroQol. In the second clinical trial we compared three methadone maintenance programmes for opioid-addicted patients. In this case, the measures of effectiveness considered were quality of life, according to the Nottingham Health Profile, and adherence to the treatment, measured as the percentage of patients who participated in the whole treatment programme.