Forum for Health Economics and Policy最新文献

{"title":"The Crowd, the Cloud and Improving the Future of Medical Device Innovation","authors":"Marco D. Huesch, R. Szczerba","doi":"10.1515/fhep-2012-0023","DOIUrl":"https://doi.org/10.1515/fhep-2012-0023","url":null,"abstract":"Abstract Barriers and delays to medical device innovation are often solely attributable to the regulatory environment instead of both the current state of innovation practices and product development processes in the industry. Increasing the pace of innovation while reducing costs requires the creation of a new approach that fits both established medical device corporations as well as entrepreneurial start-ups. In this commentary we advance the concept of innovation platforms to facilitate ideation in the medical device space. Such platforms could also allow the full health benefits from individual medical devices to be reaped, by overcoming interoperability concerns through simulation and credentialing. Given the dramatic benefits of medical device success, such non-traditional business models for development may be potential solutions for industry, users and regulators.","PeriodicalId":38039,"journal":{"name":"Forum for Health Economics and Policy","volume":"75 1","pages":"13 - 20"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89012401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medicare Reimbursement Reform for Provider Visits and Health Outcomes in Patients on Hemodialysis.","authors":"Kevin F Erickson,&nbsp;Wolfgang C Winkelmayer,&nbsp;Glenn M Chertow,&nbsp;Jay Bhattacharya","doi":"10.1515/fhep-2012-0018","DOIUrl":"https://doi.org/10.1515/fhep-2012-0018","url":null,"abstract":"<p><p>The relation between the quantity of many healthcare services delivered and health outcomes is uncertain. In January 2004, the Centers for Medicare and Medicaid Services introduced a tiered fee-for-service system for patients on hemodialysis, creating an incentive for providers to see patients more frequently. We analyzed the effect of this change on patient mortality, transplant wait-listing, and costs. While mortality rates for Medicare beneficiaries on hemodialysis declined after reimbursement reform, mortality declined more - or was no different - among patients whose providers were not affected by the economic incentive. Similarly, improved placement of patients on the kidney transplant waitlist was no different among patients whose providers were not affected by the economic incentive; payments for dialysis visits increased 13.7% in the year following reform. The payment system designed to increase provider visits to hemodialysis patients increased Medicare costs with no evidence of a benefit on survival or kidney transplant listing.</p>","PeriodicalId":38039,"journal":{"name":"Forum for Health Economics and Policy","volume":"17 1","pages":"53-77"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/fhep-2012-0018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34293533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Cost-Benefit Analysis of Using Evidence of Effectiveness in Terms of Progression Free Survival in Making Reimbursement Decisions on New Cancer Therapies","authors":"Warren Stevens, T. Philipson, Yanyu Wu, Connie Chen, D. Lakdawalla","doi":"10.1515/fhep-2013-0025","DOIUrl":"https://doi.org/10.1515/fhep-2013-0025","url":null,"abstract":"Abstract Payers increasingly require evidence of a statistically significant difference in overall survival (OS) for reimbursement of new cancer therapies. At the same time, it becomes increasingly costly to design clinical trials that measure OS endpoints instead of progression-free survival (PFS) endpoints. While PFS is often an imperfect proxy for OS effects, it is also faster and cheaper to measure accurately. This study develops a general cost-benefit framework that quantifies the competing trade-offs of the use of PFS versus that of OS in oncology reimbursement. We then apply this general framework to the illustrative case of metastatic renal cell carcinoma (mRCC). In the particular case of mRCC, the framework demonstrates that the net benefit to society from basing reimbursement decisions on PFS endpoints could be between $271 and $1271 million in the United States, or between €171 and €1128 million in Europe. In longevity terms, waiting for OS data in this case would result in a net loss of 3549–14,557 life-years among US patients, or 6785–27,993 life-years for European patients. While more stringent standards for medical evidence improve accuracy, they also impose countervailing costs on patients in terms of foregone health gains. These costs must be weighed against the benefits of greater accuracy. The magnitudes of the costs and benefits may vary across tumor types and need to be quantified systematically.","PeriodicalId":38039,"journal":{"name":"Forum for Health Economics and Policy","volume":"4 1","pages":"21 - 52"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75709695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Opportunities in the Economics of Personalized Health Care and Prevention","authors":"D. Meltzer","doi":"10.1515/fhep-2013-0012","DOIUrl":"https://doi.org/10.1515/fhep-2013-0012","url":null,"abstract":"Abstract Personalized medicine is best viewed from a broad perspective of trying to use information about a patient to improve care. While “personalized medicine” often emphasizes the value of genetic information, traditional clinical approaches to personalizing care based on patient phenotype, provider and system-level factors should not be neglected. As these diverse approaches to personalization are examined, tools such as cost-effectiveness analysis can provide important insights into the value of these approaches, strategies for their implementation and dissemination, and priorities for future research. Such analyses are likely to be most insightful if they recognize that patient and provider behaviors are essential determinants of the value of treatments and that patient factors in particular may have large effects on the value of treatments and the need for interventions to improve decision making. These comments suggest three major areas of opportunity for economic analyses of personalized medicine: (1) traditional clinical approaches to personalized medicine, (2) multi-perspective studies of the benefits and costs of personalized medicine, and (3) the role of behavior in the value of personalized medicine.","PeriodicalId":38039,"journal":{"name":"Forum for Health Economics and Policy","volume":"22 1","pages":"S13 - S22"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83254126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Value of Diagnostic Testing in Personalized Medicine","authors":"D. Goldman, Charu N. Gupta, E. Vasudeva, K. Trakas, R. Riley, D. Lakdawalla, D. Agus, N. Sood, A. Jena, T. Philipson","doi":"10.1515/fhep-2013-0023","DOIUrl":"https://doi.org/10.1515/fhep-2013-0023","url":null,"abstract":"Abstract Personalized medicine – the targeting of therapies to individuals on the basis of their biological, clinical, or genetic characteristics – is thought to have the potential to transform health care. While much emphasis has been placed on the value of personalized therapies, less attention has been paid to the value generated by the diagnostic tests that direct patients to those targeted treatments. This paper presents a framework derived from information economics for assessing the value of diagnostics. We demonstrate, via a case study, that the social value of such diagnostics can be very large, both by avoiding unnecessary treatment and by identifying patients who otherwise would not get treated. Despite the potential social benefits, diagnostic development has been discouraged by cost-based, rather than value-based, reimbursement.","PeriodicalId":38039,"journal":{"name":"Forum for Health Economics and Policy","volume":"16 1","pages":"S87 - S99"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88608162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized Medicine in the Context of Comparative Effectiveness Research","authors":"A. Basu","doi":"10.1515/fhep-2013-0009","DOIUrl":"https://doi.org/10.1515/fhep-2013-0009","url":null,"abstract":"Abstract The world of patient-centered outcomes research (PCOR) seems to bridge the previously disjointed worlds of comparative effectiveness research (CER) and personalized medicine (PM). Indeed, theoretical reasoning on how information on medical quality should inform decision making, both at the individual and the policy level, reveals that personalized information on the value of medical products is critical for improving decision making at all levels. However, challenges to generating, evaluating and translating evidence that might lead to personalization need to be critically assessed. In this paper, I discuss two different concepts of personalized medicine – passive personalization (PPM) and active personalization (APM) that are important to distinguish in order to invest efficiently in PCOR and develop objective evidence on the value of personalization that will aid in its translation. APM constitutes the process of actively seeking identifiers, which can be genotypical, phenotypical or even environmental, that can be used to differentiate between the marginal benefits of treatment across patients. In contrast, PPM involves a passive approach to personalization where, in the absence of explicit research to discover identifiers, patients and physicians “learn by doing” mostly due to the repeated use of similar products on similar patients. Benchmarking the current state of PPM sets the bar to which the expected value of any new APM agenda should be evaluated. Exploring processes that enable PPM in practice can help discover new APM agendas, such as those based on developing predictive algorithms based on clinical, phenotypical and preference data, which may be more efficient that trying to develop expensive genetic tests. It can also identify scenarios or subgroups of patients where genomic research would be most valuable since alternative prediction algorithms were difficult to develop in those settings. Two clinical scenarios are discussed where PPM was explored through novel econometric methods. Related discussions around exploring PPM processes, multi-dimensionality of outcomes, and a balanced agenda for future research on personalization follow.","PeriodicalId":38039,"journal":{"name":"Forum for Health Economics and Policy","volume":"85 1","pages":"S73 - S86"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78940140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Economics of Personalization in Prevention and Public Health","authors":"D. Kenkel, Hua Wang","doi":"10.1515/fhep-2013-0011","DOIUrl":"https://doi.org/10.1515/fhep-2013-0011","url":null,"abstract":"Abstract Personalized prevention uses family history and predictive genetic testing to identify people at high risk of serious diseases. The availability of predictive genetic tests is a newer and still-developing phenomenon. Many observers see tremendous potential for personalized prevention to improve public health. At the same time, the emergence of these new markets raises familiar health policy concerns about costs, cost-effectiveness, and health disparities. This paper first discusses an economic framework for the analysis of personalized prevention. On the demand side, consumers use personalized prevention as a form of information that allows them to make better choices about prevention, including medical care and health behaviors like diet and exercise. On the supply side, an interplay of complex market forces and regulations will determine the prices, advertising, and insurance coverage of predictive genetic tests. Beyond the question of whether health insurance will cover the costs of predictive genetic tests, there is a great deal of concern about whether consumers’ use of genetic tests might place them at risk of genetic discrimination or might lead to adverse selection. The paper also reports descriptive analysis of data from the 2000, 2005, and 2010 National Health Interview Surveys on the use of predictive genetic tests. The empirical analysis documents large socioeconomic status-related disparities in consumers having heard of genetic tests: for example, consumers with less schooling, Blacks, and Hispanics were substantially less likely to have heard of genetic tests. Evidence from other empirical studies provides little evidence that genetic testing leads to genetic discrimination in insurance markets. There is more evidence suggesting adverse selection, where genetic testing leads consumers to purchase long-term care insurance. The paper concludes with some preliminary thoughts about important directions for future research. The goal of the paper is to review relevant research to help develop an economic approach and social science research agenda into the determinants and consequences of genetic tests for prevention.","PeriodicalId":38039,"journal":{"name":"Forum for Health Economics and Policy","volume":"75 1","pages":"S53 - S71"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80107589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Economics of Personalized Health Care and Prevention: Introduction","authors":"Gregory Bloss, J. Haaga","doi":"10.1515/fhep-2013-0018","DOIUrl":"https://doi.org/10.1515/fhep-2013-0018","url":null,"abstract":"","PeriodicalId":38039,"journal":{"name":"Forum for Health Economics and Policy","volume":"90 1","pages":"S1 - S11"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78543040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Economic Perspectives on Personalized Health Care and Prevention","authors":"K. Phillips, J. Sakowski, S. Liang, N. Ponce","doi":"10.1515/fhep-2013-0010","DOIUrl":"https://doi.org/10.1515/fhep-2013-0010","url":null,"abstract":"Abstract The objective of this paper is to provide an overview of economic evaluation of personalized medicine, focusing particularly on the use of cost-effectiveness analysis and other methods of valuation. We draw on insights from the literature and our work at the University of California, San Francisco Center for Translational and Policy Research on Personalized Medicine (TRANSPERS). We begin with a discussion of why personalized medicine is of interest and challenges to adoption, whether personalized medicine is different enough to require different evaluation approaches, and what is known about the economics of personalized medicine. We then discuss insights from TRANSPERS research and six areas for future research: Develop and Apply Multiple Methods of Assessing Value Identify Key Factors in Determining the Value of Personalized Medicine Use Real World Perspectives in Economic Analyses Consider Patient Heterogeneity and Diverse Populations in Economic Analyses Prepare for Upcoming Challenges of Assessing Value of Emerging Technologies Incorporate Behavioral Economics into Value Assessments","PeriodicalId":38039,"journal":{"name":"Forum for Health Economics and Policy","volume":"23 1","pages":"S23 - S52"},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86052142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Better Quality of Care or Healthier Patients? Hospital Utilization by Medicare Advantage and Fee-for-Service Enrollees.","authors":"Lauren Hersch Nicholas","doi":"10.1515/fhep-2012-0037","DOIUrl":"10.1515/fhep-2012-0037","url":null,"abstract":"<p><p>Do differences in rates of use among managed care and Fee-for-Service Medicare beneficiaries reflect selection bias or successful care management by insurers? I demonstrate a new method to estimate the treatment effect of insurance status on health care utilization. Using clinical information and risk-adjustment techniques on data on acute admission that are unrelated to recent medical care, I create a proxy measure of unobserved health status. I find that positive selection accounts for between one-quarter and one-third of the risk-adjusted differences in rates of hospitalization for ambulatory care sensitive conditions and elective procedures among Medicare managed care and Fee-for-Service enrollees in 7 years of Healthcare Cost and Utilization Project State Inpatient Databases from Arizona, Florida, New Jersey and New York matched to Medicare enrollment data. Beyond selection effects, I find that managed care plans reduce rates of potentially preventable hospitalizations by 12.5 per 1,000 enrollees (compared to mean of 46 per 1,000) and reduce annual rates of elective admissions by 4 per 1,000 enrollees (mean 18.6 per 1,000).</p>","PeriodicalId":38039,"journal":{"name":"Forum for Health Economics and Policy","volume":"16 1","pages":"137-161"},"PeriodicalIF":0.0,"publicationDate":"2013-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/fhep-2012-0037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32120735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}