Technological Innovation in Health Care and the Long-Term Fiscal Outlook

医疗保健技术创新和长期财政前景

• 批准号: 8750769
• 负责人: DANA P GOLDMAN
• 金额: $ 39.97万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-15 至 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8750769
• 关键词: Accounting; Age; Aging; American; Behavioral; Behavioral Model; Budgets; Caring; Complement; Disease; Economic Models; Economics; Elderly; Equilibrium; Evolution; Feedback; Future; Generations; Government; Growth; Health; Health Care Costs; Health Policy; Healthcare; Heart; Income; Insurance; Knowledge; Labor Forces; Life; Literature; Longevity; Maps; Medical; Medical Technology; Medicare; Medicare/Medicaid; Modeling; Outcome; Past Trends; Pharmacologic Substance; Policies; Population; Population Growth; Price; Production; Research; Role; Running; Scientific Advances and Accomplishments; Social Values; Social Welfare; Stream; Technology; Time; United States; United States Centers for Medicare and Medicaid Services; aged; aging population; base; cost; economic outcome; falls; functional status; innovation; models and simulation; mortality; population health; programs; public health relevance; simulation; technological innovation; trend

DESCRIPTION (provided by applicant): Over the past 50 years, longevity has risen at a steady pace. Functional status of the elderly has also witnessed important improvements, although gains have leveled off recently. These improvements do not come without costs. The United States has devoted an increasing share of its income to health care over that same period. In part because of the projected increase of health costs, the long-term fiscal outlook of the U.S. presents important challenges. Fiscal forecasts from the Congressional Budget Office and the Centers for Medicare & Medicaid Services (CMS) are dire, with Medicare spending alone projected to more than double as a share of national income, from 3.7% today to 7.3% in 2050. However, these forecasts take the driver of health care costs-technology-as exogenous, or fixed. This is unrealistic. We know innovators maximize the expected value of a future profit stream, and they will take into account the demand for their innovations in the future. The proposed research will marry two distinct but complementary approaches from the health policy and macroeconomic fields. First, we will incorporate technological change, and its determinants, into existing models projecting the long- term fiscal outlook of the U.S. Our engine will be an economic-demographic microsimulation that covers the population aged 25 and older, the Future Americans Model (FAM). This approach will examine granular technological change to understand consequences for spending and population health. Second, we will build a macroeconomic growth model to understand endogenous innovation and health care spending in overlapping generations (OLG) that incorporates the projections of our economic-demographic simulation. Such OLG models have proven very useful for understanding long-run economic outcomes and their effects on government debt. Results from the OLG model will complement the results of the FAM by revealing the fiscal paths consistent with a balanced government budget when taking behavioral and macroeconomic changes into account. There are four principal outcomes of this project. First, we will quantify the fiscal consequences of various innovation scenarios. Second, the research will identify scenarios under which new innovations will impact health by effectively treating one disease while simultaneously impacting innovation for other diseases. Third, the project will identify scenarios under which health spending would rise or fall in future years as a result of technological change and how health spending can be financed with minimum welfare consequences using the FAM-calibrated OLG model. Fourth, by combining behavioral models with simulation models such as the Future Americans Model, this project will advance the state of knowledge on the possibilities of combining different approaches to make long-term fiscal forecasts.

描述（由申请人提供）：在过去的50年里，寿命稳步增长。老年人的功能状况也有了重大改善，尽管最近进展趋于平稳。这些改进并非没有代价。在同一时期，美国将越来越多的收入用于保健。部分由于预计医疗费用将增加，美国的长期财政前景面临重大挑战。来自国会预算办公室和医疗保险和医疗补助服务中心（CMS）的财政预测是可怕的，仅医疗保险支出就预计将占国民收入的两倍以上，从今天的3.7%增加到2050年的7.3%。然而，这些预测将医疗保健成本的驱动因素-技术-视为外生的或固定的。这是不现实的。我们知道，创新者会最大化未来利润流的预期价值，他们会考虑未来对其创新的需求。拟议的研究将结合卫生政策和宏观经济领域两种不同但互补的方法。首先，我们将把技术变革及其决定因素纳入预测美国长期财政前景的现有模型中。我们的引擎将是一个涵盖25岁及以上人口的经济人口微观模拟，即未来美国人模型（FAM）。这种方法将检查颗粒技术变化，以了解支出和人口健康的后果。其次，我们将建立一个宏观经济增长模型，以了解内生创新和医疗保健支出在重叠的世代（OLG），结合我们的经济人口模拟的预测。事实证明，这种OLG模型对于理解长期经济结果及其对政府债务的影响非常有用。OLG模型的结果将补充FAM的结果，揭示在考虑行为和宏观经济变化时与平衡政府预算相一致的财政路径。该项目有四个主要成果。首先，我们将量化各种创新情景的财政后果。其次，研究将确定新的创新将通过有效治疗一种疾病同时影响其他疾病的创新来影响健康的情景。第三，该项目将确定未来几年由于技术变革而导致卫生支出上升或下降的情景，以及如何使用FAM校准的OLG模型在最小福利后果的情况下为卫生支出提供资金。第四，通过将行为模型与“未来美国人模型”等模拟模型相结合，提高对综合运用各种方法进行长期财政预测的可能性的认识。