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

描述(申请人提供)：在过去的50年里，长寿一直在稳步增长。老年人的功能状况也有了重要的改善，尽管最近有所平缓。这些改进并不是没有代价的。在同一时期，美国将越来越多的收入用于医疗保健。在一定程度上，由于预计医疗成本会增加，美国的长期财政前景面临着重大挑战。国会预算办公室和医疗保险和医疗补助服务中心(CMS)的财政预测非常糟糕，仅医疗保险支出占国民收入的比例预计就将增加一倍以上，从目前的3.7%上升到2050年的7.3%。然而，这些预测认为医疗成本的驱动因素--技术--是外生的，或者是固定的。这是不现实的。我们知道，创新者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。拟议的研究将结合来自卫生政策和宏观经济领域的两种截然不同但相互补充的方法。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。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)。这种方法将考察细粒度的技术变化，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，以了解重叠世代(OLG)的内生创新和医疗保健支出，该模型纳入了我们的经济-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，在考虑到行为和宏观经济变化时，揭示与平衡政府预算一致的财政路径。该项目的主要成果有四个。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医疗支出将因技术变革而上升或下降的情景，以及如何使用FAM校准的OLG模型在福利后果最小的情况下为医疗支出融资。第四，通过将行为模型与模拟模型(如未来美国人模型)相结合，该项目将提高对结合不同方法做出长期财政预测的可能性的了解。

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.