X-INEFFICIENCY IN US HOSPITALS

拟议研究的具体目标是：(1)研究 美国医院X-低效的相关性和(2)验证 随机前沿分析(SFA)的低效估计X- 低效率是最优性能和实际性能之间的差异 性能。X-低效可能占高达18%的 美国医院的费用。对过度支出的担忧 引起了人们长期以来对了解 美国医疗保健组织的效率低下。大多数研究都是 试图研究市场力量的影响或 关于医院效率的规定使用了成本作为间接的 效率的衡量标准。隐含的假设是，如果成本 都在减少，效率必须提高。这一假设可能不是 对，是这样。除了提高效率外，还可以控制成本 通过减少服务或将产品组合从更昂贵改为更低 昂贵的产出。因此，一项直接衡量医院状况的检查 效率很重要，因为它减少了对脆弱的依赖 假设，从而澄清环境因素对 这是医院绩效的一个重要方面。 拟议的研究将应用SFA来开发低效措施。 在美国的医院。低效的稳定性和有效性 估算值将通过双变量分析进行评估。估计 无效率指数将作为多变量中的因变量 分析与低效相关的因素。独立的 变量将被用来检验关于经济衰退影响的假设 医院效率低下的后续问题：教学任务、所有权状况、 监管压力、竞争压力、医疗保健组织普及率，以及 无偿护理1997年1,700多个大都市的横截面 医院将被分析。主要数据源包括HCFA、AHA和 ARF(区域资源文件)。

The specific goals of the proposed research are to: (1) examine the correlates of X-inefficiency in U.S. hospitals and (2) validate inefficiency estimates from Stochastic Frontier Analysis (SFA) X- inefficiency is the difference between optimal performance and actual performance. X-inefficiency may account for as much as 18 percent of the expenses in U.S. hospitals. Concerns about excessive expenditures have created a long standing interest in understanding the causes of inefficiency in U.S. health care organizations. Most of the studies that have attempted to examine the impact of market forces or regulations on hospital efficiency have used costs as an indirect measure of efficiency. The implicit assumption has been that if costs are decreasing, efficiency must increasing. This assumption may not be correct. In addition to increasing efficiency, costs can be contained by reducing services or changing product mix from more expensive to less expensive outputs. Thus, an examination of a direct measure of hospital efficiency is important because it reduces reliance on tenuous assumptions, thereby clarifying the impact of environmental factors on an important dimension of hospital performance. The proposed research will apply SFA to develop measures of inefficiency in U. S. hospitals. The stability and validity of the inefficiency estimates will be evaluated by bivariate analysis. The estimated inefficiency index will serve as the dependent variable in multivariate analysis of the factors associated with inefficiency. Independent variables will be used to test hypotheses about the impact of the following on hospital inefficiency: teaching mission, ownership status, regulatory pressures, competitive pressures, HMO penetration, and uncompensated care A 1997 cross-section of over 1,700 metropolitan hospitals will be analyzed. Primary data sources include HCFA, AHA, and ARF (Area Resource File).