X-INEFFICIENCY IN US HOSPITALS

本研究的具体目标是：（1）检查 美国医院X-无效率的相关因素;（2）验证 随机前沿分析（SFA）X- 低效率是最佳性能和实际性能之间的差异 性能 X-低效率可能占18%， 美国医院的费用。 对过度支出的担忧 已经创造了一个长期的兴趣，了解的原因， 美国医疗保健机构效率低下。 大多数研究 试图研究市场力量的影响， 关于医院效率的法规已经将成本作为一种间接的 衡量效率。 隐含的假设是， 在减少的同时，效率必须提高。 这个假设可能不是 正确. 除了提高效率外，还可以控制成本 通过减少服务或改变产品组合， 昂贵的输出。 因此，检查医院的直接措施 效率很重要，因为它减少了对脆弱的 假设，从而澄清环境因素对 医院绩效的一个重要方面。 拟议的研究将应用SFA来制定低效措施 在us S.医院的 无效率的稳定性和有效性 将通过双变量分析评价估计值。 估计 无效率指数将作为多变量中的因变量， 分析与效率低下有关的因素。 独立 变量将被用来测试假设的影响， 对医院低效率的关注：教学使命，所有权地位， 监管压力、竞争压力、HMO渗透率，以及 1997年，在1,700多个大都市中， 医院将进行分析。 主要数据来源包括HCFA、AHA和 ARF（Area Resource File）

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).