VARIATION IN INPATIENT COST & NET CORPORATE VALUE BY DRG

在医疗保健服务迅速变化的时期，管理人员和 政策制定者严重缺乏证据来证明他们的行动的影响 可以客观衡量和评价的结果。 这个项目 建议提供一个重要的新的衡量之间的关系， 住院医院资源和净价值收到，使用独特的 雇主/集团采购商的观点。 大公司，一部分 其中35万名员工的数据将被研究， 直接保健服务（投入方）和吸收间接保健服务（投入方） 损失的工作时间的补偿和生产力成本（产出方面）。 为 疾病住院天数或费用的确定减少可 但这一影响可能会被恢复工作速度较慢的增量效应所抵消。 或者，直接医疗保健成本控制行动的效果可能 由于避免了住院引起的更快的恢复 并发症 这项研究将提供主要DRG的经验数据， 该公司所服务的各个领域和不同行业， 在直接卫生保健成本和间接影响之间进行权衡， 企业价值 这些结果也将是有用的指标， 卫生系统对其他人口的总体有效性 片段 该方法将利用多元回归估计系数 输入（住院天数和付款）和一些协变量（工作 分类、性别、年龄、终止概率和前几年 利用率作为健康状况的指标）和其他虚拟控制 变量（行业、位置）。 这些将被审查的意义和 亚组之间的差异。 MANOVA还将用于测试 这种差异的重要性，以及相互作用的重要性 方面的影响. 使用参数和非参数的聚合技术 转换，将用于控制DRG之间的差异， 从而允许更大的样本量用于检验假设。 最终结果将是按关键次级领域分列的具体信息， 边际保健支出的有效性的制度。 领域 将强调在政策和管理方面的注意。

In a period of rapid change in heath care delivery, both managers and policy makers are sorely lacking evidence as to the impact of their actions on outcomes that can be objectively measured and valued. This project proposes to provide an important new gauge of the relationship between inpatient hospital resources and net value received, using the unique perspective of the employer/group purchaser. The large company, a portion of whose data on 350,000 employees will be studied, is both paying for direct heath care services (input side) and absorbing the indirect compensation and productivity costs of lost work time (output side). For the firm decrease in days or dollars of hospitalization for an illness may be offset by the incremental effect of a less rapid return to work. Alternatively, the effect of direct heath care cost containment action may be a faster recovery due to the avoidance of hospital induced complications. This research will provide empirical data by major DRG, among geographical areas and across different industries served by this firm, regarding the tradeoff between direct health care cost and its indirect effect on corporate value. These results also will be useful as indicators of the general effectiveness of the health system regarding other population segments. The methodology will utilize multiple regression to estimate coefficients of input (hospital days and payments) and a number of covariates (job classification, sex, age,probability of termination, and prior years utilization as an indicator of health status) and other dummy control variable (industry, location). These will be examined for significance and differences among subgroups. MANOVA also will be used to test the significance of such differences, and the importance of interaction effects. Aggregation techniques using both parametric and nonparametric transformation, will be used to control for differences across DRGs and thereby permit larger sample sizes for testing hypotheses. The end result will be specific information by critical sub areas of the system on the effectiveness of marginal heath care expenditures. Areas for attention in both policy and management will be highlighted.