Developing a Robust Measure of Hospital End-of-Life Intensity

制定医院临终强度的稳健衡量标准

• 批准号: 7915430
• 负责人: AMBER E BARNATO
• 金额: $ 14.27万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-15 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7915430
• 关键词: Acute; Address; Admission activity; American; Arts; Back; Behavior; Cancer Patient; Caring; Case Series; Cessation of life; Characteristics; Clinical; Clinical Data; Cost Control; Data; Decision Making; Elderly; Epidemiology; Goals; Gold; Health Care Costs; Health Services Research; Hospitalization; Hospitals; Individual; Intensive Care; Knowledge; Life; Link; Measures; Medical; Medicare; Methods; Patients; Pattern; Pennsylvania; Performance; Policies; Population; Probability; Procedures; Provider; Quality of Care; Race; Reporting; Research; Research Personnel; Risk; Severity of illness; Structural Models; Subgroup; Survival Analysis; System; Techniques; Time; Variant; Vital Statistics; abstracting; base; beneficiary; cohort; end of life; follow-up; improved; outcome forecast; performance tests; stem

DESCRIPTION (provided by applicant): Several researchers have argued that the high intensity of medical care that elderly Medicare beneficiaries receive at the end of life is a potential indicator of poor quality of care and of inefficiency. However, the traditional method for measuring intensity of treatment through the decedent follow-back (case-series) approach may be flawed. Specifically, many decedents were not known to be "dying" and that intensive treatment for them may be appropriate; instead, the goal is to identify patients with poor prognosis for whom intensive care may have low marginal value. We have developed a new measure of end-of-life intensity that focuses on treatment patterns among patients with a high probability of dying (HPD), defined as admissions in the 95th percentile of predicted probability of death. Our HPD measure is a theoretically less biased estimate of decision making for "dying" patients; it compares similar populations across hospitals, and it may be a marker of inefficiency. We plan to use our HPD intensity measure to further elucidate the causes and consequences of race- and condition-specific variations in decision making near the end of life by studying the contribution of individual hospital behavior to these patterns. Blacks have higher rates of life-sustaining treatment (LST) use and patronize hospitals with greater ICU use, and in the aggregate, cancer patients are much less likely than non-cancer patients with serious life limiting illness to receive end-of-life ICU care. Second, we seek to test the performance characteristics of our HPD measure across racial groups and conditions to ascertain whether the measure should be calculated and reported separately for patient subgroups. Third, we seek to explore the generalizability of the HPD approach to the vast majority of administrative data lacking the clinical and risk prediction data available in Pennsylvania. Our aims are:1) To calculate race-specific measures of hospitals' end-of-life treatment intensity and explore the relationship between a hospital's race-specific intensity and post-admission survival; 2) To calculate condition-specific measures of hospitals' end-of-life treatment intensity and explore the relationship between a hospital's condition-specific intensity and post-admission survival; and 3) To develop an administrative data-derived HPD measure and compare it to our "gold standard" clinical data-augmented HPD measure. All analyses will use Pennsylvania Health Care Cost Containment Council data linked to state vital statistics data. Our proposed statistical procedures for developing hospital-specific intensity measures will rely on state-of-the-art Bayesian techniques, and survival analyses will extend to health services research the marginal structural models originally developed in epidemiology to address time-varying confounders. The new measure we seek to refine has the potential to fill a niche in current policy efforts to publicly profile hospitals' performance and to help us better understand how decisions to use intensive care and LST vary by race and condition.

描述（由申请人提供）：一些研究人员认为，老年医疗保险受益人在临终时接受的高强度医疗护理是护理质量差和效率低的潜在指标。 然而，通过死者随访（病例系列）方法来衡量治疗强度的传统方法可能存在缺陷。 具体而言，许多死者并不知道“即将死亡”，对他们进行强化治疗可能是适当的；相反，我们的目标是识别预后不良的患者，而重症监护对这些患者的边际价值可能较低。我们开发了一种新的临终强度测量方法，重点关注高死亡概率 (HPD) 患者的治疗模式，高死亡概率 (HPD) 的定义是入院时处于预测死亡概率的第 95 个百分位数。我们的 HPD 测量理论上是对“垂死”患者决策的偏差较小的估计；它比较了不同医院的相似人群，这可能是效率低下的标志。我们计划通过研究个体医院行为对这些模式的贡献，利用 HPD 强度测量来进一步阐明临终决策中种族和条件特定差异的原因和后果。黑人接受生命维持治疗 (LST) 的比例较高，并且光顾使用 ICU 较多的医院，总体而言，癌症患者比患有严重生命限制疾病的非癌症患者接受临终 ICU 的可能性要低得多关心。其次，我们试图测试 HPD 测量在不同种族群体和条件下的表现特征，以确定是否应该针对患者亚组单独计算和报告该测量。第三，我们寻求探索 HPD 方法对宾夕法尼亚州缺乏临床和风险预测数据的绝大多数行政数据的普遍性。我们的目标是：1）计算医院临终治疗强度的种族特异性指标，并探讨医院临终治疗强度与入院后生存率之间的关系； 2）计算医院临终治疗强度的具体病情指标，并探讨医院具体病情强度与入院后生存率之间的关系； 3) 开发一种基于管理数据的 HPD 测量方法，并将其与我们的“黄金标准”临床数据增强 HPD 测量方法进行比较。所有分析都将使用与州生命统计数据相关的宾夕法尼亚州医疗保健成本控制委员会数据。我们提出的用于制定医院特定强度措施的统计程序将依赖于最先进的贝叶斯技术，生存分析将扩展到卫生服务研究最初在流行病学中开发的边际结构模型，以解决随时间变化的混杂因素。我们寻求完善的新措施有可能填补当前政策努力中的一个空白，以公开描述医院的绩效，并帮助我们更好地了解使用重症监护和 LST 的决策如何因种族和条件而异。