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生命结束时获得的高强度医疗保健是潜在的指标，表明护理质量和效率低下。 但是，通过死者的后卫（案例系）方法测量治疗强度的传统方法可能存在缺陷。 具体而言，许多死者并不是“死亡”，对他们的强化治疗可能是合适的。相反，目的是确定预后不良的患者的重症监护率可能很低。我们已经开发了一种新的寿命强度量度，该量度重点介绍了高垂死可能性（HPD）的患者之间的治疗模式，这定义为预测死亡概率的第95个百分点。我们的HPD措施是对“死亡”患者的决策做出的偏差估计值。它比较了整个医院的类似人群，并且可能是效率低下的标志。我们计划使用我们的HPD强度度量来进一步阐明种族和条件特定的决策变化在生命结束结束时的原因和后果，通过研究单个医院行为对这些模式的贡献。黑人的使用率更高，维持生命的治疗率（LST）使用更大的ICU使用情况，并且在总体使用情况下，癌症患者的可能性要比患有严重生命的疾病的非癌症患者的可能性要小得多。其次，我们试图测试种族群体和条件的HPD度量的性能特征，以确定是否应计算该措施并分别报告患者亚组。第三，我们试图探索HPD方法对缺乏宾夕法尼亚州临床和风险预测数据的绝大多数行政数据的普遍性。我们的目的是：1）计算医院临终治疗强度的特定种族衡量标准，并探索医院特定种族的强度与入学后生存之间的关系； 2）计算医院临终治疗强度的特定条件度量，并探索医院特定的病情特定强度与入学后生存之间的关系； 3）制定管理数据衍生的HPD度量，并将其与我们的“黄金标准”临床数据增强的HPD度量进行比较。所有分析都将使用宾夕法尼亚州的医疗保健成本遏制委员会数据与州生命统计数据有关。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新的贝叶斯技术，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来解决时间变化的混杂因素。我们寻求完善的新措施有可能在当前的政策努力中填补公开介绍医院绩效的利基市场，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状况而异。