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: 7559808
• 负责人: AMBER E BARNATO
• 金额: $ 14.41万
• 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/7559808
• 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接受的高强度医疗护理是护理质量差和效率低下的潜在指标。 然而，通过死者随访（病例系列）方法测量治疗强度的传统方法可能存在缺陷。 具体来说，许多死者并不知道是“垂死”，对他们的强化治疗可能是适当的;相反，目标是确定预后不良的患者，重症监护可能具有较低的边际价值。我们开发了一种新的临终强度测量方法，重点关注死亡概率高（HPD）患者的治疗模式，HPD定义为预测死亡概率的第95百分位数。我们的HPD指标在理论上是对“垂死”患者决策的偏差较小的估计;它比较了医院之间的相似人群，并且可能是效率低下的标志。我们计划使用我们的HPD强度措施，通过研究个体医院行为对这些模式的贡献，进一步阐明种族和条件特异性变化的原因和后果。黑人使用维持生命治疗（LST）的比率较高，并光顾ICU使用率较高的医院，总体而言，癌症患者比患有严重生命限制性疾病的非癌症患者接受临终ICU护理的可能性要小得多。其次，我们试图测试我们的HPD措施在不同种族群体和条件下的性能特征，以确定该措施是否应单独计算和报告患者亚组。第三，我们试图探索HPD方法的普遍性，以绝大多数缺乏临床和风险预测数据的管理数据在宾夕法尼亚州。本研究的目的是：1）计算不同种族医院的临终治疗强度指标，并探讨不同种族医院的临终治疗强度指标与患者入院后生存率的关系; 2）计算不同疾病医院的临终治疗强度指标，并探讨不同疾病医院的临终治疗强度指标与患者入院后生存率的关系;和3）开发管理数据衍生的HPD测量，并将其与我们的“金标准”临床数据增强的HPD测量进行比较。所有分析将使用宾夕法尼亚州卫生保健成本控制理事会数据链接到国家的生命统计数据。我们提出的统计程序开发医院的具体强度措施将依赖于国家的最先进的贝叶斯技术，和生存分析将扩展到卫生服务研究的边缘结构模型最初在流行病学中开发，以解决随时间变化的混杂因素。我们寻求改进的新措施有可能填补当前政策努力的空白，以公开描述医院的表现，并帮助我们更好地了解使用重症监护和LST的决定如何因种族和条件而异。