Empirical Classification of the Typologies of Hospital Deaths

医院死亡类型的实证分类

• 批准号: 10261322
• 负责人: AMBER E BARNATO
• 金额: $ 20.5万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10261322
• 关键词: Acute Disease; Acute myocardial infarction; Attention; Caring; Cessation of life; Chronic Disease; Chronic Obstructive Airway Disease; Classification; Clinical; Cluster Analysis; Computerized Medical Record; Data; Data Set; Data Sources; Decision Analysis; Decision Making; Dementia; Development; Disease; Epidemiology; Event; Failure; Goals; Health system; Heart failure; Hospitals; Incentives; Intervention; Interview; Judgment; Life; Malignant Neoplasms; Maps; Measurement; Measures; Medical Errors; Medical center; Mental Recall; Methods; Modeling; Organ failure; Outcome; Patient Preferences; Patients; Performance; Pneumonia; Process; Provider; Quality of life; Reporting; Research; Rewards; Risk; Stroke; System; Techniques; Training; Typology; United States; Validation; acute care; adjudicate; adjudication; base; data mining; design; end of life care; end-of-life decision making; high reward; hospital performance; improved; innovation; insight; member; mortality; novel; overtreatment; palliation; prevent; preventable death; prospective; quality of death; tool

PROJECT ABSTRACT Measurement of hospital performance through risk-adjusted mortality rates is a cornerstone of the United States health system, factoring heavily into value-based purchasing and other strategies to incent quality improvement. Although risk-adjusted mortality captures one of the key functions of a hospital – its ability to prevent death – it makes no distinction between deaths that result from suboptimal care and those that result from informed decisions to forego life-sustaining treatment and focus on palliation in anticipation of death. This results in two critical problems. First, by rewarding hospitals for fewer deaths, these measures create an incentive to prolong life with aggressive interventions when palliation in anticipation of death may better align with patient preferences. Second, by failing to distinguish between different types of deaths, these measures do not provide insight into the specific targets on which a hospital should focus its quality improvement efforts, since the mechanism underlying the high mortality rate is not known. Ideally, a mortality-based hospital performance measure would simultaneously (1) reward reductions in deaths that result from suboptimal disease-directed care or medical errors, (2) reward increases in high-quality palliation in anticipation of death when consistent with patient preferences, and (3) provide hospitals with actionable information for quality improvement by offering insight into how patients are dying. A critical barrier to the development of such a measure is our current inability to distinguish different types of deaths. Missing is a detailed epidemiology of the typologies of hospital deaths, i.e., “how” hospitalized patients are dying. The goal of this project is to identify typologies of hospital deaths that can inform new strategies in hospital performance measurement to improve multidimensional quality of hospital care. We will study patients hospitalized with chronic obstructive pulmonary disease, acute myocardial infarction, heart failure, pneumonia, and cerebrovascular accident because they are the subject of public mortality reporting and pay-for-performance initiatives and represent acute and chronic diseases for which in-hospital decisions regarding end-of-life care may be different. We will focus particular attention on patients with dementia who may be at disproportionate risk for both under- treatment (i.e., failure to rescue due to provider bias) and over-treatment (i.e., rescue when quality of life is already below the patient’s minimal acceptable function). We will use innovative data mining techniques to identify key clinical variables through mixed-methods analysis of the decision-making processes of institutional mortality reviewers. Findings from this R21 will be used to support an R01 application designed to externally validate the typologies with the ultimate goal of developing innovative composite performance measures that can simultaneously reward high-quality disease-directed care and high-quality end-of-life care.

项目摘要 通过风险调整后的死亡率来衡量医院的绩效是联合国 国家卫生系统，在很大程度上考虑到基于价值的采购和其他战略，以激励质量 改进.虽然风险调整死亡率抓住了医院的关键功能之一-它的能力， 预防死亡-它不区分因护理欠佳而导致的死亡和 从明智的决定到放弃维持生命的治疗，并专注于减轻预期死亡。这 导致两个关键问题。首先，通过奖励减少死亡的医院，这些措施创造了一个 当预期死亡的姑息治疗可能更好地结合时， 患者的偏好。其次，由于未能区分不同类型的死亡，这些措施 没有提供医院应重点改进质量的具体目标， 因为高死亡率背后的机制尚不清楚。理想情况下，一家以死亡率为基础的医院 性能指标将同时（1）奖励因次优而导致的死亡减少 疾病导向的护理或医疗错误，（2）预期死亡的高质量缓解的奖励增加 当与患者偏好一致时，以及（3）为医院提供可操作的质量信息 通过深入了解病人的死亡方式来改善病情。发展这种技术的关键障碍是 我们目前无法区分不同类型的死亡。缺少的是一个详细的流行病学， 医院死亡的类型学，即，住院病人是如何死亡的该项目的目标是 确定医院死亡的类型学，可以为医院绩效评估提供新的策略， 提高医院护理的多维质量。我们将研究慢性阻塞性肺疾病住院患者， 肺部疾病、急性心肌梗死、心力衰竭、肺炎和脑血管意外 因为他们是公共死亡率报告和绩效工资计划的主题， 急性和慢性疾病，其临终护理的住院决定可能不同。我们将 特别关注痴呆症患者，他们可能有不成比例的风险， 处理（即，由于提供者的偏见而未能挽救）和过度治疗（即，当生活质量 已经低于患者的最小可接受功能）。我们将使用创新的数据挖掘技术， 通过机构决策过程的混合方法分析确定关键临床变量 死亡率审查员。此R21的结果将用于支持R 01应用程序，该应用程序设计为从外部 验证类型学，最终目标是开发创新的综合业绩衡量标准， 可以同时奖励高质量的疾病导向护理和高质量的临终护理。