Improving Process Measurement

改进过程测量

• 批准号: 8101921
• 负责人: JEFFREY H SILBER
• 金额: $ 45.7万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2013-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8101921
• 关键词: Improving; Process; Measurement

DESCRIPTION (provided by applicant): This proposal aims to improve process measurement in order to better assess hospital quality of care. Discrete process measures like those used by CMS for Hospital Compare (HC) are commonly used by healthcare evaluation organizations because they are: (1) quick to collect (require less time in abstraction than traditional global measures); (2) easy to understand and point to potentially improvable provider actions; and (3) purportedly require no severity adjustment. In fact, a common motivation for utilizing process measurement is the belief that such measures do not require severity adjustment, beyond coarse inclusion and exclusion criteria. This application examines two fundamental questions regarding process measurement: (1) Do discrete process measures such as those utilized in CMS Hospital Compare need severity adjustment above and beyond the selection criteria commonly used; and (2) Are there other sampling schemes that could be utilized rather than typical random sampling (as is done by CMS) that could better (i.e., more efficiently and with less bias) sample processes at hospitals and therefore allow for the collection of more global process measures that could have stronger associations with outcomes than the discrete measures utilized in Hospital Compare. We present a conceptual model to study the need for severity adjustment for process measures and to compare various process measurement schemes based on reducing overall mean square error (MSE): This study will propose and present preliminary data on a potentially better method to sample charts for process measurement based on a multivariate matching algorithm we call "Multivariate Template Matching" which produces directly standardized matches of patients in order to better select patients inside hospitals to compare process of care across hospitals. We will also examine process measures using a Medicare data set collected as part of the CMS Hospital Compare initiative to (a) establish that we can achieve excellent matches using the Template Matching algorithm; and (b) test whether the bias observed with the present sampling schemes used by CMS for Hospital Compare is reduced using Template Matching. In summary, working with a data set to which CMS has given us special permission to analyze, we will formally test for bias in Hospital Compare, and formally test a more optimal scheme for conducting process measurement through Multivariate Template Matching. If successful, Multivariate Template Matching would allow for the collection of more detailed global process measures since the required sample size for abstraction (and therefore cost) could be reduced. PUBLIC HEALTH RELEVANCE: This application seeks to improve process measurement by (1) testing whether unadjusted process measures are biased because patient factors are associated with process adherence; and (2) developing a new methodology, Multivariate Template Matching, for more efficiently selecting patient charts in which to follow and compare process adherence. The application seeks to utilize a large database of process measure abstractions by CMS through the Hospital Compare project. If successful, results from this application could be utilized to implement template matching when assessing process compliance for Hospital Compare and other programs which study process as a quality of care indicator.

描述（由申请人提供）：本提案旨在改进过程测量，以便更好地评估医院护理质量。医疗保健评估组织通常使用像CMS用于医院比较（HC）的离散过程度量，因为它们：（1）快速收集（比传统的全局度量需要更少的抽象时间）;（2）易于理解并指向潜在的可改进的提供者行动;（3）据称不需要严重性调整。事实上，利用过程度量的一个共同动机是相信这样的度量不需要严重性调整，除了粗略的包含和排除标准。该应用程序检查了关于过程测量的两个基本问题：（1）离散过程测量（如CMS医院比较中使用的测量）是否需要在常用的选择标准之上进行严重性调整;以及（2）是否可以使用其他采样方案，而不是典型的随机采样（如CMS所做的），这些方案可以更好地（即，更有效且偏差更小），从而允许收集更多的全局过程度量，这些度量与医院比较中使用的离散度量相比与结果具有更强的关联。我们提出了一个概念模型来研究过程测量的严重性调整的需要，并比较基于降低总体均方误差（MSE）的各种过程测量方案：这项研究将提出并提出一个潜在的更好的方法，样本图表的过程测量的基础上，我们称之为“多元模板匹配”的多元匹配算法的初步数据。其产生患者的直接标准化匹配，以便更好地选择医院内的患者以比较医院之间的护理过程。我们还将使用作为CMS医院比较计划的一部分收集的医疗保险数据集来检查过程措施，以（a）确定我们可以使用模板匹配算法实现出色的匹配;（B）测试CMS用于医院比较的当前采样方案是否使用模板匹配减少了观察到的偏倚。总之，使用CMS特别允许我们分析的数据集，我们将正式测试医院比较中的偏倚，并正式测试通过多元模板匹配进行过程测量的更优方案。如果成功，多变量模板匹配将允许收集更详细的全球过程测量，因为可以减少抽象所需的样本量（从而降低成本）。 公共卫生相关性：该应用程序旨在通过以下方式改进过程测量：（1）测试未调整的过程测量是否存在偏差，因为患者因素与过程依从性相关;以及（2）开发一种新的方法，即多变量模板匹配，用于更有效地选择患者图表，以便跟踪和比较过程依从性。该应用程序旨在通过医院比较项目利用CMS的大型过程度量抽象数据库。如果成功，则在评估医院比较和其他研究过程作为护理质量指标的程序的过程依从性时，可以利用该应用程序的结果来实现模板匹配。