课题基金 / 基金详情

Categorizing Nursing Homes Based on Quality Performance

Categorizing Nursing Homes Based on Quality Performance
根据质量表现对疗养院进行分类
批准号:
6910156
负责人:
SHANNON N FLOOD
金额:
$3.24万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2005
资助国家:
美国
项目状态:
已结题
起止时间:
2005-02-15 至 2006-05-31

项目摘要

项目成果

相关文献

中文摘要
翻译
描述(由申请人提供):最小数据集(MDS)养老院质量指标(QIs)已被各种受众用于各种目的。然而,尽管经过长期和激烈的研究,如何使用质量指标得出关于养老院的结论的概念仍处于起步阶段。研究人员倾向于使用大量的测量方法,而不关注它们如何协同工作,也很少评论如何使用它们来比较一个设施和另一个设施。
英文摘要
DESCRIPTION (provided by the applicant): Minimum Data Set (MDS) nursing home quality indicators (QIs) have been used for a variety of purposes by a variety of audiences. Yet, despite prolonged and intense examination, the concept of how to use quality indicators to draw conclusions about nursing homes is still in its infancy. Researchers tend to use a large number of measures without focusing on how they work together and offering little comment on how they can be used to compare one facility to another. This project aims to address some of these deficiencies by creating a classification of nursing homes based on their performance along dimensions of quality. This research could aid current efforts to link nursing home reimbursement to quality incentives, and to provide better summary information on quality to consumers. The specific aims of this study are as follows: 1) To evaluate the underlying dimensions of risk adjusted quality care indicators using several model candidates including: 1) process/outcome domains 2) Quality Indicator (Ql) content domains and 3) medical/nursing origin of the MDS Qls. 2) To create a categorization of nursing homes based on their functioning on the dimensions created in Specific Aim 1. 3) To evaluate and test this categorization by comparing the clusters with regard to the structural/organizational aspects of the nursing homes. The data for this study come from a project which examines the relationship between the amount of nursing effort and quality of care achieved in a nursing home. MDS records for all Minnesota nursing homes in 2003 will be examined. Statistical approaches include confirmatory factor analysis, two stage cluster analysis, MANOVA and regression.
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