Mathematica Policy Research, Princeton, NJ.
Mathematica Policy Research, Washington, DC.
Health Serv Res. 2018 Apr;53(2):944-973. doi: 10.1111/1475-6773.12673. Epub 2017 Mar 13.
To describe the modified Patient-Centered Medical Home Assessment (M-PCMH-A) survey module developed to track primary care practices' care delivery approaches over time, assess whether its underlying factor structure is reliable, and produce factor scores that provide a more reliable summary measure of the practice's care delivery than would a simple average of question responses.
DATA SOURCES/STUDY SETTING: Survey data collected from diverse practices participating in the Comprehensive Primary Care (CPC) initiative in 2012 (n = 497) and 2014 (n = 493) and matched comparison practices in 2014 (n = 423).
Confirmatory factor analysis.
Thirty-eight questions organized in six domains: Access and Continuity of Care, Planned Care for Chronic Conditions and Preventive Care, Risk-Stratified Care Management, Patient and Caregiver Engagement, Coordination of Care across the Medical Neighborhood, and Continuous Data-Driven Improvement.
Confirmatory factor analysis suggested using seven factors (splitting one domain into two), reassigning two questions to different domain factors, and removing one question, resulting in high reliability, construct validity, and stability in all but one factor. The seven factors together formed a single higher-order factor summary measure. Factor scores guard against potential biases from equal weighting.
The M-PCMH-A can validly and reliably track primary care delivery across practices and over time using factors representing seven key components of care as well as an overall score. Researchers should calculate factor loadings for their specific data if possible, but average scores may be suitable if they cannot use factor analysis due to resource or sample constraints.
描述经过修改的以患者为中心的医疗之家评估(M-PCMH-A)调查模块,该模块用于跟踪初级保健实践的护理提供方法随时间的变化,评估其潜在的因子结构是否可靠,并产生因子分数,为实践的护理提供比简单平均问题回答更可靠的综合衡量标准。
数据来源/研究环境:2012 年(n=497)和 2014 年(n=493)参加综合初级保健(CPC)计划的各种实践以及 2014 年匹配的比较实践中收集的调查数据(n=423)。
验证性因子分析。
38 个问题分为六个领域:获得和连续护理、慢性病和预防保健计划护理、风险分层护理管理、患者和护理人员参与、医疗邻里间的协调护理以及持续数据驱动的改进。
验证性因子分析表明,使用七个因素(将一个域拆分为两个),将两个问题重新分配给不同的域因素,并删除一个问题,除了一个因素外,其余所有因素都具有较高的可靠性、结构有效性和稳定性。这七个因素共同构成了一个单一的高阶因素综合衡量标准。因子分数可以防止由于加权相等而产生的潜在偏差。
M-PCMH-A 可以使用代表护理七个关键组成部分的因素以及总体得分,在实践和随时间跟踪初级保健的提供情况,具有有效性和可靠性。如果可能的话,研究人员应计算其特定数据的因子载荷,但如果由于资源或样本限制而无法使用因子分析,则平均得分可能是合适的。