首页> 美国卫生研究院文献>Journal of Patient Experience >Do collaboRATE Scores Reflect Differences in Perceived Shared Decision-Making Across Diverse Patient Populations? Evidence From a Large-Scale Patient Experience Survey in the United States
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Do collaboRATE Scores Reflect Differences in Perceived Shared Decision-Making Across Diverse Patient Populations? Evidence From a Large-Scale Patient Experience Survey in the United States

机译:协作分数是否反映了在不同患者人口中被感知的共享决策的差异?来自美国大规模患者体验调查的证据

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摘要

Patient characteristics have been linked to prevalence and quality of shared decision-making (SDM) behaviors across diverse studies of varied size and focus. We aim to evaluate the extent to which patient characteristics are associated with patient-rated SDM scores as measured by collaboRATE and whether or not collaboRATE varies at the provider group level. We used the 2017 California Patient Assessment Survey data set, which included adult patients of 153 California-based medical groups receiving services between January and October 2016. Mixed-effects logistic regression evaluated relationships between collaboRATE scores and patient characteristics. We analyzed 31 265 total survey responses. Among included covariates, patients’ health status, race, primary language, and mode of survey response were significantly associated with collaboRATE scores. Case-mix adjustment is common in healthcare quality measurement and can be useful in pay-for-performance systems. For those use cases, we recommend adjusting collaboRATE scores by patients’ age, health status, gender, race, and language spoken at home, and survey response mode. However, when case-mix adjustment is not required, we suggest highlighting observed disparities across diverse patient populations to improve attention to inequities in patient experience.
机译:患者特征与各种规模和焦点不同研究的共同决策(SDM)行为的患病率和质量相关联。我们的目标是评估患者特性与通过协作测量的患者特征与患者额定SDM分数相关的程度,以及是否在提供者组水平上变化。我们使用了2017年加州患者评估调查数据集,其中包括2016年1月至10月期间加州的加利福尼亚州的成人患者。混合效应逻辑回归在合作分数和患者特征之间进行了评估的关系。我们分析了3165份全面调查响应。在包含的协变量中,患者的健康状况,种族,主要语言和调查反应模式与合作分数明显相关。案例混合调整在医疗保健质量测量中是常见的,并且可用于性能的性能系统。对于那些用例,我们建议通过家庭患者的年龄,健康状况,性别,种族和语言调整协作分数以及调查响应模式。然而,当不需要案例混合调整时,我们建议突出各种患者群体观察到的差异,以提高患者体验的不公平。

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