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Latent Class Analysis of Barriers to Care Among Emergency Department Patients

机译:急诊科患者护理障碍的潜在类别分析

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Introduction: Emergency department (ED) patients experience a variety of barriers to care that can lead to unnecessary or repeated visits. By identifying the patterns of barriers experienced by subsets of the ED patient population, future researchers might effectively design interventions to circumvent these barriers and improve care. This study sought to identify classes of individuals with regard to perceived barriers to care.?Methods: Over a 10-week period, two medical students distributed surveys to eligible patients ≥18 years who presented to the ED. After consent, patients provided demographics data and rated their perceived access to care on nine specific items (scored 1-5). We used latent class analysis (LCA), a parametric clustering method, to determine patient groups. Demographic characteristics were then compared across classes.Results: We enrolled a total of 637 patients. Results of the LCA indicated that a six-class solution fit best: 1) low barriers (60%); 2) “work responsibility” barriers (13%); 3) economic-related barriers (10%); 4) “appointment difficulty” barriers (8%); 5) “illness and care responsibilities” barriers (6%); and 6) diverse barriers (2%). Patients in the low-barriers class were the oldest across classes (p.001). Individuals in the low-barriers class were also more likely to be White (p=.015) and have private insurance (p.001) than those in the “appointment difficulty,” “illness and care responsibilities,” and diverse barriers classes.?Conclusion: LCA suggests there are six distinct classes of patients with regard to perceived access to care. These classes may be used as a potential starting point in designing targeted interventions for ED patients to improve continuity of care.
机译:简介:急诊科(ED)患者遇到各种护理障碍,可能导致不必要或重复的就诊。通过确定ED患者群体的一部分所经历的障碍的模式,未来的研究人员可能会有效地设计干预措施来规避这些障碍并改善护理。该研究试图确定与感知障碍有关的个人类别。方法:在10周的时间内,两名医学专业学生向18岁以上就诊的合格患者进行了调查。同意后,患者提供了人口统计学数据,并评估了他们在九个特定项目上获得的护理服务(评分为1-5)。我们使用潜在分类分析(LCA)(一种参数聚类方法)来确定患者组。然后比较各类别的人口统计学特征。结果:我们共纳入637例患者。 LCA的结果表明,六类解决方案最适合:1)低障碍(60%); 2)“工作责任”障碍(13%); 3)与经济有关的壁垒(10%); 4)“任命难度”障碍(8%); 5)“疾病和护理责任”障碍(6%); 6)各种壁垒(2%)。低障碍人群的年龄在所有人群中年龄最大(p <.001)。与“任命难度”,“疾病和护理责任”以及各种障碍类别中的人相比,处于低障碍类别中的个人更可能是白人(p = .015)并拥有私人保险(p <.001)结论:LCA建议在感知获得的护理方面有六类不同的患者。这些类别可以用作为ED患者设计针对性干预措施以改善护理连续性的潜在起点。

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