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首页> 外文期刊>Journal of public health management and practice: JPHMP >Identification of patients with arthritis and arthritis-related functional limitation using administrative data.
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Identification of patients with arthritis and arthritis-related functional limitation using administrative data.

机译:使用行政数据识别患有关节炎和关节炎相关功能受限的患者。

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OBJECTIVE: To develop algorithms on the basis of administrative data to identify patients with arthritis and arthritis-related functional limitation (AFL). STUDY DESIGN AND SETTING: In this retrospective study, 361 enrollees of a health plan underwent a clinical examination to confirm arthritis and assessment of functional limitation on the basis of responses to the health assessment questionnaire. Administrative data were obtained on these subjects and included arthritis drugs dispensed, as well as outpatient and emergency department diagnoses and procedures (including radiographic studies, arthritis procedures, and laboratory tests). Composite risk scores for arthritis and AFL were created on the basis of the association of arthritis-related healthcare utilization with confirmed arthritis and AFL. Algorithms were then developed on the basis of the composite risk scores using logistic regression models. RESULTS: The algorithm using the arthritis composite score to identify arthritis patients had an area under the ROC curve (AUC) of 0.74, sensitivity of 75 percent and specificity of 57 percent. Similarly, the algorithm using the AFL composite score to identify patients with AFL had an AUC of 0.73, sensitivity of 62 percent, and specificity of 75 percent. CONCLUSION: The developed algorithms will enable health plans to screen their enrollees for persons with arthritis and AFL. This will facilitate enrollment of patients with arthritis and AFL in disease management programs and/or targeted interventions.
机译:目的:在行政数据的基础上开发算法,以识别患有关节炎和关节炎相关功能受限(AFL)的患者。研究设计与设置:在这项回顾性研究中,对361名健康计划的参与者进行了临床检查,以根据对健康评估调查表的回答确定关节炎并评估功能障碍。获得了关于这些主题的行政数据,包括分配的关节炎药物,以及门诊和急诊科的诊断和程序(包括射线照相研究,关节炎程序和实验室检查)。根据关节炎相关的医疗保健利用与确诊的关节炎和AFL的关联,创建了关节炎和AFL的综合风险评分。然后,使用logistic回归模型在综合风险评分的基础上开发算法。结果:使用关节炎综合评分来识别关节炎患者的算法的ROC曲线下面积(AUC)为0.74,敏感性为75%,特异性为57%。同样,使用AFL综合评分来识别AFL患者的算法的AUC为0.73,敏感性为62%,特异性为75%。结论:开发的算法将使健康计划能够筛查其患有关节炎和AFL的人。这将有助于将患有关节炎和AFL的患者纳入疾病管理计划和/或有针对性的干预措施。

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