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首页> 外文期刊>Journal of Patient Experience >Using Electronic Medical Records and Health Claim Data to Develop a Patient Engagement Score for Patients With Multiple Chronic Conditions: An Exploratory Study
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Using Electronic Medical Records and Health Claim Data to Develop a Patient Engagement Score for Patients With Multiple Chronic Conditions: An Exploratory Study

机译:利用电子医疗记录和健康索赔数据为多重慢性病患者开发患者参与评分:探索性研究

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The study objective was to (1) develop a statistical model that creates a novel patient engagement score (PES) from electronic medical records (EMR) and health claim data, and (2) validate this developed score using health-related outcomes and charges of patients with multiple chronic conditions (MCCs). This study used 2014-16 EMR and health claim data of patients with MCCs from Sanford Health. Patient engagement score was created based on selected patients’ engagement behaviors using Gaussian finite mixture model. The PES was validated using multiple logistic and linear regression analyses to examine the associations between the PES and health-related outcomes, and hospital charges, respectively. Patient engagement score was generated from 5095 patient records and included low, medium, and high levels of patient engagement. The PES was a significant predictor for low-density lipoprotein, emergency department visit, hemoglobin A1c, estimated glomerular filtration rate, hospitalization, and hospital charge. The PES derived from patient behaviors recorded in EMR and health claim data can potentially serve as a patient engagement measure. Further study is needed to refine and validate the newly developed score.
机译:研究目标是(1)开发一个统计模型,从电子医疗记录(EMR)和健康索赔数据中创建一个新的患者参与评分(PES),并且(2)使用与健康相关的结果和指控进行验证这一发展分数患者患有多种慢性条件(MCC)。本研究使用了来自桑福德健康的MCCS患者的EMR和健康索赔数据。基于使用高斯有限混合物模型的选定患者的参与行为来创建患者参与评分。使用多个逻辑和线性回归分析验证PE,以检查PE和与健康相关结果和医院费用之间的关联。患者参与评分由5095名患者记录产生,包括低,中等和高水平的患者参与。 PE是低密度脂蛋白,急诊培序,血红蛋白A1C,估计的肾小球过滤率,住院和医院费用的重要预测因子。源自EMR和健康主张数据中记录的患者行为的PE可能是患者参与度量。需要进一步研究来细化和验证新开发的分数。

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