The calculation of quality of care measures based on electronic medical records (EMRs) may be inaccurate because of incomplete capture of past services. We evaluate the influence of different statistical approaches for calculating the proportion of patients who are up-to-date for a preventive service, using the example of colorectal cancer (CRC) screening. We propose an extension of traditional mixture models to account for the uncertainty in compliance which is further complicated by the choice of various screening modalities with different recommended screening intervals. We conducted simulation studies to compare various statistical approaches and demonstrated that the proposed method can alleviate bias when individuals with complete prior medical history information were not representative of the targeted population. The method is motivated by and applied to data from the National Cancer Institute–funded consortium Population-Based Research Optimizing Screening through Personalized Regiments (PROSPR). Findings from the application are important for the evaluation of appropriate use of preventive care and provide a novel tool for dealing with similar analytical challenges with EMR data in broad settings.

中文翻译：

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电子病历中不完整历史数据的预防保健措施分析：以结直肠癌筛查为例

由于对过去服务的捕获不完整，基于电子病历 (EMR) 的护理质量衡量标准的计算可能不准确。我们以结直肠癌 (CRC) 筛查为例，评估了不同统计方法对计算最新预防服务患者比例的影响。我们提出了传统混合模型的扩展，以解决依从性的不确定性，由于选择具有不同推荐筛查间隔的各种筛查方式，这种不确定性变得更加复杂。我们进行了模拟研究来比较各种统计方法，并证明当具有完整既往病史信息的个体不能代表目标人群时，所提出的方法可以减轻偏差。该方法的灵感来自于美国国家癌症研究所资助的联盟“通过个性化军团优化筛查”（PROSPR）的数据。该应用程序的发现对于评估预防性护理的适当使用非常重要，并为在广泛的环境中处理 EMR 数据的类似分析挑战提供了一种新颖的工具。