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C-C1-03: Using the Electronic Health Record to Create Population Denominators: Optimization Using Insurance Enrollment

机译:C-C1-03:使用电子病历创建人口分母:使用保险注册进行优化

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摘要

>Background and Aims: The HMORN includes research centers that are part of integrated delivery systems where the practice and insurance entities are independent (e.g., Geisinger, Marshfield). Rules for defining population denominators at these centers require acknowledging that patients in the practice population may not be members of the insurance entity. Methods for defining a population denominator for primary care patients from the electronic health record (EHR) can be validated in the subset of patients who are in the primary care system and members of the insurance entity. The aim of this study was to validate and optimize a method for calculating population denominators from the EHR.>Methods: We proposed a method for defining population denominators from the EHR data at HMORN 2009. This method was based on describing utilization patterns of primary care patients’ overtime. For this study, the cohort was limited to the subset of Geisinger primary care patients who were enrolled in the Geisinger Health Plan. Survival analysis was used to minimize bias in person-time estimates and incidence estimates. The aims were to identify optimal times from insurance enrollment to first utilization and time from final utilization to insurance disenrollment. Since the results are likely dependent upon gender and age (i.e. a typical gap in utilization will tend to be longer for young males as compared to older males), the analyses were compared across patient demographics.>Results: To define cohort entry, the time between initial encounter and insurance enrollment was estimated using survival analysis. EHR enrollment was considered active until the patient failed to have any encounters with a primary care clinic for an age/gender specific cutoff of time. If the patient became inactive, the end date was imputed forward in time based on optimization from survival analysis using the insurance enrollment. These estimates were used to create an age/gender specific algorithm for calculating population denominators from the EHR.>Conclusions: EHR utilization can be used to define population denominators. Validation of the proposed method was conducted by comparing results to insurance enrollment spans. This application is limited to clinical areas where there is evidence of relatively complete capture.
机译:>背景和目标:HMORN的研究中心是集成交付系统的一部分,在这些交付系统中,执业和保险实体是独立的(例如Geisinger,Marshfield)。在这些中心定义人口分母的规则要求确认实践人群中的患者可能不是保险实体的成员。从电子健康记录(EHR)定义基层医疗患者群体分母的方法可以在基层医疗系统和保险实体成员的患者子集中进行验证。这项研究的目的是验证和优化一种从EHR计算人口分母的方法。>方法:我们提出了一种从2009年HMORN的EHR数据中定义人口分母的方法。该方法基于描述初级保健患者加班的使用方式。在本研究中,研究对象仅限于参加Geisinger健康计划的Geisinger基层医疗患者。生存分析用于最大程度地减少人事时间估计和发生率估计中的偏差。目的是确定从保险注册到首次使用的最佳时间以及从最终使用到取消保险的时间。由于结果可能取决于性别和年龄(即,与男性相比,年轻男性的使用率差距通常会更长),因此对患者人群进行了分析比较。>结果:在定义队列进入时,使用生存分析来估计初次接触和参加保险之间的时间。 EHR登记被认为是活跃的,直到患者未能在特定年龄/性别的截止时间与初级保健诊所进行任何会面。如果患者变得不活跃,则根据使用保险注册的生存分析得出的优化结果,将结束日期及时推算出来。这些估计值用于创建特定年龄/性别的算法,以根据EHR计算人口分母。>结论:EHR利用率可用于定义人口分母。通过将结果与保险注册期限进行比较来验证所提出方法的有效性。此应用程序仅限于有相对完整的捕获证据的临床区域。

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