From the Department of Neurology (C.M.B., L.P.), University of Colorado Anschutz School of Medicine, Aurora; Department of Neurology (S.B.), University of Minnesota, Minneapolis; Department of Neurology (A.V.), Massachusetts General Hospital, Harvard University, Boston; American Academy of Neurology (K.L., B.M., B.S.), Minneapolis; and Department of Neurology (L.K.J.), Mayo Clinic, Rochester, MN.
Neurology. 2019 Apr 30;92(18):847-858. doi: 10.1212/WNL.0000000000007404. Epub 2019 Apr 5.
To conduct a data validation study encompassing an accuracy assessment of the data extraction process for the Axon Registry®.
Data elements were abstracted from electronic health records (EHRs) by an external auditor (IQVIA) using virtual site visits at participating sites. IQVIA independently calculated Axon Registry quality measure performance rates based on American Academy of Neurology measure specifications and logic using Axon Registry data. Agreement between Axon Registry and IQVIA data elements and measure performance rates was calculated. Discordance was investigated to elucidate underlying systemic or idiosyncratic reasons for disagreement.
Nine sites (n = 720 patients; n = 80 patients per site) with diversity among EHR vendor, practice settings, size, locations, and data transfer method were included. There was variable concordance between the data elements in the Axon Registry and those abstracted independently by IQVIA; high match rates (≥92%) were observed for discrete elements (e.g., demographics); lower match rates (<44%) were observed for elements with free text (e.g., plan of care). Across all measures, there was a 76% patient-level measure performance agreement between Axon Registry and IQVIA (κ = 0.53, < 0.001).
There was a range of concordance between data elements and quality measures in the Axon Registry and those independently abstracted and calculated by an independent vendor. Validation of data and processes is important for the Axon Registry as a clinical quality data registry that utilizes automated data extraction methods from the EHR. Implementation of remediation strategies to improve data accuracy will support the ability of the Axon Registry to perform accurate quality reporting.
对 Axon 注册中心的数据提取过程进行准确性评估，开展一项数据验证研究。
外部审核员（IQVIA）通过虚拟现场访问，从电子健康记录（EHR）中提取数据元素。IQVIA 独立使用 Axon 注册中心的数据，根据美国神经病学学会的衡量标准和逻辑，计算 Axon 注册中心的衡量绩效率。计算了 Axon 注册中心和 IQVIA 数据元素和衡量绩效率之间的一致性。对差异进行了调查，以阐明不一致的潜在系统或个体原因。
纳入了 9 个具有 EHR 供应商、实践环境、规模、地点和数据传输方法多样性的站点（n = 720 名患者；每个站点 80 名患者）。Axon 注册中心的数据元素与 IQVIA 独立提取的数据元素之间存在一定程度的一致性；离散元素（例如人口统计学数据）的匹配率较高（≥92%）；具有自由文本的元素（例如护理计划）的匹配率较低（<44%）。在所有衡量标准中，Axon 注册中心和 IQVIA 之间的患者层面衡量绩效一致性为 76%（κ = 0.53，<0.001）。
Axon 注册中心的数据元素和质量衡量标准与独立供应商独立提取和计算的数据元素之间存在一定的一致性。数据和流程验证对于 Axon 注册中心作为利用 EHR 自动数据提取方法的临床质量数据注册中心非常重要。实施改进数据准确性的补救策略将支持 Axon 注册中心进行准确的质量报告的能力。
