Danford Christopher P, Horvath Monica M, Hammond W Edward, Ferranti Jeffrey M
Duke University School of Medicine, Durham, NC;
Duke Health Technology Solutions, Duke University Health System, Durham, NC;
AMIA Annu Symp Proc. 2013 Nov 16;2013:278-83. eCollection 2013.
Self-service database portals may improve access to institutional data resources for clinical research or quality improvement, but questions remain about the validity of this approach. We tested the accuracy of data extracted from a clinical data repository using a self-service portal by comparing three approaches to measuring medication use among patients with coronary disease: (1) automated extraction using a portal, (2) extraction by an experienced data architect, and (3) manual chart abstraction. Outcomes included medications and diagnoses (e.g., myocardial infarction, heart failure). Charts were manually reviewed for 200 patients. Using matched criteria, self-service query identified 7327 of 7358 patients identified by the data analyst. For patients in both cohorts, agreement rates ranged from 0.99 for demographic data to 0.94 for laboratory data. Based on chart review, the self-service portal and the analyst had similar sensitivities and specificities for comorbid diagnoses and statin use.
自助式数据库门户可能会改善临床研究或质量改进对机构数据资源的获取,但这种方法的有效性仍存在疑问。我们通过比较三种测量冠心病患者用药情况的方法,测试了使用自助式门户从临床数据存储库中提取数据的准确性:(1)使用门户自动提取,(2)由经验丰富的数据架构师提取,以及(3)手动图表摘要。结果包括药物和诊断(如心肌梗死、心力衰竭)。对200名患者的图表进行了人工审核。使用匹配标准,自助查询识别出数据分析师识别出的7358名患者中的7327名。对于两个队列中的患者,人口统计学数据的一致率为0.99,实验室数据的一致率为0.94。基于图表审核,自助式门户和分析师在合并症诊断和他汀类药物使用方面具有相似的敏感性和特异性。