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Systematic review of scope and quality of electronic patient record data in primary care

机译:对基层医疗电子病历数据的范围和质量进行系统审查

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摘要

Objective To systematically review measures of data quality in electronic patient records (EPRs) in primary Design Systematic review of English language publications, 1980-2001. Data sources Bibliographic searches of medical databases, specialist medical informatics databases, conference proceedings, and institutional contacts. Study selection Studies selected according to a predefined framework for categorising review papers. Data extraction Reference standards and measurements used to judge quality. Results Bibliographic searches identified 4589 publications. After primary exclusions 174 articles were classified, 52 of which met the inclusion criteria for review. Selected studies were primarily descriptive surveys. Variability in methods prevented meta-analysis of results. Forty eight publications were concerned with diagnostic data, 37 studies measured data quality, and 15 scoped EPR quality. Reliability of data was assessed with rate comparison. Measures of sensitivity were highly dependent on the element of EPR data being investigated, while the positive predictive value was consistently high, indicating good validity. Prescribing data were generally of better quality than diagnostic or lifestyle data. Conclusion The lack of standardised methods for assessment of quality of data in electronic patient records makes it difficult to compare results between studies. Studies should present data quality measures with clear numerators, denominators, and confidence intervals. Ambiguous terms such as "accuracy" should be avoided unless precisely defined.
机译:目的在1980-2001年英语出版物的初步设计系统评价中,系统地回顾电子病历(EPR)中数据质量的度量。数据源医学数据库,专业医学信息数据库,会议记录和机构联系人的书目搜索。研究选择根据预定义的框架对研究论文进行分类选择的研究。数据提取用于判断质量的参考标准和测量值。结果书目搜索确定了4589种出版物。在排除主要对象后,对174篇文章进行了分类,其中52篇符合纳入标准。选定的研究主要是描述性调查。方法的可变性阻止了结果的荟萃分析。 48份出版物涉及诊断数据,37份研究测量了数据质量,15份范围内的EPR质量。通过比率比较评估数据的可靠性。敏感性的测量高度依赖于所调查的EPR数据元素,而阳性预测值始终很高,表明其有效性。处方数据通常比诊断或生活方式数据质量更好。结论缺乏用于评估电子病历中数据质量的标准化方法,使得难以比较研究之间的结果。研究应提供清晰的分子,分母和置信区间的数据质量度量。除非明确定义,否则应避免使用诸如“准确性”之类的歧义术语。

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