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A system for solution-orientated reporting of errors associated with the extraction of routinely collected clinical data for research and quality improvement

机译：一种溶液导向的系统，与常规收集的研究和质量改进进行常规收集的临床数据相关的错误

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Background: We have used routinely collected clinical data in epidemiological and quality improvement research for over 10 years. We extract, pseudonymise and link data from heterogeneous distributed databases; inevitably encountering errors and problems. Objective: To develop a solution-orientated system of error reporting which enables appropriate corrective action. Method: Review of the 94 errors, which occurred in 2008/9. Previously we had described failures in terms of the data missing from our response files; however this provided little information about causation. We therefore developed a taxonomy based on the IT component limiting data extraction. Results: Our final taxonomy categorised errors as: (A) Data extraction Method and Process; (B) Translation Layer and Proxy Specification; (C) Shape and Complexity of the Original Schema; (D) Communication and System (mainly Software-based) Faults; (E) Hardware and Infrastructure; (F) Generic/Uncategorised and/or Human Errors. We found 79 distinct errors among the 94 reported; and the categories were generally predictive of the time needed to develop fixes. Conclusions: A systematic approach to errors and linldng them to problem solving has improved project efficiency and enabled us to better predict any associated delays.
机译：背景：我们已经在流行病学和质量改进研究中使用了常规收集的临床数据超过10年。我们从异构分布式数据库中提取，假奏名和链接数据;不可避免地遇到错误和问题。目的：开发一种方向性的错误报告系统，可实现适当的纠正措施。方法：审查2008/9发生的94个错误。以前我们在响应文件中缺少的数据方面描述了错误;但是，这提供了有关因果关系的少量信息。因此，我们根据IT组件限制数据提取开发了分类学。结果：我们的最终分类学分类错误是：（a）数据提取方法和过程; （b）翻译层和代理规范; （c）原始架构的形状和复杂性; （d）通信和系统（主要是基于软件）的​​故障; （e）硬件和基础设施; （f）通用/未分类和/或人为错误。我们发现了94个报告中的79个不同的错误;这些类别通常预测开发修复所需的时间。结论：对问题解决的错误和LINDNG的系统方法有所提高，并使我们能够更好地预测任何相关的延迟。

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