Routinely-collected general practice data are complex, but with systematic processing can be used for quality improvement and research.
Routinely-collected general practice data are complex, but with systematic processing can be used for quality improvement and research.
复制标题
常规收集的全科医疗数据很复杂，但经过系统处理可用于质量改进和研究。
DOI：
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发表时间：
2006
影响因子：
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通讯作者：
Pushpa Kumarapeli
中科院分区：
文献类型：
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作者：
S. de Lusignan;N. Hague;J. van Vlymen;Pushpa Kumarapeli
背景
联合王国的一般做法是计算机化的，基于计算机数据的质量目标为提高数据质量提供了进一步的激励。国家信息技术方案正在使技术基础设施标准化，并消除数据汇总的一些障碍。收集的数据是一种未充分利用的资源，但很少有人写过如果我们要从一般实践数据中推断出意义，需要考虑的各种因素。
目的
报告全科医学计算机数据的复杂性以及在其处理和解释中需要考虑的因素。
方法
我们开展以临床为重点的项目，为临床医生提供临床相关的反馈，并为地方和研究人员提供总体统计数据。然而，考虑到这些数据的复杂性，我们仔细设计了一个系统的过程阶段和过程控制，以保持参考完整性，提高数据质量和减少错误。这些都集成到我们的设计和加工阶段。我们的系统记录查询，参考代码集和创建唯一的患者ID。设计阶段之后是评估：数据输入问题，如何在临床系统中表示概念，编码模糊性，在需要时使用替代品，验证和试点。数据的后续处理包括提取、迁移和整合来自不同来源的数据、清理、处理和分析。
结果
结果旨在说明人口分母、数据输入问题、识别未满足需求的人以及如何使用常规数据进行真实世界的药物测试等问题。
结论
及时收集的初级保健数据可对改善健康进程作出更大贡献;然而，处理这些数据的人员需要充分了解数据输入所处环境的复杂性。
BACKGROUND
UK general practice is computerised, and quality targets based on computer data provide a further incentive to improve data quality. A National Programme for Information Technology is standardising the technical infrastructure and removing some of the barriers to data aggregation. Routinely collected data is an underused resource, yet little has been written about the wide range of factors that need to be taken into account if we are to infer meaning from general practice data.
OBJECTIVE
To report the complexity of general practice computer data and factors that need to be taken into account in its processing and interpretation.
METHOD
We run clinically focused programmes that provide clinically relevant feedback to clinicians, and overview statistics to localities and researchers. However, to take account of the complexity of these data we have carefully devised a system of process stages and process controls to maintain referential integrity, and improve data quality and error reduction. These are integrated into our design and processing stages. Our systems document the query, reference code set and create unique patient ID. The design stage is followed by appraisal of: data entry issues, how concepts might be represented in clinical systems, coding ambiguities, using surrogates where needed, validation and pilot-ing. The subsequent processing of data includes extraction, migration and integration of data from different sources, cleaning, processing and analysis.
RESULTS
Results are presented to illustrate issues with the population denominator, data entry problems, identification of people with unmet needs, and how routine data can be used for real-world testing of pharmaceuticals.
CONCLUSIONS
Routinely collected primary care data could contribute more to the process of health improvement; however, those working with these data need to understand fully the complexity of the context within which data entry takes place.