临床质量登记处的数据质量审核：通用框架及澳大利亚和新西兰髋部骨折登记处案例研究
Data quality audit of a clinical quality registry: a generic framework and case study of the Australian and New Zealand Hip Fracture Registry.
作者信息
Tan Aidan Christopher, Armstrong Elizabeth, Close Jacqueline, Harris Ian Andrew
机构信息
Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia.
Falls, Balance and Injury Research Centre, Neuroscience Research Australia, Sydney, New South Wales, Australia.
出版信息
BMJ Open Qual. 2019 Jul 17;8(3):e000490. doi: 10.1136/bmjoq-2018-000490. eCollection 2019.
OBJECTIVES
The value of a clinical quality registry is contingent on the quality of its data. This study aims to pilot methodology for data quality audits of the Australian and New Zealand Hip Fracture Registry, a clinical quality registry of hip fracture clinical care and secondary fracture prevention.
METHODS
A data quality audit was performed by independently replicating the data collection and entry process for 163 randomly selected patient records from three contributing hospitals, and then comparing the replicated data set to the registry data set. Data agreement, as a proxy indicator of data accuracy, and data completeness were assessed.
RESULTS
An overall data agreement of 82.3% and overall data completeness of 95.6% were found, reflecting a moderate level of data accuracy and a very high level of data completeness. Half of all data disagreements were caused by information discrepancies, a quarter by missing discrepancies and a quarter by time, date and number discrepancies. Transcription discrepancies only accounted for 1 in every 50 data disagreements. The sources of inaccurate and incomplete data have been identified with the intention of implementing data quality improvement.
CONCLUSIONS
Regular audits of data abstraction are necessary to improve data quality, assure data validity and reliability and guarantee the integrity and credibility of registry outputs. A generic framework and model for data quality audits of clinical quality registries is proposed, consisting of a three-step data abstraction audit, registry coverage audit and four-step data quality improvement process. Factors to consider for data abstraction audits include: central, remote or local implementation; single-stage or multistage random sampling; absolute, proportional, combination or alternative sample size calculation; data quality indicators; regular or ad hoc frequency; and qualitative assessment.
目的
临床质量登记处的价值取决于其数据质量。本研究旨在试点澳大利亚和新西兰髋部骨折登记处的数据质量审核方法，该登记处是一个关于髋部骨折临床护理和继发性骨折预防的临床质量登记处。
方法
通过独立复制来自三家参与医院的163份随机选择的患者记录的数据收集和录入过程，进行数据质量审核，然后将复制的数据集与登记处数据集进行比较。评估数据一致性（作为数据准确性的替代指标）和数据完整性。
结果
发现总体数据一致性为82.3%，总体数据完整性为95.6%，这反映出数据准确性处于中等水平，数据完整性处于非常高的水平。所有数据不一致情况中，一半是由信息差异导致的，四分之一是由缺失差异导致的，四分之一是由时间、日期和数字差异导致的。转录差异仅占每50个数据不一致情况中的1个。已确定了不准确和不完整数据的来源，旨在实施数据质量改进。
结论
定期进行数据提取审核对于提高数据质量、确保数据有效性和可靠性以及保证登记处输出的完整性和可信度是必要的。提出了一个临床质量登记处数据质量审核的通用框架和模型，包括三步数据提取审核、登记处覆盖范围审核和四步数据质量改进过程。数据提取审核需考虑的因素包括：集中、远程或本地实施；单阶段或多阶段随机抽样；绝对、成比例、组合或替代样本量计算；数据质量指标；定期或临时频率；以及定性评估。
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