Hoxhaj Vjola, Andaur Navarro Constanza L, Riera-Arnau Judit, Elbers Roel J H J, Alsina Ema, Dodd Caitlin, Sturkenboom Miriam C J M
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Clinical Pharmacology Service, Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, Universitat Autònoma de Barcelona, Barcelona, Spain.
Pharmacoepidemiol Drug Saf. 2025 Jan;34(1):e70089. doi: 10.1002/pds.70089.
To describe the development of INSIGHT, a real-world data quality tool to assess completeness, consistency, and fitness-for-purpose of observational health data sources.
We designed a three-level pipeline with data quality assessments (DQAs) to be performed in ConcePTION Common Data Model (CDM) instances. The pipeline has been coded using R.
INSIGHT is an open-source tool that identifies potential data quality issues in CDM-standardized instances through the systematic execution and summary of over 588 configurable DQAs. Level 1 focuses on conformance to the ConcePTION CDM specifications. Level 2 evaluates the temporal plausibility of events and uniqueness of records. Level 3 provides an overview of distributions, outliers, and trends over time to facilitate fit-for-purpose evaluation. Therefore, level 1 and 2 assure a proper data standardization, while level 3 provides information regarding the study population, and potential sub-populations. The DQAs are run locally and assessed centrally by a data quality revisor together with the data access provider's representatives.
Data quality is the sum of several internal and external features of the data. While DQAs can provide reassurance about fitness-for-purpose for secondary-use data sources, improvements in data collection are essential to reduce errors and enhance overall data quality for Real World Evidence.
INSIGHT aims to support clinical and regulatory decision-making for medicines and vaccines by evaluating the quality of observational health data sources to support fit for purpose assessment. Assessing and improving data quality will enhance the reliability and quality of the generated evidence.
This research was registered in EU PAS registration with number EU50142.
描述INSIGHT的开发情况，这是一种用于评估观察性健康数据源的完整性、一致性和适用性的真实世界数据质量工具。
我们设计了一个三级流程，在ConcePTION通用数据模型（CDM）实例中进行数据质量评估（DQA）。该流程已使用R语言编码。
INSIGHT是一个开源工具，通过系统执行和总结超过588个可配置的DQA，识别CDM标准化实例中的潜在数据质量问题。第1级侧重于符合ConcePTION CDM规范。第2级评估事件的时间合理性和记录的唯一性。第3级提供随时间的分布、异常值和趋势概述，以促进适用性评估。因此，第1级和第2级确保适当的数据标准化，而第3级提供有关研究人群和潜在亚人群的信息。DQA在本地运行，并由数据质量审核员与数据访问提供商的代表一起进行集中评估。
数据质量是数据若干内部和外部特征的总和。虽然DQA可以为二次使用数据源的适用性提供保证，但改进数据收集对于减少错误和提高真实世界证据的整体数据质量至关重要。
INSIGHT旨在通过评估观察性健康数据源的质量以支持适用性评估，来支持药品和疫苗的临床及监管决策。评估和改进数据质量将提高所生成证据的可靠性和质量。
本研究已在欧盟PAS注册，注册号为EU50142。
