Which of the following describes a systematic approach to ensuring data accuracy in healthcare?

A systematic approach to ensuring data accuracy in healthcare is best described by data governance. This framework encompasses the policies, processes, and standards that organizations implement to manage data effectively. Through data governance, organizations establish roles and responsibilities, ensuring that data is accurate, consistent, and trustworthy across all systems and departments. It also involves continuous monitoring and oversight, which are essential for maintaining high-quality data.

In healthcare, where data integrity is crucial for patient safety and compliance with regulations, data governance plays a pivotal role. It supports the implementation of best practices for data handling, encourages accountability among staff, and promotes a culture of data stewardship. This means that data governance not only focuses on the accuracy of the data but also addresses data privacy and security concerns, ultimately contributing to better decision-making and improved patient outcomes.

Other options, while relevant to data accuracy, do not represent a comprehensive framework like data governance does. Clinical documentation improvement focuses specifically on enhancing the quality of clinical documents. Quality assurance measures typically refer to processes put in place to ensure that services or products meet specific requirements. Data lifecycle management relates to the stages of data from creation to disposal but does not encapsulate the broader governance strategy needed for comprehensive data accuracy.