Guidelines are needed for EHR data quality assessment (DQA) to improve the efficiency, transparency, comparability, and interoperability of data quality assessment, according to a study published in ...

Data quality assessment encompasses the systematic evaluation of data to ensure its suitability for intended purposes within information systems. As organisations amass vast and heterogeneous datasets ...

Electronic health record (EHR)–based real-world data (RWD) are integral to oncology research, and understanding fitness for use is critical for data users. Complexity of data sources and curation ...

An article recently published in Nature proposes a new way to evaluate data quality for artificial intelligence used in healthcare. Several documentation efforts and frameworks already exist to ...

Data quality measures a data set's condition based on factors such as accuracy, completeness, consistency, timeliness, uniqueness and validity. Measuring data quality can help organizations identify ...

Many organizations nowadays are struggling with the quality of their data. Data quality (DQ) problems can arise in various ways. Here are common causes of bad data quality: Multiple data sources: ...

Data quality is paramount in data warehouses, but data quality practices are often overlooked during the development process. The true measure of an effective data warehouse is how much key business ...

We’re just starting to tap the potential of what AI can do. But amid all the breakthroughs, one thing is fundamental: AI is only as good as the data it was trained on. Unlike people, who can draw on ...

Utilities are becoming increasingly skilled at adapting to changes brought on by the digital age: pressure from automation, disruption from new technology, and challenges with how to ingest, manage, ...