This chapter presents our assessment of a classic measure of quality for the data collected in the 2020 Census—namely, the accuracy of data on age. Such accuracy may be analyzed by examining the ...

Obesity is a serious chronic disease and risk factor for a broad range of outcomes. This study identifies opportunities for improving quality in obesity care. Objectives: To evaluate the ...

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 ...

Survey methodology encompasses the design, implementation and analysis of instruments for collecting information from populations of interest. Core components include defining a clear research ...

Despite clear data quality and regulatory advantages, paper-based clinical outcome assessments persist due to cost asymmetry, ...

The use of analytics is no longer limited to big companies with deep pockets. It’s now widespread, with 59% of enterprises using analytics in some capacity. And companies are capitalizing on this ...

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 ...

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 ...

Preferences and perceptions of patients with metastatic castration-resistant prostate cancer for treatments and biomarker testing: An international qualitative study. This is an ASCO Meeting Abstract ...

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