In this module, you will explain why comparing healthcare providers with respect to quality can be beneficial, and what types of metrics and reporting mechanisms can drive quality improvement. You'll recognize the importance of making quality comparisons fairer with risk adjustment and defend this methodology to healthcare providers.

In this module, you will define clinical identification algorithms, identify how data are transformed by algorithm rules, and articulate why some data types are more reliable than others when constructing the algorithms. You'll review quality measures and discuss how groupers can help analyze claims or clinical data.

In this module, you will describe logical processes used by database and statistical programmers to extract, transform, and load (ETL) data into data structures required for solving medical problems. You will also harmonize data from multiple sources and prepare integrated data files for analysis.

In this module, you will describe risk stratification for categorizing patients, apply analytical concepts to Medicare data, use data dictionaries and codebooks, and articulate why context matters when analyzing healthcare data.