Standardized Data
Standardize observational data so that applications, tools and methods can be applied across different datasets

Interoperability of healthcare data is essential to a Learning Healthcare System.

Historically, healthcare organizations and individual investigators have assembled a wealth of datasets, coding algorithms, phenotype definitions, and data dictionaries for a myriad of different purposes without any coordination or standardization. This has resulted in huge inefficiencies that impede the translation of evidence-based practices into routine care. Thus, any attempt to combine or compare two or more of these datasets still requires enormous effort. If every healthcare system uses a different name for a data field, then evaluating and comparing system-wide performance becomes impossible.

The purpose of a Common Data Model (CDM) is to standardize the format and content of observational data so that standardized applications, tools and methods can be applied across different datasets. Use of a Common Data Model integrates healthcare records across healthcare organizations so that these data resources can be queried to answer important questions quickly and efficiently.