The clinical terminology used to describe a patient’s health is complex. Indeed, clinical documentation must not only be robust, descriptive, and granular; but also link to the appropriate standardized codes and metadata that inform initiatives such as:
- Revenue cycle management
- Precise patient cohorting
- Accurate quality reporting
But data is gathered from a variety of disparate sources, meaning health systems must manage information that isn’t always compatible. In addition, through the process of sharing data between different EHRs and health IT systems, information that once told a complete patient story can quickly become filled with gaps when important details – like secondary codes – are lost in the data transfer process.
In our latest eBook, The downstream impacts of a dirty data lake and how normalization can help, we examine how incomplete or inconsistent data can impede important initiatives within health systems. We also explore how a robust normalization solution, grounded in a foundational layer of clinical terminology, can help complete and enrich patient data to help mitigate this challenge.