The downstream impacts of a dirty data lake and how normalization can help

When it comes to patient data, accuracy and reliability are paramount. That’s especially true when this information is used to guide important initiatives – like quality reporting, revenue cycle management, and patient cohorting.

But in the era of increased interoperability, data is often transferred and aggregated from multiple sources before it is put to actual use. With each exchange, new opportunities for data degradation arise, and these hazards – often in the form of lost secondary codes or a lack of specificity – can cause significant downstream problems for health systems.

That’s why a data normalization solution is so important. But not all methods of standardizing information are created equal.

This eBook explores the need for normalization, the negative impacts of dirty patient data, and how solutions that harness the power of a robust and granular clinical terminology can help enrich data to ensure both its completeness and accuracy.

Download the eBook