Addressing the challenges in healthcare data mapping and normalization
The complexity of modern healthcare systems presents unique challenges, especially when it comes to managing data from diverse sources, formats, and standards. Problems like data fragmentation, inconsistent coding, and manual mapping inefficiencies result in significant barriers for healthcare organizations in getting the full value from their enterprise data.
Poor data quality results in redundant testing, inaccurate analytics, compliance risks, and costly delays in care.
- Inefficiency in data preparation: Data scientists spend over 50% of their time cleaning data rather than analyzing it.
- Patient care gaps: Nearly one-third of patients experience breakdowns in information exchange, such as repeated tests or incomplete records.
- Financial implications: Poor data quality costs organizations up to $12.9 million annually on average.
Organizations need robust, scalable solutions to normalize and optimize their data while preserving its clinical relevance and enabling interoperability.