Precision in Practice
Each patient’s health history is scattered across multiple data sources consisting of unstructured notes and scanned documents. From this fragmented chaos, clinicians and professional HCC coders face the thankless annual task of constructing a complete list of conditions for each and every patient.
In many cases, clinicians and practices may not have access to every data source that makes up the complete patient record. Further complicating matters, even if they have access to all of the sources, they are likely to require multiple platforms and systems to access them. There is no unified source of truth containing all of the clinical information.
Download our white paper to learn how Navina's powerful AI turns fragmented multi-source data into comprehensive and coherent clinical recommendations at the point of care, dramatically improving risk adjustment accuracy.
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Precision in Practice
Multi-Source Data Reconciliation for Risk Adjustment
Each patient’s health history is scattered across multiple data sources consisting of unstructured notes and scanned documents. From this fragmented chaos, clinicians and professional HCC coders face the thankless annual task of constructing a complete list of conditions for each and every patient.
In many cases, clinicians and practices may not have access to every data source that makes up the complete patient record. Further complicating matters, even if they have access to all of the sources, they are likely to require multiple platforms and systems to access them. There is no unified source of truth containing all of the clinical information.
Download our white paper to learn how Navina's powerful AI turns fragmented multi-source data into comprehensive and coherent clinical recommendations at the point of care, dramatically improving risk adjustment accuracy.
