Natural language processing is an emerging area that can help unlock value from the vast stores of unstructured data that account for as much as 80% of all clinical data. UPMC Health Plan does just that.
Big data analytics in healthcare has largely been about looking at claims, electronic health records (EHR) and other forms of structured data. Natural language processing (NLP) is an emerging area that can help unlock value from the vast amounts of unstructured data that are pervasive in healthcare. In the emerging era of value-based payments, risk adjustments may well determine the difference between profit and loss for the health insurance industry.
UPMC Health Plan, the health insurance arm of the University of Pittsburgh Medical Center (UPMC), has deployed NLP-based technology and big data analytics to efficiently process millions of pieces of documentation to accurately identify risk adjustment possibilities and capture incremental revenue.
Risk adjustment - the money on the table for health plans
Under the risk adjustment program for Medicare Advantage, the Centers for Medicare and Medicaid Services (CMS) adjusts reimbursement amounts based on risk scores that take into account a variety of conditions. The purpose is to adequately cover the costs of providing healthcare, especially to those with complex conditions. Under this program, health plans can increase revenues by submitting documentation from doctor-patient interactions that justifies the risk adjustment. However, they often fail to capitalize on this opportunity for a variety of reasons. The result is a potential loss of revenue for the health plan.
Despite the billions of dollars spent on digitizing medical records under the so-called meaningful use provisions of the HITECH Act, the vast majority of clinical data (estimated widely to be around 80%) is in the form of unstructured data, such as clinical notes, audio transcripts, images and so on. Since unstructured data can support risk adjustment claims, big data and NLP technologies can be used to parse the information in these types of documentation for evidence of incremental risk that can qualify for additional payments.
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