Customer Success
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Customer Success |
Highmark maximizes Medicare revenues with SAS®With the ever-increasing cost of healthcare in the United States, providers and insurers can't afford to under-report potential risks associated with their Medicare/Medicaid patients. Otherwise, they risk losing money when they provide necessary services that might not be related to the initial diagnoses reported to the Centers for Medicare & Medicaid Services (CMS). That's why Highmark relies on SAS Enterprise Miner to build decision trees that map all of the possible outcomes a patient faces based on measures such as symptoms, health history and demographics. Called the Security Blue Reimbursement Model, the application looks for patients who are potentially under-diagnosed for up to 13 conditions and prioritizes them based on risk. Accuracy in diagnosis is critical because the reimbursement received from CMS is contingent upon the diagnosis. "For example, if we find that someone is at risk for renal failure or chronic obstructive pulmonary disease and they are not diagnosed for it, that will cost us a lot of money," explains Chris Scheib, Highmark's Manager of Data Mining and Pattern Discovery. "Using SAS, we are able to look at our medical records to see who might have gone undiagnosed so that we can submit a change and receive proper reimbursement if a patient winds up needing to be treated for one of those illnesses." The cost of resubmitting claims can be thousands of dollars per case, while correctly diagnosing potential risk can save Highmark and its customers millions of dollars, says Shawn McNelis, Highmark's Vice President of Healthcare Informatics. "We are looking at multimillion-dollar savings," he says. Here's how it works: Using a proxy target, the decision tree builds a hierarchy for how patients get identified for each of 13 diseases (the number of illnesses modeled grows each year). When Highmark sums up the parts of the 13 trees – each with hundreds of nodes – the company finds that it has comprehensive information summarized into one score.
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Shawn McNelis Vice President of Healthcare Informatics Highmark
Challenge:
Fine-tune potential risk among patient population to ensure proper diagnoses to allow for fair Medicaid and Medicare reimbursements.
Solution:
SAS Enterprise Miner builds decision trees that help rank patients according to risk so that nurses will know whose medical records to investigate further.
Benefits:
Multimillion-dollar savings in healthcare reimbursements. “ Our ability to reveal gaps between reported data and actual diagnoses delivers significant competitive advantage. We would not have access to this intelligence without SAS. ” Shawn McNelis Vice President of Informatics, Highmark Read more:
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