From the March 2017 issue of HealthCare Business News magazine
2: Revenue cycle management
Machine learning can easily be applied outside the realm of patient care, for example, in claims denial management. Denials are one of the most persistent problems in the revenue cycle, with the health care system devoting tremendous time, resources and money to recoding and resubmitting hundreds of billions of dollars in denied or rejected claims.
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The denial of a claim can be due to multiple and complex variables, including patient, procedure, location, doctor, sequencing or payer. This means that uncovering solutions can be scattershot and infrequent. As with clinical variation, query-based approaches are time- and resource-intensive, and often fail to target the root cause of denied claims.
Machine intelligence applications are designed to find the answers by detecting all of the relationships associated with the data. Whereas human investigations into denied claims are slow and inaccurate, machine learning is able to drill down and identify the characteristics of denied or rejected claims holistically and identify changes that can be proactively driven upstream into the claims preparation workflow and potentially into point-of-care guidance.
From a revenue cycle management perspective, the ability to understand, monitor and manage clinical variation for a variety of episodes of care across the care continuum enables health systems to have a clear line of sight to their performance against bundled payments and other value-based arrangements. They will now be able to make the necessary course corrections to minimize an end-of-year shock when payers reconcile performance against contracts.
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