Rejected or denied claims represent hundreds of billions of dollars in lost revenue for healthcare organizations each year, costing them about 5% of their net revenue stream. While the recommended rate for denials is under 4%, the actual rate for many healthcare organizations is around 20%. The most efficient way to reduce the amount of lost revenue is to target the root cause of denied claims – something that is very difficult to do without abundant computing resources and highly tuned software.
Organizations that rely solely on humans to extract obscure insights from their data receive much less accurate results at a much slower pace, as the sheer complexity of the of the endeavor exceeds human capacity.
The only way to address these challenges is with machine intelligence. Machines that relentlessly reverse engineer payer behavior, independent of the actual “rules” -- focusing on outcomes, behaviors and subtlety – dispensing with concepts such as “should” and “intent.”
Ayasdi's Denials Management application gives analysts a holistic view of all claims and the ability to drill down and identify the characteristics of denied or rejected claims. These defining characteristics can surface a combination of diagnostic codes, administrative processes, physicians and more that would have taken months of iteration to produce. By using these denials management insights to implement process improvements and drive better performance, providers will minimize denials and lost revenue to the organization.
Take for example, the following denials management scenario that Ayasdi uncovered:
Screening colonoscopy claims are notorious for being rejected on grounds of lack of medical necessity. Providers regularly run afoul of complex coding guidelines. A provider is typically faced with a variety of coding scenarios each of which is often susceptible to inconsistent interpretation of the guidelines by the adjudicator:
Using Ayasdi’s denials management software, it turns out that the “secret” to success involves the ordering of the diagnoses codes in the claim and picking the appropriate "G" code or HCPCS code for the appropriate scenario above. This sequencing of codes triggers the screening colonoscopy as “diagnostic" thereby impacting how much of the payment is covered or deferred to the patient's out-of-pocket expense. Over time, some providers have identified this phenomenon and adjusted accordingly – but this knowledge is inherently local, thus the persistence and magnitude of this - a known problem claim.
To learn more about how we have transformed the denials management process for one of the largest Catholic healthcare systems in the country, ask for a demonstration or drop us a note at firstname.lastname@example.org. Our Machine Intelligence platform has solved complex problems from managing care variation to detecting fraud, waste and abuse for the nation’s largest health insurance payer.