Big data and its related technology solutions are making a major impact on industries all over the world – driving smart decisions, supporting shifts in strategy, and providing new insights never before uncovered. In an industry like healthcare where the adoption of technology is typically behind the curve or focused more on the patient care innovation side of technology – executives would be well-suited to think differently.
While advanced analytics can help in many areas of a health system’s revenue cycle, let’s look specifically at the cost of denials. According to a 2017 report issued by Change Healthcare, here are some staggering statistics related to denials.
• Denials cost the average health system an estimated $4.9M per year.
• Approximately 9% of claims submitted are initially denied.
• Hospitals may rework up to 3% or more of their net patient revenue due to denials.
• Nationwide, hospitals may spend $9B in administrative costs to recover denied revenue.
• In 2016, $262 billion out of $3 trillion submitted in claims was denied in hospitals across the US.
These few statistics help paint the picture of the massive potential health systems have when it comes to better denial management. While there are numerous implications, we’ll explore the top two ways using data can help reduce the impact of denials on your bottom line.
Predict Denials & Optimize Claims Submissions – Using technology-enabled solutions that employ advanced analytics, healthcare organizations can evaluate their claims data before it’s even submitted. Advanced technologies can predict whether a claim will be denied – offering detailed information on why it may be denied so action can be taken prior to dropping the charge. (For example, if prior authorization was needed, getting this cleared up before the charges are submitted can allow staff to get the appropriate documentation/authorization before the charge is submitted.) This can help healthcare organizations not only save lost revenue that could have been denied but can save significantly in the administrative costs required to manually work denials on a case-by-case basis weeks or months after the encounter.
Identify Trends & Opportunities for Improvement – Looking at large amounts of data in aggregate is one of the best ways to identify trends and opportunities for improvement that could only be identified anecdotally with manual denial resolution processes. Big data and machine learning can often unearth key opportunities that would have never been identified otherwise. That’s because computers can evaluate data such as claim adjustment reason codes (CARCs) and remittance advice remark codes (RARCs) in aggregate to help health systems analyze the rationale behind denials. Using this information, systems can identify process breakdowns, opportunities for education, gaps in policy adherence, etc. Diving deeper into this data can help systems identify clinical areas or departments that appear to be struggling with certain processes – which can drive big gains when education and awareness have a significant impact on reducing denial rates.
The potential impact of using data analytics for reducing the financial impact of denials is proven – and can significantly help improve your revenue cycle. GeBBS team of experts understand how to accurately analyze account history, appeal denied claims and ensure a timely turnaround to recover and close out A/R. To learn more about our technology enabled RCM solutions visit: www.gebbs.com
