I ran across an article the other day on how CFOs want to reduce costs but feel they don’t have the data or resources to do so. As a CFO myself, I found the article quite interesting. Financial professionals wanting more data? I’m hardly shocked – it’s what we do, take data inputs and do our best to predict and forecast future financial outputs. As the article suggested, CFOs don’t really need more data. The data is there, whether it be in claims systems, EHR’s, cost accounting systems, etc. It just needs to be accumulated, organized and analyzed to yield insights to better serve the organization.
This becomes even more critical at health systems where the majority of revenues come from governmental programs, such as Medicare and Medicaid. In those environments, there are fewer options to bolster revenue thereby increasing the need to focus on cost – and ways to judge how effectively capital is being used throughout the organization.
An area we’ve been working on with healthcare providers that has given insight to saving costs is claim denials management. First, let’s recognize that not getting paid for services rendered is frustrating to say the least. The fact that providers then have the increased costs of small armies to “fight the good fight” to work with internal clinicians and coders and external payers to prepare appeals that support and justify payment only exacerbates the frustration.
Our tool uses predictive analytics to score and rank denials in the order in which they should be worked, based on the propensity that the denial will get overturned. A variation of the Pareto principle applies – 80% of claim recoveries stem from working only 20% of the denials. The challenge lies in which 20%. Our solution actually shows you the 20% that will yield the greatest return. And, it’s not always the highest dollar value that ranks in that top tier.
Additionally, focusing on the right denials to appeal generates two quantifiable benefits:
A critical component of success is that we use your data to provide tailored recommendations based on your claims and denial experience, not a generic algorithm that only produces generic results. Further, we work within the constructs of your current systems and workflows to minimize time and costs. We analyze hundreds of predictive elements in the data – not just a few that other models often focus on. We analyze it all to ensure reliability and to maximize your return.
The article offers up a few important findings, unfortunately none of which are inherently positive. As finance leaders we want more data, more reliability, more tools, so we’re able to do our jobs and serve our organizations better. Issues like becoming more agile, increasing forecasting quantity while decreasing time budgeting, and searching for cost reductions are all huge and complex initiatives alone. We can’t hope to conquer them all simultaneously, nor can we ignore them.
It reminds me of an old saying: How do you eat an elephant? One bite at a time. I’d encourage you to consider speaking with us so we can help you take the first bite out of your cost reduction initiatives.