While providers around the country are investing heavily in financial counseling, a large number of patients meriting financial assistance still fall through the cracks. Those being missed are declared bad-debt and sent to collections. Clearly, this is not ideal.
Providers are turning to predictive analytics, or “scores” to determine accounts meriting charity that are either not participating in counseling or not in counseling processes at all. This is a proactive process to ensure all accounts are given fair, similar and equitable review for financial assistance.
Size of the Opportunity
Connance and PARO research found that 20-30 percent of a provider’s bad debt commonly is from guarantors who would qualify for charity classification but slipped through the cracks. The amount of missed charity will vary based on the local market, specific assistance policies, and financial counseling process.
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Missed charity is an issue of increasing concern for the IRS, community leaders and legislators. Form 990 Schedule H has a several questions focused on understanding the size and root cause of missed charity. Health reform includes clear expectations for comprehensive financial counseling prior to extraordinary collections.
Root Causes of Missed Charity
Several structural issues act as barriers to those often most in need. Consumers living in poverty have less education and higher illiteracy than the average household. They often lack stable addresses, are immigrants, or are embarrassed by their situation and prefer to not participate in application processes.
It is estimated that as much as 25 percent of those living in poverty lack access to traditional “banking” resources. This means they are unable to provide financial documentation and they will not be present databases of such information.
Poverty and Credit Scores
It stands to reason that if people living in poverty lack traditional banking relationships they will also lack a credit score. However, the corollary is not true – just because one lacks a credit score does not mean poverty.
Credit scores are really not an income measure, but a delinquency measure. Poverty is not being overextended or spending more than you make but a question of income and household structure.
A patient living on a fixed income without any property and a credit card that they carefully use is a classic example of the difference between credit scores and poverty. They will have a relatively good credit score and also likely be eligible for financial assistance classification. Contrast this with a middle-income consumer who has racked up large bills. They probably have poor credit scores, but would not meet the charity test.
Presumptive Charity Analytics Leading Solution
Presumptive charity analytics is the leading approach to addressing both day-to-day operational issues of missed charity. These are models built specifically to identify accounts eligible for poverty classification using publicly available information.
Providers are using predictive analytics to evaluate accounts that fail to document through standard financial counseling processes. Accounts are scored just prior to bad-debt assignment, after routine active A/R processes are complete as well as all other eligibility and funding sources have been exhausted. Those qualifying for presumptive charity are reclassified and removed from the bad-debt placement file. Those failing to qualify are declared bad debt and are handled as normal.
This is a comprehensive and proactive program to identifying and aiding needy patients. Every account, including those that were missed by or failed to participate in financial counseling, are reviewed using a proactive, consistent and repeatable process. The estimate of missed charity ending up in bad- debt is significantly reduced. Form 990 submissions are both more compelling and more detailed and documented. Bad debt efforts are more focused on accounts with financial responsibility, and focus leads to better performance.
Running in a Matter of Weeks
Adding presumptive charity analytics prior to bad debt is an easy operational process, most commonly using secure file transfer. Accounts to be evaluated are sent in batch files to a scoring vendor. The vendor will score each account and send back a response file. The patient account system grabs the scored file and automatically reclassifies accounts based on the score.
It is also possible to review, at initiation, existing bad-debt inventory and execute a one-time financial adjustment for those identified as presumptive charity eligible.
Within weeks an organization can be proactively reviewing every account going to bad-debt and be confident that they are not missing needy patients.
Steve Levin is Connance CEO and Neil Smithson developed the PARO charity care predictive model.