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E-Brief | Volume 15 | Issue 1 | January 16, 2014
Using a Data-Driven Approach to Develop a Self-Pay Strategy With
many healthcare organizations seeking out innovative and effective
methods to boost their collection rates from self-pay patients,
conducting statistical, in-depth analyses of account data has
emerged as a useful tactic to help leaders identify an optimal
strategy for handling patient balances. Among other things,
understanding the factors that influence a patients likelihood to
pay can make it easier for organizations to identify which types of
accounts to discount, write-off, allot more staff/resources to,
etc., which by extension, can lead to improved financial
performance.
Several studies in particular have looked at size of balances
relative to the likelihood of payment. For example, National
Patient Account Services (NPAS) found that recovery rates
predictably tend to decrease as the patient balance size increases.
NPAS determined that when self-pay balances went from $50 or less
to the $200300 range, the recovery rate decreased by more than 20
percentage points. (As a point of comparison, the decrease in the
recovery rate between balances in the $200300 range and the $9,000
10,000 range appeared to be only around 5 percentage points or so.)
This may be something to consider as providers allocate their staff
and resources, as following up with patients who have $250 balances
may be more vital than contacting those who are only $200 or
less.
A study entitled Healthcare Financial Hardship Limits: Finding
the Tipping Point Where Out-of-Pocket Expenses Place too Great a
Strain on Families, was conducted by TransUnion Healthcare on
behalf of North Shore-LIJ Health System (NSLIJ). The study found
that the healthcare financial hardship limit for NSLIJ's patient
populationwhich also represented the point where recoveries
diminishedwas when out-of-pocket medical costs exceeded 3.5% of a
family's adjusted gross income. A previous study by the Center for
Studying Health System Change (HSC) found similar information,
stating that "financial pressures from medical bills increase
sharply when out-of-pocket spending exceeds 2.5% of family income."
In fact, two-thirds of people with medical bill problems spent less
than 5% of their family income on those medical bills.
It is also important to note that while balance size alone, or
as a percentage of income, may be a useful factor in assessing
patients' ability to afford healthcare costs and their likelihood
to pay, there are other factorssuch as family size and even
frequency or severity of illnessthat could significantly impact how
well patients are able to resolve larger balances. As an anecdotal
example, a previous survey of New Yorkers conducted by the
Community Service Society discovered that, overall, 57% of
individuals found that paying 5% of pre-tax income for family
healthcare seemed "about right," while 27% thought that this amount
was too high. In contrast, 36% of participants with children
indicated that 5% of pre-tax income was too much to pay for family
healthcare. As this study suggests, a number of factors beyond
balance size may play a role in the collectability of an account,
and by extension, decisions related to discounts or assistance.
Furthermore, the credit card industry seems to express a similar
sentiment, with multiple sources conveying the idea that balance
size alone cannot necessarily support an accurate assessment of an
individual's likelihood to repay debtafter all, even cardholders
with higher balances can represent a relatively low default risk
when their past credit history is good. However, because banks'
objectives often differ from hospitals' (i.e., due to interest,
banks have a greater incentive to take on high balances as long as
eventual payment is likely), it is difficult to draw a direct
parallel between the credit industry and healthcare industry in
this regard.
In taking some of the trends and methods listed above into
consideration, providers may be better suited to tailor their
collection efforts for optimal account resolution. By relying on
in-depth analysis of organization-specific data to drive their
collection strategies, such leaders can refine their practices and
policies to better suit the financial needs and behavior of their
patient population.
