Is It Bad Debt Or Charity Care? The Right Way To Measure Uncompensated Care
As part of the Affordable Care Act of 2009, new requirements were introduced relating to community benefits for nonprofit hospitals to meet in order to qualify for 501(c) (3) tax-exempt status. Following a robust industry comment period, final IRS regulations on this issue were released in late 2014. Central to the finalized requirements are expectations and requirements regarding how hospitals’ uncompensated care is split between bad debt and charity care and how accounts are to be handled in each group. Notably, given the new regulations, hospitals can and should deploy predictive analytics to avoid inappropriate placement as well as to determine the correct way to measure and report their bad debt and charity care information.
By Lori M. Jones, chief revenue officer, Connance
“If you’re not measuring the right things to begin with, you’re not going to get better results by measuring them more accurately.” — Peppers and Rodgers, Extreme Trust
Now that the Affordable Care Act’s community benefit requirements for nonprofit hospitals have been finalized, CFOs and revenue-cycle managers are committed to deploying more comprehensive and effective ways to review uncompensated care and report the portion related to bad debts and the portion related to charity care. But getting a clear picture of what to measure — and how to do it — isn’t always easy. Assigning patient revenue to bad debt as opposed to charity requires a specialized measurement tool because many patients simply don’t declare themselves to be one or the other.
Purpose-built healthcare charity predictive analytics is the missing link. These predictive analytics help hospitals identify who is likely qualified for charity classification and therefore know how to separate bad debt from charity and conform to the IRS regulations as well as demonstrate forcefully their commitment to their local community.
The IRS Form Every Nonprofit Hospital Knows
Since 1969, tax-exempt hospitals, which today account for about 78 percent of U.S. hospitals, had to abide by a general requirement to engage in activities that benefit the communities they serve. The Internal Revenue Service left it up to the hospitals to determine what was a community benefit and how it was to be measured. All that changed in 2009 with the passage of the Affordable Care Act (ACA).
Now, under the ACA, all tax-exempt hospitals must complete and submit the entire IRS Form 990 Schedule H, which requires hospitals estimate the amount of charity care in their reported bad debt. Schedule H requires hospitals:
- summarize their charity care policies
- document their community benefits and community building programs
- identify how they meet community healthcare needs
- describe other activities or characteristics the IRS associates with tax-exempt status and/or believes important to disclose to the public
- clearly distinguish between charity care and bad debt
Many of these requirements are fairly straightforward, but the devil is always in the details. For instance, Part III, Section A, line 3 states, “Enter the estimated amount of the organization’s bad-debt expense attributable to patients eligible under the organization’s charity care policy.” Hospitals are to value bad debt (at cost) for patients “for whom sufficient information was not obtained to make a determination of their eligibility” for charity care — using any reasonable method.
501(R) Ups The Ante To Preserve Tax-Exempt Status
An additional ACA requirement, which went into effect this year, changed the status quo and put a hospital’s charity care policy and process under an operational spotlight. Known as 501(r), this condition requires hospitals make “reasonable” efforts to determine whether or not a patient is eligible for charity care before engaging in “extraordinary collections” against the individual. It is no longer simply acceptable to estimate the share of bad debt that is likely charity, but non-profit facilities must now proactively keep charity out of bad debt in the first place. Failure to fully adhere to the Form 990 and 501(r) requirements can result in a hospital’s losing its tax-exempt status and the Medicare and Medicaid subsidies that go with it.
Some communities have already latched onto the new regulations and begun challenging hospitals’ tax exemption because of their excessive debt collection activities and inability to sufficiently identify charity care patients. A New Jersey tax court ruled Morristown Medical Center is to lose its property tax exemption, in large part because of their overly aggressive debt collection from poor patients.
Following that ruling, 35 other NJ municipalities filed tax appeals against 35 nonprofit hospitals. In Washington, a class action suit against Northwest Hospital & Medical Center claims the hospital “fails to properly screen poor patients eligible for charity care” and sent their debts to collection instead. Not only were those actions outside the ACA requirements, the lawsuit claims they were also in violation of the state’s Charity Care Act.
Identifying Charity Care Amid Thousands Of Accounts
The challenge lies in finding out who qualifies for charity care within the steady flow of patient accounts — literally hundreds or thousands of new accounts every day flowing into the hospital.
While many hospitals are streamlining documentation requirements associated with the charity care eligibility process, patients fail to fill them out. The reasons are as varied as the patients themselves: there may be a language barrier; patients may lack insurance (or have insurance, but with a high deductible); or patients are unaware such an option exists. According to Barbara Tapscott, vice president of revenue management at Geisinger Health System, in a recent Pittsburgh Post-Gazette article, “Some people are just plain embarrassed and don’t apply. Or they may think on their own, ‘That doesn’t apply to me.’”
The problem is rapidly escalating and becoming more complicated with the proliferation of high-deductible insurance plans. Patients who are insured are facing sizable bills of hundreds or thousands of dollars. Classically, insured patients would not be a priority for charity qualification; however, the new norm is many of these patients could qualify for some charity relief based on income and asset tests.
Hospitals are caught in a classic catch-22: tax-exempt status is dependent on how well you screen patients for charity care, but the qualified patient may not emerge until after care is provided and billed or never even emerge, hiding amid the thousands of other patient accounts. Many patients look insured but in fact are floundering with medical bills.
Presumptive Eligibility An Opportunity For Predictive Analytics
To meet this challenge, many leading hospitals are turning to predictive analytics to make valid assumptions about who is likely to be eligible for charity care; utilize these insights to prioritize counseling efforts and regulate placement through collection; to add depth and consistency to public reporting; and, over time, to diagnose and improve the overall financial counseling process.
With presumptive charity analytic technology, hospitals combine patient demographic information provided at registration with additional information from third-party databases to generate a view as to the patient’s likelihood of living in poverty as defined by the hospital policies. Once patients are evaluated, hospitals are able to utilize the prediction to guide financial counseling and, with program enrollment teams, help patients get covered or, in the unsuccessful effort, to grant charity even though documentation might be missing.
Presumptive Charity A Specialized Challenge
Presumptive charity models are a special class of analytics, substantially different than what banks and traditional consumer lenders utilize because the issue is different. Banks and lenders focus on a consumer’s ability to repay a loan, which is primarily a delinquency issue. They utilize models tied to broad income estimates and focus on questions of repayment risk.
Hospitals and health providers are not assessing delinquency, but considering a fundamental issue around the patient’s income situation relative to poverty. Whether the patient would pay the bill is not the issue according to government regulations, but simply the extent to which a patient lives in poverty. Moreover, most hospital policies define income brackets at which charity is granted as relatively narrow so the typical error rates on estimated income can swing a patient’s eligibility significantly. Finally, people living in poverty tend to have nontraditional financial profiles and lack detailed credit-bureau files, substantive bank statements, and tax files.
Better presumptive charity models are calibrated to the local market and the hospital’s charity care policy as well as being a way to test eligibility using multiple independent models, one of which is sociodemographics. Research has shown sociodemographic factors correlate to poverty and are more accurate than income estimates. Using multiple tests ensures process integrity as well as avoidance of over or under qualification. Better models will also focus on third-party data sources that match this population’s footprint so they are not credit-bureau focused or reliant on bank databases.
“Hospitals need to be proactive in responding to 501(r) because most organizations are delivering on their commitment but are challenged to prove it,” says Steve Levin, Chief Executive Officer of Connance. “Presumptive charity tools, which incorporate data science, are a great solution to this problem and, properly deployed, applicable across the patient life cycle. We have seen providers turn this issue into a vehicle for improving their local connections and ultimately improving the health of their service areas.”
Applying Charity Analytics For End-To-End Transformation
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Nonprofit hospitals are, in many ways, a public trust. Accurately measuring their community benefit builds confidence in the institution and ensures they will continue to thrive, no matter what challenges a changing healthcare payment landscape may bring.