Faisal Zain brings a wealth of expertise to the intersection of medical technology and health systems management. As an expert in the manufacturing and deployment of diagnostic tools, he has a unique vantage point on the economic structures that underpin hospital operations. With medical debt remaining a persistent crisis for millions of American households, Zain’s insights into the mechanics of hospital financial assistance programs—and the emerging shift toward automated “presumptive eligibility”—offer a critical perspective on why so many eligible patients still find themselves crushed by bills they shouldn’t have to pay.
The discussion explores the complex landscape of hospital “charity care,” highlighting the administrative barriers that prevent patients from accessing help, such as invasive documentation requirements. It also examines the legislative momentum in states like Oregon and California, where new mandates are forcing hospitals to proactively identify vulnerable patients. Zain breaks down the economic trade-offs of these policies, the role of tax exemptions for nonprofit institutions, and the data-driven methods hospitals use to estimate a patient’s ability to pay without ever seeing a tax return.
The process of applying for hospital financial aid is often described as intentionally cumbersome, requiring patients to provide invasive documentation like divorce filings, bank statements, and tax returns. How do these administrative hurdles fundamentally impact the patient’s ability to access the care they are legally entitled to?
The administrative burden is one of the most significant barriers to equitable healthcare access today. When a hospital requires a patient to submit pay stubs, tax returns, and even divorce filings via fax or in person, they are creating a friction-filled environment that many patients simply cannot navigate. We see in various surveys that a vast number of patients are completely unaware these financial assistance programs even exist, while others find themselves “stymied” by the sheer complexity of the forms. This isn’t just an inconvenience; it has a quantifiable economic impact, with one analysis finding that hospitals and health systems billed patients for at least $2 billion that they likely didn’t owe simply because the relief process was too difficult to complete. By making the process so “invasive,” as some economists have noted, hospitals effectively lower the number of people who successfully receive aid, leaving those patients with unpaid bills that haunt their credit scores for years.
We are seeing a significant shift toward “presumptive eligibility,” where hospitals automatically screen patients for aid. Could you explain the mechanics behind this approach and how it is changing the traditional model of “relief of last resort”?
Presumptive eligibility is a transformative shift that moves the burden of proof from the patient to the institution. Instead of waiting for a patient to struggle through a multi-page application, the hospital proactively uses public records, ZIP code data, and consumer credit tools to estimate a household’s income. This trend was largely catalyzed by the Affordable Care Act, which requires nonprofit hospitals to make “reasonable efforts” to identify eligible patients before taking aggressive collection actions like suing or selling debt to agencies. Since the federal regulations took full effect in 2016, we’ve seen a massive adoption rate; in the first year, about 70% of tax-exempt hospitals reported using some form of proactive screening, and as of 2022, that figure rose to nearly 90%. While some hospital executives still view charity care as a “relief of last resort” to be used only after every other payment source is exhausted, these automated systems are beginning to flip that script by wiping out debt before a bill is ever even generated.
In states like Oregon, we have seen a dramatic increase in the number of patients receiving aid without an application. What can we learn from the specific data coming out of these early adopters regarding the effectiveness of mandatory screening?
Oregon provides a fascinating case study in how legislative mandates can rapidly scale financial relief. Starting in 2024, the state required hospitals to screen any patient who owed more than $500, as well as anyone who was uninsured or on Medicaid. The results were immediate and profound: in 2025, approximately 80% of Oregon patients who received financial help never had to fill out a single application form. This proves that the data necessary to provide relief already exists within the system; it just requires the political and institutional will to use it. Interestingly, the Oregon legislature has already moved to refine this by raising the screening threshold to include patients who owe at least $1,500 for a single visit, showing that this is an evolving strategy. By removing the “hoops” patients have to jump through, these states are ensuring that financial aid actually reaches the low-income and middle-income families it was designed for.
Large health systems like Ascension and Christus Health have different ways of communicating their screening criteria to the public. How does the lack of transparency in these policies affect the “safety net” for patients who don’t live in states with strict mandates?
The lack of transparency creates a “geographic lottery” for patients. For example, some hospitals owned by Ascension—one of the largest Catholic systems in the country—provide very vague guidance, stating only that they might screen patients who have a “sufficient unpaid balance.” Similarly, Christus Health’s policy is to screen only after all other payment sources are exhausted, which means a patient might be subjected to months of stress before help arrives. In states without mandates, eligibility criteria are often “buried in official policies” that are nearly impossible for the average person to decipher. Some institutions have very niche criteria, like Christus Health writing off bills for those in religious orders who have taken a vow of poverty, while others use “propensity to pay” scores to decide who is worth pursuing. Without clear, public-facing standards, patients are left in the dark, often unaware that their income level—which in states like North Carolina or Maryland can be double the poverty threshold and still qualify for free care—makes them eligible for total debt forgiveness.
Nonprofit hospitals receive an estimated $24 billion in tax exemptions annually in exchange for providing community benefits. Is the current output of financial assistance, which can range from less than 1% to over 7% of expenses, a fair return for the public investment?
This is the central question of the “hidden help” debate. Taxpayers essentially subsidize these institutions by allowing them to bypass income, sales, and property taxes, which KFF estimated at $24 billion in 2020 alone. When you see a hospital spending less than 1% of its yearly expenses on financial assistance, it’s hard to argue that the community is getting a fair return on that $24 billion investment. While for-profit and government-owned hospitals have different subsidy structures—often receiving smaller payments for treating high numbers of Medicaid patients—the nonprofit sector is under the most scrutiny. The discrepancy between the tax breaks received and the actual charity care provided is exactly why we are seeing states like California and Illinois step in with stricter requirements. If the public is footing the bill through lost tax revenue, there is a reasonable expectation that the “safety net” should be proactive and robust, rather than a “last resort” hidden behind a wall of bureaucracy.
When hospitals use “propensity to pay” scores and consumer credit data to screen patients, what are the risks that these tools might be used to maximize collections rather than provide relief?
The risk is that these data tools can be a double-edged sword. On one hand, they allow a hospital to see that a patient lives in a high-poverty ZIP code and should be given a 100% discount automatically. On the other hand, “propensity to pay” models can identify people who are likely to pay their bills even if they are low-income, perhaps because they are traditionally diligent or have a small amount of savings. This is exactly why states like California and Oregon have banned the use of “propensity to pay” scores. The concern is that if a hospital knows a patient will pay even if it causes them financial ruin, they might skip the screening process to protect their bottom line. We want these technologies to be used to identify vulnerability, not to “cherry-pick” which low-income patients should be pressured into payment plans versus who should be granted charity care.
What is your forecast for the future of hospital financial aid and the expansion of these auto-enrollment programs?
I believe we are entering an era of “mandatory transparency” and automation. Within the next few years, the model pioneered by Oregon and the upcoming 2027 requirements in California will likely become the blueprint for federal policy. We will see a shift where screening happens “before” the first bill is even printed, rather than as a reactive measure after a patient has been sent to collections. As data integration improves, hospitals will have fewer excuses for “stymieing” patients with paperwork. The $2 billion in incorrect billing we see today will likely decrease as “presumptive eligibility” becomes the standard operating procedure for any hospital receiving federal tax breaks. Ultimately, the goal will be to make medical debt a rare exception for low-income households rather than a predictable outcome of a hospital visit, ensuring that “charity care” finally functions as the accessible safety net it was always intended to be.
