Improving charity care classifications: a guide for revenue cycle leaders

by Experian Health 6 min read August 19, 2024

“For too many, coverage is either unavailable or insufficient.” This is the harsh reality the American Hospital Association highlighted in a recent statement to the US Senate, urging action to address growing medical debt. Despite efforts to expand insurance coverage, hospitals continue to lose billions of dollars through unpaid bills. The statement notes that hospitals provided over $42 billion in uncompensated care in 2020 alone.

Guiding low-income patients to appropriate charity care programs can mitigate a significant portion of this uncompensated care. Unfortunately, many eligible patients are either unaware of these options or choose not to apply, causing hospitals to waste time and money chasing bills from people who cannot afford to pay. Helping those patients find and apply for financial support is critical to reducing bad debt.

But that’s a challenging prospect without automation. Presumptive screening with Patient Financial Clearance offers a faster route to reliable charity care classifications and a reduction in uncompensated care.

What is charity care?

Charity care programs provide free or discounted healthcare to patients who can’t afford to pay their bills, covering medically necessary inpatient and emergency room services. Typically, programs offer full or partial discounts to uninsured patients, but those with insurance may be entitled to assistance if their plan doesn’t cover their care. Eligibility depends on the hospital’s financial assistance policies and relevant state regulations.

Hospitals do not expect to be reimbursed for charity care, though tax exemptions and government funding may offset some of the cost. In this way, charity care is distinct from “bad debt,” which refers to unpaid patient bills that hospitals expect to collect.

Frustratingly, too many accounts that could have been eligible for charity care are written off to bad debt—perhaps because patients don’t realize they’re eligible, don’t know how to fill out the application, or feel embarrassed to seek help. The problem is further exacerbated by the growth in high-deductible and “skinny” health plans, as patients without sufficient coverage assume support is only for the uninsured.

Several states are tackling medical debt by bolstering charity care programs. For example, North Carolina plans to boost federal payouts to hospitals that agree to waive medical debt for low-and middle-income patients. In Milwaukee, County Supervisors are taking a preventive approach, using income data to automatically enroll at-risk patients into charity care programs to stave off bad debt before it takes root, in a process known as presumptive charity.

What is presumptive charity?

Checking eligibility and helping patients apply for charity care is predictably form-heavy. Patients must provide tax returns, pay stubs, and bank statements to confirm their household income and financial status. Manual reviews are time-consuming for providers, while the overall experience can feel intrusive or confusing to patients.

Presumptive charity screening expedites charity care checks by automatically screening patients for financial assistance eligibility. It uses automation and data analytics to quickly evaluate the patient’s credit information, financial data and demographic details to make a ‘presumptive’ determination of eligibility for charity care, regardless of coverage status.

Better charity care classifications reduce bad debt

Automated charity care checks mean more patients will be classified correctly so hospitals can confidently seek reimbursement from the appropriate source. This offers several advantages, such as:

  • Reducing bad debt: Presumptive charity screening results in fewer eligible patients missing out on financial support, so they’re less likely to be sent bills they can’t pay.
  • Increasing efficiency: More accurate screening allows staff to cut time spent on administrative tasks and stop chasing collections from patients who are unlikely to be able to pay.
  • Expediting classification decisions: Automation means eligible patients don’t have to wait for long periods to find out if they’ll get financial support, which is especially important in urgent and high-volume services, such as emergency departments and large hospital systems.
  • Improving the patient experience: Speedy systems with fewer forms reduce the patient’s involvement to a minimum, contributing to a more convenient and compassionate financial journey.
  • Maintaining compliance: Hospitals comply and maintain their non-profit status by providing charity care to their community.
  • Supporting patients: Providing accessible healthcare to those who are low-income and are most vulnerable.

How can Patient Financial Clearance help providers improve charity care classifications?

Patient Financial Clearance (PFC) is a presumptive screening tool designed to help providers quickly determine which patients may be eligible for financial assistance. It then connects those patients with relevant charity care programs and automatically enrolls them, or establishes tailored payment plans for the amount they owe based on their financial situation.

First, Patient Financial Clearance triggers automatic checks before or at the point of service to rapidly assess whether the patient qualifies for Medicaid, charity care or other financial assistance programs. It uses Experian Health’s superior data and analytics to accurately estimate the patient’s income, household size and Federal Poverty Line (FPL) percentage, and it calculates a Healthcare Payment Risk Score to predict their propensity to pay. Unlike alternative models which are built to estimate incomes for consumers with higher incomes, PFC’s income estimates are optimized to predict incomes below 400% of the FPL.

Then, if patients are likely to be eligible for charity care, the tool pre-populates application forms and initiates auto-enrollment to reduce staff manual input and the risk of errors.

There needs to be a prompt process to help patients who do not qualify for charity care manage their bills. For patients who do not qualify for charity care, there also needs to be a prompt process to help. Patient Financial Clearance recommends optimal payment plan amounts per the organization’s terms and policies. Staff can pull up summarized and detailed views of the patient’s credit history and custom scripts to guide financial counseling discussions.

Alex Liao, Product Manager for Patient Financial Clearance at Experian Health, explains how better charity classifications help reduce bad debt and increase collections:

“The obvious benefit is that clients can accelerate the charity care application process and ensure eligible patients get assistance quickly. However, having a more accurate picture of patients’ financial needs offers wider benefits across the revenue cycle: it ensures each patient account is handled appropriately to increase upfront collections and reduce bad debt. Those with a low ability to pay receive a payment plan they can afford, while those with a greater capacity to pay are not just paying the minimums.”

Patient Financial Clearance in practice

See how UCHealth used PFC to create a more streamlined approach to charity care classifications, resulting in:

  • $26 million in disbursed charity care.
  • More than 1,700 patients covered.
  • 600 charity cases closed in one month alone (August 2023).

To hear more about how automating charity care classifications with Patient Financial Clearance could help your organization reduce bad debt, contact us today for a demo.

Related Posts

Andy’s New WP Workflow Test Article Using Quick Edit

Key takeaways: Revenue cycle teams can use automation to reduce repetitive work and apply AI where data-driven prediction, matching or prioritization can improve a workflow. Experian Health’s 2025 State of Claims survey found that 41% of providers now face denial rates of 10% or higher, while 68% say submitting clean claims is more challenging than a year ago. Patient Access Curator™ (PAC) uses AI to support front-end data validation and insurance discovery, while AI Advantage™ helps teams predict denial risk and prioritize denial follow-up. Artificial intelligence (AI) and automation can support administrative work in healthcare. In the revenue cycle, teams depend on accurate information, timely decisions and efficient follow-up to keep claims moving. In revenue cycle management, AI and automation can help organizations reduce manual checks, find data gaps, predict denial risk and prioritize work queues. These tools are most useful when they support staff judgment, payer expertise and compliance oversight. They can handle repetitive, data-heavy tasks so staff can focus on exceptions and decisions that need human review. In 2023, McKinsey & Company reported that research suggests effectively deploying automation and analytics could eliminate $200 billion to $360 billion of spending in U.S. healthcare. For revenue cycle leaders, the practical question is where to apply those capabilities first. The case for applying AI and automation in healthcare Revenue cycle teams juggle many daily tasks. Staff collect and verify patient information, confirm eligibility, identify the right payer, submit clean claims, monitor status, work denials and manage collections. Small data gaps at the beginning of the process can create downstream rework and delays. Rework also consumes staff time, adding to these operational pressures. As costs rise and revenue cycles tighten, there is increasing pressure to do more with less. Experian Health’s 2025 State of Claims survey found that 54% of providers say claim errors are increasing and 90% of claim denials are reworked with at least some human review before resubmission. Providers are also managing broader financial and administrative pressures. The American Hospital Association has reported that prior authorization requirements, claim audits, denials and other payer policies add administrative burden and cost for hospitals and health systems. These requirements also consume staff time to appeal denials and manage payer processes. AI and automation are different but complementary. Automation follows defined rules to complete repeatable work. AI models can identify patterns in data, predict risk and help teams decide where to focus attention. When used together, they can support more consistent revenue cycle workflows. How AI and automation can support revenue cycle workflows Revenue cycle management automation and AI are most useful when tied to a specific workflow and a measurable operational problem. The goal is to help teams act earlier, reduce avoidable rework and focus staff time where judgment is needed most. For example, automation can complete rule-based eligibility checks. AI can help identify claims with a higher likelihood of denial. In insurance discovery workflows, AI can also help identify coverage that wasn’t captured at registration. When these tools fit into existing workflows, they can support more consistent decisions and reduce manual work. Three practical applications include: 1. Improving front-end data quality with Patient Access Curator Patient and coverage information collected early in the revenue cycle can affect downstream claim outcomes. Incomplete or outdated demographic details, eligibility responses, coordination of benefits or Medicare Beneficiary Identifier information can create problems that lead to claim delays or denials later in the cycle. Experian Health’s Patient Access Curator helps prevent claim denials by validating demographics, eligibility, insurance discovery, coordination of benefits and Medicare Beneficiary Identifier data in seconds. PAC’s AI and machine learning capabilities help improve match accuracy, coverage sequencing and data confidence by writing the validated data back into the host system and sequencing payers before the claim is created. This automates work that would otherwise require manual coverage checks. 2. Using insurance discovery to find coverage not captured at registration When active coverage isn’t identified during registration, claims may be delayed or submitted with incomplete insurance information. Insurance discovery looks for coverage that may not have been captured during registration. Patient Access Curator includes insurance discovery as part of its front-end validation workflow. It can help identify and correct missing or incorrect insurance information so claims can be submitted with more complete coverage data. 3. Using AI to prevent and prioritize denials Even with strong front-end processes, some claims still require additional attention. AI can help claims teams decide which claims to review before submission and which denials to work first after payer response. Experian Health’s AI Advantage supports two denial management use cases:1. AI Advantage – Predictive Denials uses a client’s historical claims data and Experian’s knowledge of payer rules to identify claims with a high likelihood of denial before submission so teams can take corrective action.2. AI Advantage – Denial Triage uses AI to segment denials and identify those with the highest potential for reimbursement. This approach can help teams prioritize with more confidence. Rather than treating every claim or denial the same way, teams can use predictive models to focus on the work that needs the most attention. Potential benefits of AI and automation in the revenue cycle A high-performing revenue cycle depends on timely, accurate and consistent work. AI and automation can help providers modernize that work without losing the expertise of the people who manage complex payer and patient situations every day. When applied to the right workflows, these tools can help organizations: Reduce manual data searches that take staff away from higher-value work Improve front-end data quality before claims are created Identify missing or incorrect coverage information earlier Spot claims that may be at higher risk of denial Prioritize denied claims by potential reimbursement Reduce rework caused by inaccurate or incomplete information Give staff more consistent information for follow-up decisions A focused AI strategy starts with the workflow problem, uses data that is relevant to that problem and keeps staff in control of judgment-based decisions. A more proactive approach to revenue cycle management Revenue cycle teams can move from reactive work toward a more proactive approach: catch errors earlier, validate coverage before claims are created and prioritize the claims and denials that need the most attention. Experian Health offers revenue cycle solutions that use AI and automation in targeted ways to support front-end data quality, reduce rework and manage denials. Patient Access Curator supports registration and coverage validation, while AI Advantage supports denial prediction and triage. Learn more about Experian Health’s Patient Access Curator and AI Advantage.

October 2, 2026 by Andy.Monte@experian.com
Experian Health ranked #1 in Best in KLAS for 2025

Experian Health is very pleased to announce that we've ranked #1 in the 2025 Best in KLAS: Software & Services report, for our Contract Manager and Contract Analysis product, for the third consecutive year. Contract Manager, when paired with Contract Analysis, empowers healthcare providers by ensuring payers comply with contract terms, identifying and recovering underpayments, and arming them with real claims data to negotiate contracts. This enables providers to negotiate more favorable terms and maintain financial stability.  Clarissa Riggins, Chief Product Officer at Experian Health, says, “In the ever-evolving healthcare landscape, our Contract Manager solution has once again been recognized as the #1 Revenue Cycle Management tool by KLAS for the third consecutive year. This prestigious ranking underscores the significant value our solution delivers to our clients by identifying underpayments and facilitating revenue recovery. We are honored to continue supporting our clients with innovative solutions that drive financial success and operational efficiency.”  Learn more about how Contract Manager and Contract Analysis can help your healthcare organization validate reimbursement accuracy, recover underpayments and boost revenue.   Learn more Contact us

February 5, 2025 by kelly.nguyen
How to increase patient engagement

Learn how providers can increase patient engagement, why it matters and key strategies that deliver improved end-to-end patient experiences.

January 30, 2025 by Experian Health

Spotlight test

Spotlight Description

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Sticky Subscribe Title

Sticky Subscribe Description
Sticky Subscribe

Testing Spotlight Paragraph block

Testing the spotlight block header

Archive Testing

Categories