The Future of Uncompensated Care

Addressing the Impact of Changing Coverage on Patient Revenue

Uncompensated care is changing—and so are hospitals' approach to collections. To help you prepare for these shifts, we've developed a new financial model to project your organization's levels of bad debt, charity care, and net patient revenue under a variety of potential future scenarios.

The nature of uncompensated care is drastically changing. Historically the bulk of bad debt and charity care stemmed from self-pay accounts, but evolving market dynamics are impacting both the degree and the type of coverage in any given market. This places covered patients on the hook for a growing portion of their health care expenditures.

These changes have dramatic implications for uncompensated care and hospitals’ approach to collections. To fully understand the impact of these dynamics, we developed a complex financial model capable of projecting levels of bad debt, charity care, and net patient revenue—among other financial metrics—under a variety of potential future scenarios.

In this white paper, you'll find a detailed explanation of the model along with observations on the changing market, key insights from the research, and strategies to positively inflect patient collections.



Introduction

Currently, the uncompensated care strategy at most health systems focuses primarily on the uninsured (or “pure self-pay”) population. This cohort of patients is largely managed in one of two principal ways. First, for indigent patients, charity care presents an opportunity to support the mission of the hospital and provide quantifiable justification for tax-exempt status for not-for-profit providers. Second, patient bills eventually deemed uncollectible are categorized as bad debt.

While collection efforts focus on large-dollar accounts, most health systems avoid aggressive attempts, not only because in aggregate the amount owed is small relative to net patient revenue, but also because such practices are resource-intensive and politically fraught. As such, patient collections are not an area where most health systems have historically excelled.

Two fundamental forces changing uncompensated care

Yet changes in the health insurance market hold the potential to drastically reshape the nature of both bad debt and charity care. The increase in the degree of coverage under the ACA via the Medicaid expansion and the advent of public health insurance exchanges should reduce the number of uninsured patients, likely reducing both bad debt and charity care, and introducing several implications for the margins and taxexempt status of provider institutions in the future.

Along with an increase in the degree of coverage, there is also a shift occurring in the type of health insurance coverage patients have. Both within traditional employer-sponsored insurance and the exchanges, cost is shifting significantly, making patients responsible for a substantial portion of their health care expenditures.

This transition places a huge burden on providers as well; in aggregate, patient obligations will account for an ever-greater portion of net patient revenue, forcing providers to focus on patient collections. Because hospitals have had little historical success in this area, these obligations are likely to put upward pressure on total uncompensated care.

Blog post: The evolution of patient obligations and bad debt

Given these changes, two questions for providers emerge:

  • What are the cumulative effects of these forces on overall uncompensated care?
  • How are these changes likely to impact hospital and health system profitability and strategy?


Explanation of Financial Model and Methodology

Lumbert Medical Center

To assess the likely impact of both forces on a typical hospital, the Council developed a financial model based on a hypothetical organization—"Lumbert Medical Center"—as a means of testing the impact of the changing dynamics in insurance coverage. The model examines a hospital with a typical payer mix and margins, and subjects it to the following shifts:

  • Uninsured patients going to Medicaid coverage
  • Uninsured patients signing up for high-deductible health plans on the exchanges
  • Commercially insured PPO or indemnity patients moving to high-deductible health plans
  • Increasing deductible levels among all types of patients
  • Propensity to pay among patients of different means
  • Discounting strategies among providers for all types of patient obligations

Net charity discounts

A note on our treatment of charity care: because we wanted to test how these forces could affect both charity care and bad debt simultaneously, we needed a common metric to assess their impact on hospital finances. Although charity care is traditionally reported as a portion of gross patient revenue, that metric does not accurately reflect the true financial impact to providers.

We therefore created a new metric—net charity discounts—which quantifies the impact of care provided either free of charge or with means-based discounts as forgone revenue reimbursed at Medicare levels. This aggregate number, which is expressed, like bad debt, as a percentage of net patient revenue, represents a truer value of charity care in terms of providers’ financial health.

Patient propensity to pay

To accurately project uncompensated care, the model has to predict how much of a patient’s obligation will be met. Hospitals calculating a patient’s propensity to pay consider a range of factors, including payment history, credit score, net worth, income, and insurance plan status. However, using those factors to assess the propensity to pay for an aggregate population is more complex, and projecting the likelihood of payment from patients who have not yet enrolled in a particular plan is even more challenging.

To do so, we examined financial records of nearly 400,000 de-identified patient records from the Advisory Board’s Self-Pay Compass, stratifying the cohort according to potential obligation levels. Historical payment rates were then layered into the analysis to determine how likely patients are to pay, and how much they were willing to pay at different deductible levels.

These analyses, supported by additional research studying patient behavior under HDHPs, lay the foundation for how patient behave within the model.

The current state of uncompensated care at Lumbert

In 2013, bad debt reached 5.13% of net patient revenue (NPR), while net charity discounts totaled 4% of NPR. However, this rate varies significantly by types of service. Looking on a case-by-case basis, inpatient surgical procedures result in a greater amount of bad debt and net charity discounts than inpatient medical or outpatient procedures. This explains why historically hospitals have focused collection efforts on these high-dollar inpatient procedures.

However, calculated as a percentage of net patient revenue from those services, bad debt from outpatient imaging exceeds other areas, while net charity discounts comprise a greater proportion of revenue from the unspecified “other” outpatient category, namely ED services.

When we include total volumes in the analysis, in aggregate, the greatest source of bad debt is in the unspecified outpatient category. This is unsurprising given the fact that ED visits are included here; traditionally many self-pay patients have utilized the ED for all of their health care needs.

In total, bad debt and net charity discounts stemming from outpatient procedures surpass those from inpatient services. Such data shows that while per case, inpatient surgical procedures generate more bad debt per case, the greater opportunity in total lies on the outpatient side.

Insights from the model

The remainder of this white paper projects levels of bad debt and net charity discounts given a variety of scenarios that may occur under coverage expansion. Using Lumbert as a baseline, the model allows us to adjust factors such as the Medicaid expansion, the proliferation of HDHPs, and pricing discounts under the exchanges, among other levers, to project the impact on uncollectible patient revenue.

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