Analytics is the Answer to Compliant Coverage Identification

Advances in technology boost providers’ ability to screen the self-pay patient

Perspective | Brian Andrews
Senior Vice President & General Manager, Revenue Optimization Solutions, Change Healthcare

Andrews’ professional experience includes versatile leadership in IT product development, services operations, marketing, and product management. The last 12 years have been focused exclusively on healthcare revenue cycle IT.

It’s a dilemma that challenges hospitals, physician practices, labs, and durable medical-equipment companies (DMEs) every day: How do you determine if a patient presenting as self-pay or charity care has undisclosed insurance coverage, without compromising compliance requirements?

The liability of self-pay accounts is a growing problem, with providers incurring billions of dollars in losses each year. An American Hospital Association report shows hospitals provided $38.3 billion in uncompensated care (including bad debt and charity care) in 2016 alone.¹

Providers need an aggressive-yet-compliant method for identifying sources of reimbursement in a timely manner, before filing deadlines, and before the only option left for recouping payment is to engage collection agencies — a last-ditch strategy with traditionally low returns.

The Root of the Problem

Capturing complete patient information during the registration process is not fool-proof, and in fact, is fraught with pitfalls. Many providers’ processes do not capture all available data, or patients may inadvertently or willfully withhold information.

Additionally, human errors made in the collection of basic demographics (name, address, Social Security number, insurance, employment) can also mask a patient’s eligibility for insurance coverage or financial assistance. And even when this information is captured and accurate, there is no guarantee that all insurance sources have been identified.

Regardless of the reason, once patient accounts have been labeled uninsured, providers have traditionally had two options: conduct eligibility searches, or accept that a percentage of accounts will inevitably fall to collections, and ultimately, become bad debt.

Advanced analytics and rich data resources can help providers tap new sources of reimbursement while maintaining compliance.

No Phishing Allowed

Because eligibility searches have the potential to expose sensitive personal health information (PHI), the Centers for Medicaid and Medicare Services (CMS) implemented ‘Rules of Behavior’ in 20162 — a policy that both outlines the appropriate use of electronic data interchange (EDI) and specifically prohibits phishing. Other government plans and the commercial-payer industry followed suit, implementing policies mirroring that of CMS.

Now, providers that are phishing for coverage, whether knowingly or inadvertently via a noncompliant solution/third-party vendor, are at tremendous risk of severe penalties. CMS can disable an offender’s National Provider Identifier (NPI) for days or even weeks, preventing the provider from verifying Medicare/Medicaid coverage.

Other government and commercial payers can disconnect providers as well. This situation can result in thousands (or hundreds of thousands) of dollars in lost revenue, in addition to fines for violating the Health Insurance Portability and Accountability Act (HIPAA).

Intelligent Analytics is the Answer

Fortunately, an innovative combination of advanced analytics and rich data resources now enables providers to detect undisclosed coverage and determine eligibility for financial assistance without compromising compliance. Uniform and analytics-driven screening processes can increase compliance with CMS and state regulations, while improving provider reimbursements and reducing operating costs.

Clearance Enhanced Eligibility, a module within the Clearance Patient Access suite of solutions, is one example.* This software uses advanced data mining, machine-learning algorithms, predictive analytics, an expansive network of payers, and more than 600 internal and external sources to identify existing insurance coverage.

Clearance Enhanced Eligibility confirms each insurance profile is one-and-the-same as the demographic profile, and rejects any profiles with identity risk. The solution’s suppression feature rejects an average 40% of accounts due to risk, which helps submit only highly-reliable data to the provider.3 This extreme vetting precludes providers wasting time filing claims with invalid funding sources, and it reduces the risk of PHI-compromise.

An average 8-17% of all self-pay accounts submitted by a provider and 46-52% of accounts submitted by DME and lab providers are found to have valid insurance coverage.4 With appropriate follow-through and timely billing by the provider, eight out of 10 coverages result in net payment.5

In 2017, Change Healthcare helped customers resolve more than $262 million in patient accounts, netting users $94 million.7

While Medicaid and Medicare coverages (including Health Maintenance Organizations) represent 68% of policies identified, commercial coverages represent about 44% of dollars recovered.6

In 2017, Change Healthcare helped customers resolve more than more than: $262 million in patient accounts, netting users $94 million.7 On average, hospitals resolved $4.3 million in patient accounts and received $1 million in reimbursements within the first 12 months of going live.8

Multiple Returns on Investment

Providers that use advanced analytics to detect undisclosed coverage can reap numerous benefits, including improved revenue performance, decreased cost-to-collect on self-pay accounts, and increased patient satisfaction that results from identifying sources of financial assistance. What’s more, some solutions — such as Clearance Enhanced Eligibility — are pay-for-performance, meaning customers are only paying a percent of the net gain.

If you’d like to learn more about Clearance Enhanced Eligibility, complete the Contact Us form at:

or call us at 866-817-3813.

1 Uncompensated Hospital Care Cost Fact Sheet, American Hospital Association, December 2017.
2 CMS HETS Rules of Behavior,
3 Based on 2017 Internal Change Healthcare data from the Coverage Insight™ product.
4-8 Ibid.

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