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White Paper
Analytics is the Answer to Compliant Coverage Identification
Summary
Using advanced analytics and rich data resources can help boost providers’ ability to screen the self-pay patient. Read this white paper to learn:
- How to uncover hidden coverage and determine eligibility for financial assistance without running into any compliance issues
- How providers can reduce the risk of PHI-compromise
- Three common scenarios where data can help you better understand patient needs
- Rich data resources that can help providers tap into new sources of compliant reimbursement
White Paper | Eddie Persinger
Director, Operations, Change Healthcare
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. The most recent American Hospital Association report on uncompensated care shows hospitals provided $42.67 billion in uncompensated care in 2020 (including bad debt and charity care).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 foolproof, and in fact, it 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 as 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 & 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 who 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.
The Coverage Insight™ solution from Optum is one example. This post encounter software uses advanced data mining, machine-learning algorithms, predictive analytics, an expansive network of payers and internal and external sources to identify existing insurance coverage.
Coverage Insight confirms each insurance profile is one and the same as the demographic profile. It also 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. This extreme vetting saves providers from wasting time filing claims with invalid funding sources, and it reduces the risk of PHI-compromise.
An average of 8%-17% of all self-pay accounts submitted by a provider and 46%-52% of accounts submitted by DMEs and lab providers are found to have valid insurance coverage. With appropriate follow-through and timely billing by the provider, 8 out of 10 coverages result in net payment.4
In 2020, Coverage Insight cleared more than $800 million in annual patient balance and recovered $241 million in annual provider recover.6
While Medicaid and Medicare coverages (including Health Maintenance Organizations) represent 68% of policies identified, commercial coverages represent about 44% of dollars recovered.5
In 2020, Coverage Insight cleared more than $800 million in annual patient balance and recovered $241 million in annual provider recovery.7
Truth in Analytics
Both the ability and reliability of advanced analytics help providers gain insights that patients are unable or unwilling to provide. Consider these three common scenarios where data reveals more than the patient conversation:
- During registration, a patient shares that he doesn’t have insurance coverage. Using his demographic profile, advanced analytics, and data-source searches, it’s revealed the patient has undocumented Medicare coverage.
- An intake conversation doesn’t reveal whether the patient qualifies for assistance. Data and patient modeling confirm the patient’s residence, estimated income, family size, and placement on the federal poverty scale. The patient is eligible for Medicaid and is automatically assigned financial assistance.
- In a third scenario, a patient shares she doesn’t have insurance coverage but expresses her ability and likelihood to pay the balance owed. An analysis of multiple private and public data sources uncovers the patient’s full demographic and financial profile, qualification of eligibility for assistance programs, and propensity to pay. Staff share charitable resources with the patient and offer to assist with applications.
Multiple Returns on Investment
Providers who use advanced analytics to detect undisclosed coverage can reap numerous benefits, including optimized revenue performance, decreased cost-to-collect on self-pay accounts, and increased patient satisfaction that results from identifying sources of financial assistance.
If you’d like to learn more about Coverage Insight, complete the Contact Us form or call us at 866-817-3813.
1 https://www.aha.org/news/headline/2021-01-21-aha-hospitals-provided-416-billion-uncompensated-care-2019
2. CMS HETS Rules of Behavior, https://www.cms.gov/Research-Statistics-Data-and-Systems/CMS-information-Technology/HETSHelp/Downloads/
EligibilityTransactionSystemInquiriesRulesofBehavior.pdf
3. Based on 2017 internal Change Healthcare data from the Coverage Insight product
4. Ibid.
5. Ibid.
6. Based on 2020 internal Change Healthcare data from the Coverage Insight Product
7. Based on 2020 internal Change Healthcare data.
