How to Address the Challenges of Eligibility Denials

Author: Christian Carlson


Real-time eligibility (RTE), patient eligibility verification that allows medical staff to electronically verify patients’ insurance coverage for medical treatment, and other electronic verification methods are widely available. These technologies return of-the-moment answers about medical coverage from Medicare, Medicaid and the span of commercial health insurance plans. Their use is meant to decrease eligibility and reimbursement denials.

Eligibility verification is useful at multiple points in the revenue cycle, starting when a patient schedules an appointment, to pre-registration and day-of appointment registration (to proactively catch insurance carrier/plan changes) through to billing post-appointment and collections management. The inflection points are determined by varying business practices and systems capabilities.

“Has your insurance changed?” Almost every patient is asked this when checking in for medical care. But, changed from what? From when? Many can’t remember their insurance coverage details from their last appointment so they simply say “no,” when this may not be true. Even when the answer is yes, eligibility may not be checked prior to service, though it should. Front-end health staff often have to juggle the patient in front of them as well as those on the phone. Working with frequent disruptions can cause inconsistency in requesting insurance cards at each visit. And, when the information is collected, the data is often entered manually which can lead to further errors. In fact, a 2015 ReveCycle Intelligence article states that only 24% of practices check eligibility at every visit.

Across the United States, approximately 25% of all healthcare claims are rejected, and it is believed that 80% are rejected for reasons associated with eligibility verification. Fifty to sixty-five percent of all claims are never resubmitted, which means that providers simply lose this income.

Confounding Factors

Information intake, such as copying insurance cards, and entering that data is still largely manual. Live discussions between staff and patients are still part of the process. Because no one is perfect, these actions are prone to typographical or data omission errors.

Entering correct information at the start of each patient interaction — prior to providing care — is one of the most critical steps in the revenue cycle process. Yet, this step relies on front-end staff who receive the lowest compensation and who may be the least incentivized to be diligent with monotonous work. Add in the nature of interfacing with the public, often with patients who are in pain and may be less than polite, and it can be easy for front-end staff to feel under appreciated quickly. According to a 2018 Medical Group Management Association (MGMA) DataDive Practice Operations report, physician’s offices said they have the hardest time recruiting and retaining non-clinical staff, with front-office staff turnover at 20%. This only adds complications and can lead to errors that cause eligibility denials, often after service has already been completed.

A Vision for the Future

Provider organizations need to remove the dependency on human-entered information that determines coverage eligibility. Freeing up staff time allows them to focus on more challenging and rewarding work, and automating the process can reduce denials.

What if the insurance card could be accurately ‘read’ by a computer? It can. The use of machine learning (ML), a subfield of artificial intelligence that actually learns from inputs, to understand and digitize insurance card information to determine eligibility should become the new normal. Patients are already increasingly utilizing telehealth services and using online portals and apps to schedule their care appointments and communicate needs to providers. Capturing insurance card information via these tools should be the new normal. Accuracy rates will go up, denials rates will decline and the entire healthcare system will become healthier.

Back-end processes will improve as well. For instance, coordination of benefit decisions will be automated. Staff will no longer have to sort through insurance cards and make potentially inaccurate determinations of which insurance is primary and secondary for those with more than one carrier and choices of hundreds of plans. The determinations will be made faster and more accurately. Patients can be contacted directly through the system, as needed.

Of course, there will always be exceptions as healthcare and insurance coverage is a complex, tangled web that requires human expertise to navigate the most difficult parts. For this we need machines and humans to work together.

The solution to navigating exceptions is what we call Unified Automation™, which has the ability to handle the complexities and outliers that ML encounters. The ML aspect of the Unified Automation™ solution runs 24/7, continuously learning at scale. It captures current workflows, analyzes complex data and constructs complex flows or automatically corrects broken ones, which eliminates unnecessary work.

When an inevitable complexity or outlier issue arises that the ML cannot handle, a TeleOps team of revenue cycle management experts is notified, triages the problem and resolves it in real-time. The system learns from the actions they take. Together, humans and machines work to increase appropriate eligibility determinations. This greatly increases accuracy, reduces denials, accelerates cash flow and improves patient satisfaction.

In Summary

It is clear eligibility denials are still pervasive and eligibility remains a critical step to be addressed for increased revenue cycle efficiency and optimized outcomes. Unified Automation™ can have a significant impact on reducing denial rates, increasing accuracy, reducing dependency on our lowest paid and highest turnover staff, increasing patient satisfaction and improving yield and cash flow.