Automated Insurance Discovery for Proactive Revenue Cycle Management
I asked what information came back when she did her verification, and she said, “Either verified or not.”I recently took my son to see his doctor for a rash. During registration, I asked the registrar if she verified that the information I gave her matched what my insurance provider had on file and she said she did.
When I asked if she got any additional information in the event of a failure, like why verification failed or what information the payer had that was inconsistent, she replied, “No. I have to dig that up myself.”
To me, this is one example of an inefficient insurance verification process.
If diligent verification is being done for every patient, I would think we could at least provide them with the reason for failure and the information the payer had that was different from the provider. An even more inefficient process is not doing verification at all, which I see more often than I want. Both situations create additional work for the patient account team once they submit a claim and get a denial.
You may have heard me mention the benefits of demographic and automated insurance verification on every single visit. This process should happen no matter how confident you are that the information hasn’t changed. There are also additional steps you can take to make sure you are in the best position to receive payment for your services rendered.
Self-Pay to Bad Debt
Most organizations define bad-debt as revenue that has been written off due to non-payment for services.
A shocking 93.94% of true self-pay revenue will be written off to bad-debt while 84.49% of self-pay after insurance will be written off. In most organizations, that equates to millions of dollars while in others it could mean billions (Gooch, 2017).
This brings the uninsured conversion KPI into extreme focus. Hospitals that can bring up their uninsured conversion rate will mitigate the impact of self-pay revenue being written off to bad-debt. Furthermore, finding additional funding sources for self-pay after insurance will further bolster collection efforts and prevent more dollars from hitting the bad-debt bucket.
The Rise of Underinsured and Uninsured
There has been a significant increase in high-deductible plans and out-of-pocket costs. Currently, estimates show that one in six Americans has past-due healthcare bills on their credit report (Santhanam, 2018). The chances are that if the debt is showing on a consumer credit report, the healthcare agency waiting for payment has already written that revenue off to bad-debt. The number of American’s classified as underinsured increased by an alarming 16% between 2003 and 2016 (Underinsured Rate Increased Sharply In 2016, 2017).
The uninsured population fares no better. In the past two years, the uninsured population grew from 12.7% to 15.5%.
An important thing to consider about these numbers is that they derive from surveys of clinicians, meaning it defines patients as uninsured or underinsured based on the information healthcare providers have on-hand. Misrepresentation in the data will cause misrepresentation in the numbers, so it is entirely possible that the patients classified as uninsured or underinsured in these studies have insurance not identified at the time of service.
The fact remains. While it is apparent that in most cases true self-pay and self-pay after insurance revenues will end up written off to bad-debt, the propensity for this occurrence is on the rise. Healthcare providers need to do everything they can to identify all payer sources or find additional sources if they are to manage the increase of bad-debt on their balance sheets.
Automated Insurance Verification Solutions
Helping Patients Find Eligibility for Assistance Programs
One proven solution to address the rise of uninsured and underinsured patients is to find additional funding. Healthcare organizations are going to great lengths to estimate a patient’s financial responsibility and help them secure funding for services they cannot afford. Often, the first step in finding additional funding is determine patient eligibility for Medicaid or Medicare. After Medicaid and Medicare eligibility is exhausted, organizations will turn to their financial assistance and charity programs.
Most organizations have contributed extensive resources to financial counseling, which is a process geared towards working with the patient to determine what sources of payment are available to them. This often looks like financial counselors being well versed in all the different criteria working directly with the patients. Some organizations have turned to technology such as BlueMark’s MAPS solution, which gives financial counselors the ability to plug in information and get quick breakdowns of all the different options available to the patient.
All too often, patients presenting as uninsured or underinsured have coverage that has not been identified. Our work in this environment has shown us that as much as 20% of the patients classified as true self-pay or self-pay after insurance actually have insurance or additional coverage that doesn't get identified during intake.
Finding undisclosed coverage can be a near impossible task. Many RCM software aimed at finding hidden coverage rely on databases, clearinghouse, or predictive analytics to find hidden coverage. While this helps, some coverage is missed, or false positives are reported from outdated information, creating unnecessary work downstream.
The only way to get accurate and complete information is going to the source of truth or, in this case, directly to the payer. When we consider searching every patient at every visit across the most active payers to find primary, secondary, and tertiary coverage we are talking about hundreds of thousands of queries per month. When you add in retro-active Medicaid searching for the true self-pay population, going directly to payers to search for hidden coverage seems impossible.
The only way to implement an efficient insurance discovery program to ensure you are exhausting all possibilities of coverage is through automation. The development and maintenance of the process can put these projects out of scope for most organizations.
The most reasonable way to conduct thorough and accurate insurance discovery is partnering with someone that can leverage their own efficiency of scale to bring the work into scope for many healthcare organizations. Automated insurance discovery solutions can be the best answer for healthcare entities that are searching for these types of solutions.
I will never discount the importance of insurance verification but must stress that demographic and insurance information verification is only one step in maximizing your potential to receive payment for the services you provide. Effective strategies and tools to determine eligibility for additional funding and discovering undisclosed payment sources are paramount in your quest to overcome the odds of self-pay dollars landing in bad debt. This will ultimately lead to converting the uninsured and identifying additional streams for the underinsured.