Picture this: You carefully examine the startling numbers in your monthly report, hoping they’re just a typo or formula error. They weren’t. You reconcile with your new dashboard, but the KPIs and charts lack the full story. Your team offers an explanation, but there’s more to it. Your gut says something isn’t right, but the answers you need seem hidden below the surface, just out of reach. Sound familiar? It’s very possible that you’re seeing the results of claim inaccuracy.
With medical and dental carriers adjudicating billions of claims each year with trillions of data points — benefit setup data, demographic and enrollment data, fee schedules, reimbursement terms, and more — a lot can go wrong. And even if you have a strong analytics program to help you detect issues, there are moments when your risk of an error skyrockets and even more attention is necessary.
Here are 8 moments when you’re at greater risk of benefit plan contract setup inaccuracy — and thus, costly adjudication issues — and how to navigate them using analytics:
1. When converting benefit plan administration systems or other technology
Some technology changes are strategic investments that support business goals. Some technology changes are necessary for compliance and controls. All technology changes have risk. System conversions aren’t just a technology project, they change processes across the entire organization and require retraining your staff on them. New systems may handle configuration and processes in a way that’s entirely foreign to how previous systems managed them.
For example, we worked with a client who migrated from a benefit administration system where if the coinsurance percentage for a specific procedure code wasn’t defined, the system defaulted to 100% coinsurance, meaning the subscriber would be responsible for the entire amount. The carrier converted to a system where it defaulted to the exact opposite — if the coinsurance percentage wasn’t defined, it defaulted to zero percent.
When claims are adjudicated based on billions of benefit plan setup data points, your risk for error increases dramatically after a system change (and especially after the multiple-year benefit administration system conversion projects that are often fraught with delays and challenges.) Analytics can help to validate your configuration through both anomaly detection and rules-based exception tests.
2. When making changes to your benefit plan setup processes and templates
Even if your underlying technology remains consistent, any process change will increase your risk for error. For example, many summary plan descriptions (SPDs) don’t explicitly define specific procedure codes, which are included in each benefit. This leaves room for interpretation — and misinterpretation — both by benefit groups and the team setting up benefit plans. When you change configuration templates used to begin setting up new benefit plans, it can lead to risk of error that they are inconsistent with the SPD.
The internal workflows that medical and dental carriers use to route a new benefit plan for setup and review can span multiple internal functions. Whether the workflow for routing a new benefit plan (or a change in benefits) is managed through email distribution groups or, ideally, a workflow system, any change to this process increases the risk for error.
When making any process change, we recommend you consider how to measure the effectiveness and any unintended consequences of the change to manage risk. This may include defining specific exception tests to compare plans configured before and after the change occurred.
3. When offering a plan or product with new reimbursement methodology
With the transition to value-based care and alternative payment models, medical and dental carriers are both expanding their product types and reimbursement methods. Even when the product shares upside and downside risk with providers, new products can create additional risk for payers.
For example, in helping a healthcare plan reconcile and validate the settlement of a new value-based care product with over a dozen providers, we identified that several provisions of the contract were ambiguous and not clearly defined, and that the plan had made incorrect assumptions about how to interpret these provisions in the configuration process.
Like in the previous example, when setting up a new product type in your benefit administration system, the end-user may need to interpret how the contract was intended to be adjudicated. Analytics can help to identify inconsistencies in how the terms in a summary plan document were interpreted and set up for your benefits administration team.
4. When experiencing shifts in volume or types of benefits utilized by members
Medical and dental carriers have experienced major shifts in claim expenses over recent years — whether caused by changes in member behavior and how they utilize health benefits through increasing high-deductible health plans or resuming their regular cadenced of six-month dental cleanings after being interrupted by the pandemic.
And as members resumed regular dental prophylaxis treatments, provider behavior has also changed. For example, in some instances, dental carriers have observed significant increases of four-radiographic imaging of bitewings offset by a decrease in the lower-cost two-radiographic imaging. Similarly, some dental carriers have seen an increase in fluoride varnish offset by a decrease in the lower-cost fluoride without varnish.
For medical carriers, the widely publicized surge in weight lost drugs and skyrocketing demand for high-cost pharmaceuticals has contributed to underwriting losses for many nationally recognized health plans, while others have reported record profits.
When facing these quickly shifting trends, analytics is a differentiator that helps to quickly identify and manage risk. We propose that having a robust data analytics strategy, which both helps to quickly identify trends and also improve benefit setup and claim accuracy, is a hallmark of healthcare insurers who are prepared for changing benefits due to changing member and provider behavior.
5. After finding a benefit plan setup error
As the saying goes, if you see an ant at your picnic, there’s probably more. Similarly, when you discover a benefit plan setup error, whether reported by a group, member, provider, or identified by your internal audit team, it’s likely not an isolated issue. When a mistake occurs, there’s a chance it’s occurred in other benefit plans as well, potentially causing your plan (or your self-insured customer) to reimburse higher claim expenses than appropriate.
Unfortunately, too often errors are fixed and teams move on to normal operations. When an error is found, analytics should be deployed. Carriers should design and educate their teams to follow a repeatable process that defines and deploys logical, rules-based exception tests in their benefit administration system or analytics platform. The process should be so widely understood that everyone in the organization knows that, if they find an error, they should identify the root cause and launch an exception test.
6. After discovering a claim processing issue that sample testing missed
Sample testing is an essential component of ensuring quality and compliance. In fact, as a medical or dental carrier, you’re likely very familiar with the sample testing performed through your SOC examination to identify exceptions to your internal controls. Sample testing performed by an independent third-party gives you — and your clients — assurance that your controls are designed properly and operating effectively.
In some cases, the odds of identifying an error are so small that it’s necessary to rely on analytics to test the entire population instead of a sample. For instance, assume a carrier has a control that claims for providers who are no longer credentialed aren’t adjudicated, but the carrier’s processes to update a provider’s information aren’t timely.
Consider a claim adjudicated before the credentialling change is made in the benefits administration system, but the service date is after the provider deactivation data that’s eventually retroactively updated. In this case, the control is ineffective, and it’s highly unlikely that sample testing claims may find the isolated claims that were processed in the two-day, two-week, or two-month time frame before the retroactive change was performed. But, with analytics, an exception test could identify all claims that were adjudicated that occurred after a provider’s credentialling status was retroactively updated.
7. After discovering unexpected (and unexplained) increases in claim expense and PMPM
No increase in per member per month (PMPM) claim expenses that was unexpected should remain unexplained. There are only three types of explanation for an increase:
- Increased member utilization.
- Increased provider payments.
- Increased benefits.
For each of these, there may be a valid external explanation or an invalid internal exception. For example, an increase in member utilization could be explained by changes in member behavior (valid, external) or duplicate claims paid (invalid, internal). An increase in PMPM driven by increased provider payments could be attributed to higher fee schedules (valid, external) or incorrect member accumulator data (invalid, internal), which led to provider payments that should have been the member’s responsibility.
When you’re monitoring your PMPM and something doesn’t feel right, analytics can help provide the answers. It’s almost never just one issue, but rather a combination of external factors and internal exceptions. The key is to begin with developing a data strategy before you’re facing an urgent need to diagnose if a PMPM increase has a valid external explanation or an invalid internal exception.
8. When allowing a high degree of “one-off” benefit terms in the sales and marketing cycle
Personalizing benefits to the unique health and financial needs of each of your employer benefit groups is great, and it can be the key to attracting and retaining top talent. When doing so, however, it’s important to know which benefit options may increase the risk for benefit setup error and claim adjudication as well as how to mitigate those risks.
In all cases, unique benefit options can create risk if they’re not clearly defined in a manner that enables your team to configure them in your benefit administration system. Additionally, custom benefit options create a greater risk for error because, when they break from a standardized template or standard set of procedure codes, they’re not as sustainable to maintain or update with code changes (and are often missed). In some cases, we’ve observed benefit options that weren’t even supported by the benefit administration system — the benefit setup team had an “add comment” on the plan that indicated to contact the benefits department for this service to have it manually adjudicated.
We don’t recommend avoiding personalized benefit options for your employer groups, but if you do choose to use them, do so recognizing and mitigating the risk with robust analytics. Your internal audit team needs the capability to identify anomalies and validate that custom benefit options are updated and maintained.
Protect yourself from claim inaccuracy and adjudication pitfalls
Analytics is the figurative “metal detector” to help find the needles in a haystack. Conducting an analytics-enabled risk assessment of your claim expense and benefits administration system risk can be paired with sample testing to help you manage risk with confidence. This is especially true when small errors can have a big impact, both on your finances and your trust in your benefit groups and provider network.
