Evaluating Payer Impact on Utilization with Claims and Coverage Data
To fully understand how payers impact patient access, pharma companies should be evaluating payers using multiple datasets, including both coverage/restriction data and claims data. While payer policies indicate how payers plan to manage a product or service, medical and pharmacy claims reveal how payers actually manage that product in reality.
Taken together, claims and coverage data generates a wealth of information about how patients move through the healthcare system—and how payer behavior impacts their access to treatment. What’s driving rejections of provider prescriptions? How restrictive are payers being with their policies, and why? The answers to these questions can help pharma companies pinpoint and resolve patient access barriers.
Here are the top eight questions MMIT’s pharma clients are answering with our integrated claims and coverage data:
1. How is a drug being covered by payers?
While payer policy and restriction data indicates how payers intend to cover a particular drug, the addition of claims data reveals how that coverage played out in reality. How are claims distributed across channels, and what percentage of claims was reimbursed with preferred access? How many claims had a prior authorization or step therapy requirement? How often are payers excluding coverage for a drug, but still offering reimbursement as a medical exception?
2. Which payer accounts should we target based on claim volume?
Integrated data allows pharma companies to compare patient and claim volume across both payers and brands. For example, a manufacturer can see exactly how many claims for both its brand and competitors A, B, C, and D are processed by each of the top 10 payers. This intelligence helps manufacturers determine which payers to target for a coverage/contracting conversation.
3. How does utilization trend over time by coverage and line of business?
By examining historical claims data, pharma companies can track shifts in claim utilization by both coverage status and line of business for top payer accounts. For each payer, which channels have the highest claim volume, and how do those channels cover the drug?
Knowing how coverage status impacts claim volume provides manufacturers with real-world evidence to share as they negotiate with payers for better coverage for a particular channel. For example, a payer’s claim volume might be much higher for its commercial channels than its Medicare line of business, because its commercial coverage is much better; this data can help pharma companies make the case for a Medicare policy change.
Utilization trend data can also help manufacturers understand how payers are covering their drug versus competitors. If a product is covered as a specialty drug but a key competitor has preferred status, which drug has higher claims volume? Knowing whether coverage status has historically been a barrier to access can help manufacturers develop a mitigation strategy.
4. Are payers enforcing utilization management criteria and adhering to their policies?
While payers may specify prior authorization or step therapy requirements, these policies are not always followed. Comprehensive claims data can reveal trends in how a product or combination therapy is managed by payers, showing the changes in claims volume over time. This data indicates the percentage of claims covered with prior authorizations or step therapy, in addition to the relative restrictiveness of a payer’s policies: is the drug being managed to label, or is coverage more or less restrictive than the prescribing information?
By marrying coverage data with claims data, pharma companies can see the discrepancy between payer policy and reality—which enables more accurate prioritization of their payer accounts. For example, a manufacturer might learn that although Payer A’s Medicaid plans required two step edits, they reimbursed claims without enforcing them. On the other hand, Payer B’s single step requirement was heavily enforced, which led to limited claims. Armed with this data, the manufacturer could safely deprioritize Payer A while targeting Payer B for contracting negotiations to remove the step.
5. What percentage of claims were dispensed vs. rejected?
Before pharma companies can intervene to improve access, they must first know how many prescriptions are not being fulfilled—and what’s happening to impede them. By tracking claims for all products in a specific market basket, pharma companies can identify how their product fits into these trends.
How many claims were rejected outright by a specific payer? How many were approved, but never filled—or filled on a second or third attempt, indicating that external factors, such as out-of-pocket cost, initially impeded access? Diving into claim denials helps pharma companies pinpoint access barriers and identify which payers to target for contracting discussions.
6. Why are claims being denied, and how has that changed over time?
Pharma companies can view quarter-over-quarter trends to identify the primary reasons a patient’s first prescription was not filled. Was the problem a payer error, clerical error or patient abandonment? For those whose claims were initially rejected, how many eventually received their prescription, and how long did that take, on average? How many patients never filled their prescription at all?
Integrated claims data can reveal hidden reasons for denials. For example, if a manufacturer discovers that missing information is the most common driver of denials, they can run a physician education campaign, sharing the right billing codes and issuing reminders to include the necessary clinical evidence or complete the correct testing in advance. An exploration of the historical data can show which manufacturer efforts to reduce rejections have proven successful.
7. How do claim rejections differ by channel?
For the claims denied because of payer restrictions, it’s important for pharma companies to understand exactly how issues differ between channels. Was the product/service denied because it wasn’t on formulary, or because the product/service was deemed “not appropriate for this location”?
Knowing the rationale behind claim denials helps pharma companies focus their resources appropriately, as reasons might differ considerably by channel. For example, a company might find that 19% of Medicare claims for a product are denied at the pharmacy due to Part D not paying for the prescription, while 12% of Medicaid claims are denied due to a Drug Utilization Review (DUR) reject error.
8. How many rejections do our patients face before approval?
Ultimately, pharma companies want to know how claim denials impact their patients. Integrated claims data can reveal the primary reasons that cause prescriptions to remain unfilled, and quantify how many attempts patients are making to fill their prescriptions before abandoning the effort. For example, a pharma company might find that clerical mistakes account for 36% of unfilled prescriptions, and that 45% of patients abandon their prescription after only one attempt to fill it.
This kind of patient journey analysis assesses not only patient behavior, but also HCP prescribing behavior. How many claim denials does it take before a patient switches to another brand? If one payer’s prior authorization process significantly impedes access to a company’s drug as compared to its competitors, pharma companies can use this evidence to argue for reduced or modified restrictions. They can also provide additional support and education for prescribers to emphasize the value of refiling the claim.
As we’ve seen, unified claims and coverage data helps pharma companies clarify the gap between what a payer’s policy says and how that policy is applied.
MMIT also integrates its coverage and pathways data with other real-world data sets, including structured/unstructured EMR data and lab tests and results. As all data is mastered, linked and tokenized, pharma companies can answer their queries without needing to worry about data accuracy or discrepancies. Commercial teams gain a complete view into the longitudinal patient journey to help them increase utilization for their therapies.
Learn how the harmonized data in Patient Access Analytics can help you uncover access barriers and drive an evidence-based contracting strategy.
