Behavioral Analytics: Key to Shutting Down the Medicare Part D ATM
Advanced behavioral analytics can differentiate patterns of fraud and abuse from instances such as oncology patients receiving multiple prescriptions from different specialists.
Fraud, waste, and abuse (FWA), especially around Schedule II drugs such as opioids, have reached epidemic levels in America. The CDC states that 5.1 million Americans abused painkillers to some degree in 2010, and the 2012 National Survey on Drug Use and Health found that 2.1 million Americans were addicted to opioid pain relievers.
The opioid abuse crisis is not limited to just users: it is also a very profitable business for prescribers and pharmacies, and can have far-reaching effects on certain patients, specifically those undergoing treatment for cancer. Nowhere is this more evident than with Medicare Part D.
From 2006 to 2014, spending on commonly abused opioids among Part D beneficiaries rose to $7.8 billion, an increase of 156%. According to the Office of the Inspector General (OIG) June 2015 data brief “Ensuring the Integrity of Medicare Part D,” in 2007, more than $1.2 billion was paid by claims with invalid prescriber identifiers—an amount that doesn’t include all other instances of FWA during that time.
Why is FWA so prevalent in Medicare Part D? The simple answer is that it is relatively easy to slip through the cracks. The same OIG report stated that Medicare plan sponsors; Health Integrity, the Medicare program integrity contractor for the Centers for Medicare & Medicaid Services (CMS) under the National Benefit Integrity Medicare Drug Integrity Contract (NBI MEDIC) charged with detecting and preventing FWA in Part D; and CMS itself, all lack adequate controls and mechanisms to address the issue.
For example, NBI MEDIC does very little to proactively seek out FWA. Instead, it relies on whistleblowers to call a toll-free phone number to report instances of FWA even though its charter states it should use data analytics to proactively find FWA. Medicare plan sponsors and CMS are also doing little in the way of data analytics, despite the proliferation of claims, clinical, and other data that are currently available.
What makes the opioid abuse issue even more complex is that plan sponsors are not required to report information on FWA, nor are there strict guidelines they can follow to remedy any situations that pop up. This lack of information makes it difficult for CMS to formulate broader action plans or hold plan sponsors accountable for protecting Part D from FWA.
All of this amounts to wasted opportunities to address FWA at all of its sources: beneficiaries, prescribers, and pharmacies. The raw data are there, but they aren’t enough. The real key is to apply advanced behavioral analytics to help uncover and address FWA while minimizing the false-positives that lead to wasted time and resources—and that ultimately interfere with the efficacy of an FWA program.
The pharmacy benefit under Part D is similar to the “any willing provider” provision under CMS: Patients can take their Part D prescriptions to any pharmacy or pharmacy benefit manager (PBM) that is willing to fulfill them. This is a tremendous benefit, especially for older Americans who may have trouble with transportation or who depend on a caretaker to fill their prescriptions.
By opening up the pool of pharmacies available, this provision makes the entire process much more convenient. Yet, when access is thus available, there is more potential for FWA, which degrades the integrity of the pharmacy benefit. In fact, some unscrupulous operators may open pharmacies in areas with heavy Part D populations specifically to take advantage of the lack of proactive oversight.
Advanced behavioral analytics can help payers and their PBMs stop these operators by identifying anomalies and patterns that are key indicators of FWA. These analytics rank suspected FWA on a numeric scale so that inspectors can focus their efforts on the situations most likely to occur while eliminating the false-positives that waste time. Given that payers and their PBMs are viewed by CMS as the frontline in identifying and preventing FWA, advanced analytics can help by lowering the costs and maintaining the integrity of the Part D benefit for everyone.
The 4 key areas in which advanced analytics can be most beneficial are member drug-seeking behavior, pharmacy FWA, overlaps in pharmacy and medical benefit payments, and compound claims.
Member Drug-seeking Behavior
Analytics make it easier for reviewers to find behaviors that are unusual. Rather than paging through spreadsheets, color-coded dashboards can assign scores based on risk factors. This format calls attention to the most likely cases of FWA based on pre-set thresholds, such as members who are seeing more than 10 physicians or filling prescriptions at more than 10 pharmacies. These thresholds can be set based on industry benchmarks or adjusted to the preferences of the payer or PBM.
One of the challenges of uncovering FWA among members is that the same patterns that indicate FWA could also indicate legitimate patient needs. For example, receiving opioid prescriptions from multiple providers and filling them at different pharmacies are common indicators of potential fraud.
Yet, an oncology patient who is receiving multiple prescriptions from several different specialists may have a legitimate reason for this behavior. This is where next-generation analytics bring in additional data, such as displaying the locations of prescribers and pharmacies on a map relative to the patient’s home. This makes it easier to detect FWA, especially if several prescriptions are being filled at different locations a long way from a patient’s home.
The intelligent application of analytics will help automate the process of bringing the most likely perpetrators of FWA to the surface, while minimizing the false-positives, thus ensuring that payers or PBM resources are being used most effectively to reduce costs, not alienate members in good standing.
Detecting FWA in Pharmacies
Members aren’t the only ones committing FWA. Pharmacies have plenty of incentive, too. In this case, analytics can uncover illegal activity by establishing a benchmark of patterns over a specified time period, such as a year, then monitoring activities against that benchmark on a weekly basis. If there are significant deviations from the benchmark, those pharmacies are highlighted to determine whether action is required.
More sophisticated analytics packages can even color-code these pharmacies, based on severity, to make it easier to determine where immediate action is required, which pharmacies should be on the “watch list,” and which location may just have had an unusual week. They also enable payers and PBMs to comply with CMS monitoring of “watch lists.” Then, displaying the results on a dashboard makes it easy to spot overall trends, such as pending claims or withholding payment; pharmacies that may require corrective interventions; and pharmacies that may need an onsite visit or other more severe actions.
Some of the metrics that can be monitored include:
- rate of new billing
- reversal rate (very high and very low)
- percentage of member co-pays
- average ingredient cost
- average paid per subscription
- average number of prescriptions per member (stratified by age)
- percentage of controlled substances
- DAW-1 percentage
- average paid per member
Pharmacy and Medical Benefit Overlaps
This is an area in which expanding beyond typical claims data analysis can help prevent plan sponsors from paying twice for the same medications. In most cases, this is more of a waste issue than intentional fraud or abuse of the system. The central problem is that medical and pharmacy benefits are billed under 2 different claims platforms that do not talk to each other.
This can be a problem for patients with long-term or chronic conditions, such as arthritis or diabetes, since certain drugs might fall under either the medical or the pharmacy benefit depending on how tightly the plan sponsor coordinates the drugs in conjunction with the PBM. As a result, even though everyone involved—clinicians, pharmacies, and patients or their caregivers—acts in good faith, a medication may be double-billed.
Here is an example: A patient has a hip replacement and is prescribed a self-injectable medication to prevent blood clots. The health plan requires the patient to obtain this particular medication through a specialty pharmacy, which then sends that prescription to the patient’s home either the day he/she gets home or the following day. When the patient goes to the physician for a follow-up visit, the physician shows the patient how to inject the medication.
The physician’s office may then mistakenly bill for the medication under the Healthcare Common Procedure Coding System as a J Code, while the specialty pharmacy correctly bills for the medication under the National Drug Code (NDC). Without analytics, the physician will likely be reimbursed for the erroneous claim.
Advanced behavioral analytics, however, can merge data from both systems, compare them, and identify both overlaps and opportunities for recovery by looking at the day span, the unit, the amount billed, and the amount paid. Advanced analytics can also help ensure physicians get paid for legitimate claims, such as when a diabetic patient goes to the physician’s office and the physician sees a dangerous spike in the glycated hemoglobin level.
Since it is urgent, the physician may give the patient an injection in the office and a prescription for an insulin pen to be filled at the local pharmacy. Although a comparison of the J Code and NDC code will show that the insulin was paid for twice, the analytics will confirm that both payments are justified in this case.
One other common area of FWA, under Part D, that advanced analytics can address is compound claims. This is accomplished by identifying spikes and bring hidden information to light so plan sponsors and their PBMs can take action to stop them. This area is very susceptible to fraud especially because, in many cases, a claim will only show 1 line of the prescription compound rather than all of the ingredients.
This limitation can give a pharmacy intent on committing FWA the opportunity to create a custom compound when there is already a less expensive option commercially available—a move that only adds to the cost—or the chance to submit a false claim for a bulk powder as 1 of the ingredients, even though it is not covered under Part D. Therefore, even though an end-stage cancer patient may be receiving a constant low dose of morphine powder rather than other, more expensive forms of the drug, the claim just shows morphine.
Shut It Down
The sheer dollar volume and lack of proactive oversight make Medicare Part D particularly susceptible to FWA. Advanced behavioral analytics use new data sources to help health plan sponsors and their PBMs identify instances of FWA more precisely. This, in turn, allows stakeholders to use their resources wisely and take the lead to shut FWA down and make sure older Americans receive the benefits they deserve.