From the September 2016 issue of HealthCare Business News magazine
That is now changing, as next-generation analytics begin to automate the review process. These analytics use multiple data points — more than a human can process at one time — to identify and surface purchasing and prescribing patterns that offer a high probability of abuse. Experts can then focus their time evaluating actionable insights rather than sifting through data to determine which members or prescribers to target. Following are the strategies being used by health plans and pharmacy benefit managers (PBMs) to discover this fraud, waste and abuse.
Discovering unusual consumer behavior
After setting a baseline of what is considered “normal” behavior, analytics are being used by health plans to churn through dozens of data points to find behaviors that fall outside the norm. Some examples are consumers who are seeing more than 10 physicians or filling prescriptions at more than 10 pharmacies.
Color-coded dashboards are then assigning scores based on risk factors to bring the most likely cases of FWA to the top. Yet it’s not quite that simple, as sometimes these patterns of unusual behavior may be legitimate. While a patient receiving opioid prescriptions from multiple providers and filling them at different pharmacies can be an indication of FWA, a cancer patient who is seeing several specialists may have a valid reason for doing so.
To help separate these patients from the abusers, next-generation analytics are bringing in additional data, such as displaying the locations of the prescribers and pharmacies on a map relative to the patient’s home. If multiple prescriptions are being filled at locations far from the patient’s home, it’s generally a strong indicator of FWA. By automating this process and using all the data at their disposal wisely, health plans and PBMs can focus their efforts more effectively while being sure not to alienate members in good standing.
At the store (pharmacy) level
Despite tight governmental control over pharmaceuticals, there is still plenty of opportunity for FWA because of the complexities involved. Next-generation analytics are helpful in uncovering this activity by establishing a benchmark of patterns over a specified time period, such as a year, and then monitoring activities against that benchmark each week going forward.
If there are significant deviations from the benchmark, those pharmacies are highlighted on a color-coded dashboard to determine which require immediate action, which should be on the “watch list” and which may have just had an unusual week. The analytics also enable health plans and PBMs to comply with the Centers for Medicare and Medicaid Services (CMS)-required monitoring of “watch lists." Among the metrics that can be monitored are:
