Life sciences companies regularly engage key opinion leaders (KOLs) – including physicians, nurses, scientists and patients – for consulting and advisory services. KOLs are critical partners in the life sciences industry for providing key insights and expertise. The industry, however, continues to face severe regulatory enforcement and fines related to payments to these KOLs. In fact, year-over-year, the U.S. Department of Justice (DOJ) has recovered millions of dollars in settlements due to violations of the Anti-Kickback Statute, Stark Law and False Claims Act. A frequent violation cited in these settlements is the compensation of KOLs for services in excess of fair market value (FMV), thus constituting a kickback or inducement, whether the overpayment was intentional or not.
In order to avoid overpayment to KOLs, companies must create a FMV rate card dictating the rate KOLs should be paid based on their level of expertise. The level of expertise is set by tiering, a process of evaluating the KOL’s experience and specialization against a standard set of criteria. Tiering has always been a tedious undertaking, and both the current and traditional tiering process continues to fail the life sciences industry and hampers the ability of using automation to solve the problem.
The traditional approach to tiering employed by the life sciences industry has been a manual process. It requires a human being to review a KOL’s curriculum vitae (CV) and pick out key accomplishments and experiences. This has inherent challenges and limitations, including:
Manual tiering is tedious and a time-intensive process, especially when a KOL’s CV is dozens of pages long. Attempts to shorten the duration of a manual tier often result in inaccurate tiering outputs as many key details may be missed.
Not only is tiering time intensive, but it requires highly skilled labor to accurately review and digest the CV. When handled in-house, tiering is usually owned by the medical affairs function, which keeps personnel with advanced degrees from performing other more meaningful work. Furthermore, tiering is costly, even when outsourced to a third-party vendor. Standard outsourcing solutions are still manual and charge a premium for reviewing CVs, which may take budget dollars away from other more strategic projects within the company.
Manual tiering relies on the subjective review of a CV to fulfill key criteria. Because subjectivity is involved, human error and/or subconscious bias is inevitable, which may cause the tierer to overlook key information or mis-categorize what they identify in the CV. Human error and bias drive the bulk of inaccurate tiering results.
Manual tiering typically only considers the information that is on the CV itself. If a KOL did not include certain accomplishments or has not provided a recent CV, key information could be missing that may result in a lower tier.
The challenges with tiering that are highlighted above are inseparable from the process, so long as manual tiering is employed. In order to escape these challenges, the industry must step away from the manual process and reimagine tiering all together. Innovation through automation will completely remove the time and cost considerations by taking human labor out of the equation; without human work effort, bias and human error fall out of the equation as well. The remaining factor to address is the source data and how to ensure that it is complete, accurate and up-to-date. Aggregating the data into a digestible format to generate tiering outputs is the paramount challenge in overcoming the limitations of manual tiering. When this automation comes, the industry will not look back to the days of tedious, manual tiering.