Complexities in Formulary Coverage and the Use of Derived Data Elements
It’s no secret that over the past decade or so the complexities contained within formulary drug coverage have skyrocketed. As drugs become more specialized and higher cost, the rules around which patientscan get these products covered by their insurance providers and at what copay often read like a complicated choose your own adventure novel (and some of these policy documents truly are novels!). This leads to confusion for those doing the prescribing (Health Care Providers), drug manufacturers trying to track coverage of their products, and pharma reps trying to explain coverage to physicians.
To help simplify some of these complexities, MMIT has developed a new feature called Derived Fields. Derived Fields represent a mechanism that allows for a series of complex business rules to sit on top of the formulary coverage and restriction information and create more consumable answers to the questions of coverage. This may be a binary answer such as covered vs not, preferred vs restricted, or it can be the simplification of complicated step criteria into a more useful, standardized format. The capabilities allow for both. These fields are then incorporated into a number of our products for use in analytics, promotion, research and more.
Simplified Step Criteria:
Much like the drugs themselves, another trend we’ve seen in coverage is a move towards class-specific, “specialized” restrictions. While something like bone density score may now be a highly relevant factor in determining coverage for Osteoporosis medications, it plays no part in something like an ADHD or Diabetes prescribing decision. Another benefit of Derived Fields is that they can incorporate class or even product specific elements into their calculations. In working with a number of brands across a variety of therapeutic areas the theme of class-relevant coverage attributes is pervasive. Incorporating this data into views like the one below is allowing brand teams to more clearly analyze specific aspects of their own and competitors’ coverage. Details previously buried in the weeds of a complex restriction document are brought to the surface and normalized:
As Derived Fields continue to grow it will be interesting to see how this relatively flexible offering evolves to meet the needs of the market and what additional problems this approach to the data can solve. With the ultimate goal of bringing greater transparency to all aspects of the marketplace, a tool that allows for the breakdown of complex data into simplified, consumable elements has the opportunity to play a key role.
Look for future posts and case studies going into greater detail around the specifics of derived field data elements.