3 Success tips for Life Science R&D teams: Market Access, Part 1
Show value from harmonized product development data
Using harmonized data from clinical trials and other relevant data sources, R&D teams can define patient populations that respond best to the product, aiding commercial prospects. Data insights derived during product development can influence commercial strategy such as pricing, market segmentation, consumer profiling. As harmonized data displays a global view of data, this also allows for better insight into operations, allowing for efficiencies in budgeting and supply chain.
Establish a collaborative dialogue with colleagues in Market Access
Health economics outcomes research (HEOR) input into study design ensures R&D is evaluating data that market access teams can leverage for conversations with payers. Such collaborative dialogue with market access teams on an ongoing basis can result in iterative commercial findings, even for products early in the earliest phases of development. For example, early discussions with payers, can yield insights enabling a focus on developing drugs or therapeutics that have the most commercial reimbursement value. Armed with such early data from payers along with operational costs data, also aids in product go/no go decision resulting in a win win.
Develop robust clinical safety and effectiveness data
Payers continue to seek ways to reduce healthcare spend, while improving patient outcomes. Payer HTA/drug formulary teams require as much product safety and effectiveness data as early as possible. This is particularly important as the shift to value based contracting is near complete. Expect that credibility and relevance of that data will need to be evaluated by the payer against their target population, providing for a cost benefit profile (and ultimately reimbursement/clinical guidelines pre-consideration). In that vein, while clinical trials data is the gold standard in evaluating safety and effectiveness for the sample trial population, it may be useful to establish how that data can expand to a larger population