Because these barriers exist, innovators have pivoted to other business models outside of the traditional reimbursement system while coding and payment systems continue to evolve to appropriately describe and value digital technologies. Direct-to-consumer, software-as-a-service, employer wellness programs, payer programs, and others have been pursued as alternative options with varying levels of success. Often companies deploy multiple models simultaneously or sequentially over time.
One premise of traditional medical device market access remains true for digital health technologies– evidence matters. However, evidence needs likely will differ depending on the business model. Published, peer-reviewed randomized controlled trials (RCTs) continue to be the gold standard for clinical evidence, but are not needed for all digital technologies or all stakeholders.
Alternative evidence development methods such as systematic literature reviews, prospective observational studies (e.g., registries), retrospective observational studies (e.g., chart review, claims analysis), workflow / time-and-motion analyses, and economic models can address the evidence needs of some stakeholders, and may not always require peer-reviewed publication to be considered.
BeaconOne supported a client developing an innovative digital health technology in women’s health that decided to pivot from a direct-to-consumer model to payer reimbursement. The client recognized the need to generate additional clinical and health economic evidence to encourage payer coverage. We supported the client in this transition by preparing an evidence development and payer engagement strategy grounded in primary research with BeaconOne’s panel of active payer decision makers. The client has initiated the recommended evidence development strategy and also adapted its payer engagement strategy.
BeaconOne Healthcare Partners’ expertise sits at the intersection of evidence, reimbursement, and market access and can support innovators in identifying optimal alternative business models for their digital technology, understanding who the key stakeholders are for adoption, and what evidence they will require. Knowing this early in the product development process can help innovators realistically plan and budget for evidence generation activities and maximize efficiency of data collection, analysis, and dissemination