Illustrating the financial consequences of outcome-based payment models from a payers perspective- the case of autologous gene therapy atidarsagene autotemcel (Libmeldy®)

Abstract

OBJECTIVES: To illustrate the financial consequences of implementing different managed entry agreements (managed entry agreements for the Dutch healthcare system for autologous gene therapy atidarsagene autotemcel [Libmeldy]), while also providing a first systematic guidance on how to construct managed entry agreements to aid future reimbursement decision making and create patient access to high-cost, one-off potentially curative therapies. METHODS: Three payment models were compared: (1) an arbitrary 60% price discount, (2) an outcome-based spread payment with discounts, and (3) an outcome-based spread payment linked to a willingness to pay model with discounts. Financial consequences were estimated for full responders (A), patients responding according to the predicted clinical pathway presented in health technology assessment reports (B), and unstable responders (C). The associated costs for an average patient during the time frame of the payment agreement, the total budget impact, and associated benefits expressed in quality-adjusted life-years of the patient population were calculated. RESULTS: When patients responded according to the predicted clinical pathway presented in health technology assessment reports (scenario B), implementing outcome-based reimbursement models (models 2 and 3) had lower associated budget impacts while gaining similar benefits compared with the discount (scenario 1, €8.9 million to €6.6 million vs €9.2 million). In the case of unstable responders (scenario C), costs for payers are lower in the outcome-based scenarios (€4.1 million and €3.0 million, scenario 2C and 3C, respectively) compared with implementing the discount (€9.2 million, scenario 1C). CONCLUSIONS: Outcome-based models can mitigate the financial risk of reimbursing atidarsagene autotemcel. This can be considerably beneficial over simple discounts when clinical performance was similar to or worse than predicted.