Conditional Reimbursement Policies: Moving Forward with Uncertain Evidence

In today’s healthcare landscape, the debate over conditional reimbursement policies has become increasingly complex. As we navigate this challenging terrain, it’s crucial to explore how we can move forward with these policies, despite the uncertainty surrounding their effectiveness and implementation.[1]

To put it simply, under a conditional reimbursement model, a new technology is initially funded on a temporary basis. Later, a second decision is made based on additional data to either continue funding (maintaining the status quo) or stop funding if the technology does not show sufficient value (a perceived loss, since the technology was initially funded and then withdrawn). By contrast, in a traditional reimbursement model, the decision is whether to fund new technology, resulting in either an improvement (gain) if funded, or no change (status quo) if not funded.[1]

Traditional fee-for-service models, where providers were paid based on the volume of services rendered, have given way to systems that reward quality and efficiency. Uncertainty frequently surrounds the total costs, cost-effectiveness, and therapeutic value of new medicines. Conditional reimbursement serves as a strategy to manage this uncertainty. New pharmacotherapies are the primary candidates for conditional reimbursement, typically encompassing new active substances or significant new therapeutic indications for medicinal products already approved for reimbursement. Conditional reimbursement policies are a cornerstone of this new paradigm, offering financial incentives to healthcare providers who meet predefined performance metrics. While the intention behind these policies is to improve patient outcomes and reduce costs, the reality of their implementation has proven more complex.[1,2]

Critics argue that conditional reimbursement policies can lead to unintended consequences, such as an overemphasis on quantity over quality, where providers prioritize meeting metric targets over-delivering comprehensive, patient-centered care. Additionally, the increased administrative burden of tracking and reporting outcomes can divert resources away from direct patient care. There is also uncertainty around evidence, with a lack of consensus on what constitutes meaningful outcomes, leading to variability in policy design and implementation. Furthermore, these policies evoke loss aversion, suggesting that higher incremental cost-effectiveness ratios (ICERs) are considered acceptable for existing treatments than for new treatments, which has important implications for allocation decisions in healthcare. This indicates that the decision-making process directly influences the outcome of a reimbursement decision. First experiences of countries using conditional reimbursement in practice showed that the reassessment process appears to be a complex and politically sensitive procedure. Gathering the additional evidence in practice appears to be challenging and policymakers seem to adopt a fairly passive role in withdrawing reimbursement, probably because of the social resistance surrounding these decisions. As a result, payers need tools to address these uncertainties from both evidence and pricing perspectives.[1,3]

Despite these challenges, proponents of conditional reimbursement policies believe they hold the key to transforming healthcare delivery. They argue that these models incentivize innovation and improvement, ultimately benefiting patients through better health outcomes and cost savings. Conditional reimbursement allows for the gathering of more robust evidence regarding the effectiveness and cost-effectiveness of new technologies without delaying market access. A conditional funding mechanism, especially one that mandates continuous data generation in real-world settings and feeds back into the HTA process, elevates the standard of evidence required from manufacturers. This approach could potentially solve the delays and lack of transparency associated with the post-HTA negotiation, especially for innovative new medications whose reviews have been accelerated by the regulatory and HTA parts of the reimbursement process.[1,4]

To navigate the complexities of conditional reimbursement policies, a balanced approach incorporating several key strategies is needed. The decision-making process directly influences the outcome of a reimbursement decision, so policymakers should be aware of this potential issue when considering conditional reimbursement as a policy instrument. Engaging stakeholders across the healthcare spectrum- providers, payers, policymakers, and patients – is crucial for collaboratively designing policies that align with shared goals of improving patient outcomes and reducing costs. Clear, measurable outcomes must be established to reflect high-quality, patient-centric care, avoiding overly broad or subjective criteria that could lead to gaming the system. Mechanisms for continuous evaluation and adjustment should be implemented to ensure policies remain effective and relevant, enabling continuous evolution based on real-world evidence. Additionally, healthcare providers must receive adequate support and training to understand and effectively implement conditional reimbursement policies, addressing any technological barriers that may hinder compliance.[1]

As we stand at the crossroads of traditional and value-based healthcare, navigating conditional reimbursement policies requires careful consideration and a commitment to collaboration and flexibility. By embracing a balanced approach that prioritizes patient outcomes, reduces administrative burdens, and fosters innovation, we can chart a path forward that benefits both healthcare providers and the communities they serve. The journey ahead is uncertain, but with thoughtful policy design and a focus on evidence-based decision-making, we can move closer to achieving a healthcare system that delivers both value and quality.

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References:

1. Van de Wetering EJ, van Exel J, Brouwer WB. The challenge of conditional reimbursement: stopping reimbursement can be more difficult than not starting in the first place! Value Health. 2017;20(1):118-125. doi:10.1016/j.jval.2016.09.001.
2. Pharmaceutical Pricing Board. Conditional reimbursement: guide for applicants. Available from: https://www.hila.fi/content/uploads/2023/01/Ehdollinen-korvattavuus_ohje-hakijoille_29.3.2023_EN.pdf.
3. Mortimer D, Li JJ, Watts J, Harris A. Breaking up is hard to do: the economic impact of provisional funding contingent upon evidence development. Health Econ Policy Law. 2011;6:509-27.
4. Glennie J, Villalba E, Wheatley-Price P. Closing the gaps to timely patient access: perspectives on conditional funding models. Curr Oncol. 2022;29(2):981–988. doi:10.3390/curroncol29020083.