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Speakers: Didier Serre, Joanne Buckle
High cost technologies, mostly targeting rare diseases and genetic disorders, are becoming a growing source of concern to public and private payers. The front-loaded budget impact of novel curative therapies, potentially disruptive to payers, heightens the immediate need to address financing considerations. Rapid advances in gene therapies evidenced by promising clinical trial results and more accurate early diagnostic testing are likely to further increase the number of patients eligible for these treatments. Current funding mechanisms, structured to cover incremental costs usually required to treat patients with chronic conditions, will have to be re-designed as highly effective curative therapies like Hepatitis C drugs are disrupting the distribution of treatment cost with the long-term realisation of clinical benefits to patients.
This research paper, sponsored by the Society of Actuaries (SOA) - Health Section, describes a framework for evaluating alternative funding approaches to high cost technologies in a structured and systematic way. Public and private payer perspectives are presented separately for the US and the UK given the unique considerations, with applicability to other markets as well. Various financing and insurance-like models are explored, ultimately highlighting the strengths and weaknesses of each of these approaches to the type of health technologies at hand, and identifying areas where traditional insurance risk principles can break down in the context of these technologies. We rely on illustrative scenarios using real-world data to explore the applicability of these alternative financing models to selected disease areas and therapies. We also develop actuarial cost models and cost projections, in a control versus treatment approach, to demonstrate the magnitude of the financial risks to payers, and to support the wider discussion on risk sharing and aligning incentives between patients, payers and manufacturers. Sensitivity analysis is also performed to identify key modelling assumptions.