Life-cycle health technology assessment for precision oncology.

• DOI: 10.1200/jco.2022.40.16_suppl.e18704
• Publication Date: 2022-06-06T16:51:41Z
• Authors: Dean A Regier; Deirdre Weymann; Brandon Chan; Cheryl Ho; Howard John Lim; Stephen Yip; Rebekah Rittberg; Sophie Sun; Marco A. Marra; Steven J. M. Jones; Janessa J. Laskin; Samantha Pollard
• Keywords: Reimbursement; Health Technology; Stakeholder Engagement; Disinvestment

e18704 Background: Rapid advances in precision oncology challenge timely and sustainable reimbursement decisions. Life-cycle health technology assessment (LC-HTA) can enable conditional patient access to promising innovations alongside evidence development. Our objective was create a life-cycle evaluative framework, called PRecision Evidence Development Cancer Treatment (PREDiCT). Methods: Through an iterative, system stakeholder-informed approach, we designed our LC-HTA framework. Elements supporting data generation were subsequently implemented within British Columbia, Canada’s provincial cancer control system. development, refinement, pilot implementation process included structured literature review, multi-disciplinary international expert consultation, formal gap assessment, series of pan-Canadian inter-disciplinary stakeholder workshops refine framework elements. Results: We engaged n = 15 stakeholders co-develop the Defining components include: (a) managed that defines time horizon pricing conditions real-world healthcare trialing; (b) collection core elements required economic evaluation using real world data; (c) externally leveraged determine comparative effectiveness, cost-effectiveness, value conducting additional research; (d) interpretation updating decisions, including investment, continued evaluation, or disinvestment from access. Key success early is expansion infrastructure routine linkage genomic sequencing treatment data, quality life clinical outcomes, as well resource use spanning diagnostic, treatment, follow up trajectory. Conclusions: Sustainable integration requires design learning systems (LHS) integrate with other information. moves beyond static estimates cost-effectiveness continuously generate reduces evidentiary uncertainty supports are embarking on PREDiCT implement real-time demonstrate ability support cycle evaluation.