Life-cycle health technology assessment for precision oncology.

Abstract

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