Purpose: Manufacturers typically do not conduct economic evaluations to guide development decisions for products that are in preclinical testing. We describe the results of a multi-stakeholder-driven approach that incorporates decision modeling and economic evaluation to help a manufacturer prioritize among its portfolio of early-phase diagnostic technologies.
Method: Working with our industry partner GE Healthcare, we convened three, one-day multi-stakeholder workshops between February and October, 2011. Stakeholders included leaders from clinical, regulatory, and health system delivery organizations. Facilitated discussions focused on identifying areas of high clinical need for new diagnostics across the continuum of care for colorectal, breast and prostate cancer. The stakeholder groups identified a number of priority areas in each disease. Following the workshop, stakeholders were asked to rank the diagnostic opportunities in each disease area. The results were then provided to our industry partner for matching with existing concept or prototype solutions. After GE Healthcare identified prototypes that matched the highest ranked need within each disease area, we developed economic models to estimate the potential costs and consequences of the new diagnostic compared to current care from the perspective of the US healthcare system.
Result: The 27 stakeholders who participated in the workshops identified 29 areas of high clinical need for novel diagnostics in colorectal (9), breast (10), and prostate (10) cancer. Following prioritization and matching, three economic models were developed for diagnostic technologies in the following areas; a) early identification of response to bevacizumab among patients with metastatic colorectal cancer, b) secondary non-invasive testing to identify cancer among women with abnormal screening mammogram, and c) localizing the site of recurrence for prostate cancer survivors with rising PSA values post-prostatectomy.
Conclusion: A process driven by identifying clinical needs from multiple perspectives and incorporating modeling to identify test performance characteristics that achieve cost-effectiveness goals could result in a more efficient process to develop and validate diagnostics that both improve human health and are well-positioned for implementation within health care delivery systems.
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