CancerLinQ: Origins, Implementation, and Future Directions

Abstract

Rapid-learning health systems have been proposed as a potential solution to the problem of quality in medicine, by leveraging data generated from electronic health systems in near-real time to improve quality and reduce cost. Given the complex, dynamic nature of cancer care, a rapid-learning health system offers large potential benefits to oncology practice. In this article, we review the rationale for developing a rapid-learning health system for oncology and describe the sequence of events that led to the development of ASCO’s CancerLinQ (Cancer Learning Intelligence Network for Quality) initiative, as well as the current state of CancerLinQ, including its importance to efforts such as the Beau Biden Cancer Moonshot. We then review the considerable challenges facing optimal implementation of a rapid-learning health system such as CancerLinQ, including integration of rapidly expanding multiomic data, capturing big data from a variety of sources, an evolving competitive landscape, and implementing a rapid-learning health system in a way that satisfies many stakeholders, including patients, providers, researchers, and administrators.