PACE (Patient Access to Cancer care Excellence), a Lilly Oncology initiative, exists to encourage public policies and health care decisions that speed the development of new medicines, assure cancer treatments respond to the needs and qualities of individual patients, and improve patient access to the most effective cancer medicines. In 2012, the PACE Global Council convened to identify potential barriers to innovation in oncology research and specific strategies to remove obstacles to improved cancer care. From this meeting emerged a recognition of the need to build understanding among researchers, policymakers, and patient advocates of the contributions of continuous innovation in the fight against cancer. Watch a White Board Video and Download Brochure
*** A journal article authored by RLA staff and collaborators, Proof-of-concept study: Homomorphically encrypted data can support real-time learning in personalized cancer medicine, was recognized by the International Medical Informatics Association (IMIA) as one of the of the best papers of 2019 in the Clinical Research Informatics arena.
RLA’s initial role was to conduct for the Global Council an analysis of the public policy environment and barriers to rapid research and development in oncology. RLA then developed the PACE Continuous Innovation Indicators™ (CII) – the first, evidence-based, customizable online tool to review progress against cancer over time. The CII provides an interactive platform for conducting a variety of analyses across 12 types of cancer. RLA regularly updates the CII with the latest data and presents consequent research at scientific conferences.
PACE Continuous Innovation Indicators—a novel tool to measure progress in cancer treatments (eCancerMedicalScience, 2015)
Dynamic value assessments in oncology supported by the PACE Continuous Innovation Indicators (Future Oncology, 2017)
The PACE continuous innovation Indicators: A flexible tool to evaluate progress in cancer treatments (Journal of Clinical Oncology, 2017)
Proof-of-concept study: Homomorphically encrypted data can support real-time learning in personalized cancer medicine (BMC Medical Informatics and Decision Making, 2019)