Do you have an algorithm or tool to help increase the uptake and use of cancer screening? If so, apply and get your work funded to improve cancer screening and preventive services in areas nationally that experience health disparities.

How can my algorithm or tool improve cancer screening and preventive services?

By training your algorithm to focus on an under-screened population, you can improve the effectiveness and efficiency of cancer screening and preventive services that reduce cancer risks, morbidity, and mortality across all populations. 

A fellow researcher can increase and expand their access to more diverse data by creating a tool that can standardize how data are collected and stored. This can help artificial intelligence models improve the screening process in areas that experience health disparities and follow up with any abnormal results.

What intervention topics interest NCI?

• Populations with historically low rates of cancer screening
• Lung cancer screening processes
• Following up after abnormal test results and other components of the screening process beyond screening uptake
• Changes in organizational practices to guidelines

When do I need to submit my application?

Application due dates reoccur every four months until 2026, starting on October 5, 2023.

What should my application include?

• Provide a hypothetical model of how the algorithm or tool will promote cancer screening, preventive services, referrals, and/or follow-ups to reduce disparities.
• Identify and describe the roles of healthcare and community partners.
• Create a data analytic plan that specifies how multi-level factors, intervention effects, interactions, and outcomes will be handled.

This Notice of Funding Opportunity (NOFO) is part of a new trans-NIH research effort, ADVANCE: Advancing Prevention Research for Health Equity. NCI and the other participating NIH institutes/offices issued this NOFO after discovering that the use and uptake of evidence-based cancer screening and preventive services that reduce cancer risks, morbidity, and mortality are not implemented equally across all populations.