Improving breast cancer screening disparities through A3 problem solving: Design of a quality improvement initiative

Abstract Introduction: Extensive research studies on breast cancer disparities have had limited impact on reducing breast cancer disparities. Quality improvement initiatives use data-driven, rigorous approaches to plan, execute, and study the delivery of healthcare services. There have been limited studies using quality improvement methods to address health disparities. The purpose of our study was to describe the design of a quality improvement initiative to address breast cancer disparities using the A3 problem solving method. Methods: A3 problem solving method was used to reduce breast cancer screening disparities. The A3 method is a quality improvement tool used to identify problems, organize and synthesize data, and propose solutions to achieve goals (summarized on one side of a sheet of paper). A3 methods are outlined by the FOCUS-PDCA acronym: F - Find process to improve, O - Organize team of stakeholders, C - Clarify current state, U - Understand sources of variation that contribute to the problem, S - Select change ideas, P - Plan and do the improvement, C - Check the results, A - Act. Quantitative data was derived from the electronic medical record. Qualitative data was derived from divergent thinking exercises asking participants from diverse standing urban stakeholder groups about barriers to cancer screening. Results: Design of the A3 problem solving process was conducted between November 2022 until May 2023 yielding the following FOCUS-PDCA design and initial results. F - Increase the percentage of patients who have undergone mammography screening within the last two years. O - Team includes representative leaders from primary care, community health, ambulatory operations, diversity, equity, and inclusion, radiology, breast center, population health, information services, and federally qualified health centers. C and U - Quantitative data collection revealed that 74% of 37,509 eligible patients (women between 50-74 years old) received a mammogram within the last two years (75% White, 58% Black, 67% Asian, 60% American Indian, 64% Hispanic). Divergent thinking exercises revealed the following root causes for mammography screening disparities: patients don’t know they are due, transportation issues, barriers to scheduling, access to mammography facilities, financial concerns, and fears about returning to clinics during COVID-19. S - Process improvements included multilingual and modality reminders, marketing campaigns, transportation vouchers, co-location of screening centers with federally qualified health centers, expanded access to mammography screening facilities, educational activities for primary care physicians, participation in community events, equity-focused review of screening guidelines. PDCA - Based on selected improvements, changes in breast cancer screening percentages will be displayed using run charts until December 2024, stratified by race and ethnicity. Conclusion: A3 problem solving tools represent structured, scientifically rigorous approaches that cancer centers can use to reduce screening disparities. Citation Format: Nia Foster, Arissa J. Milton, Mai A. Elezaby, Roberta M. Strigel, Meeghan A. Lautner, Ryan W. Woods, Nicci O. Brackett, Noelle K. LoConte, Anand K. Narayan. Improving breast cancer screening disparities through A3 problem solving: Design of a quality improvement initiative [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C112.