Limitations in US cancer screening rate data and opportunities for improvement.

Abstract

e22535 Background: Cancer screening is a cornerstone of cancer control, yet screening participation remains inconsistent across tumor types, populations, and geographies in the United States. Despite widespread reliance on screening rate data to inform clinical guidance, policy decisions, patient advocacy, and early detection innovation, the underlying data landscape is fragmented. Screening rates are derived from surveys, electronic health records (EHRs), and claims data; however, limitations across these sources may misrepresent screening participation and impede early detection. This study assessed the cancer screening data ecosystem to characterize critical limitations and identify prioritized solutions. Methods: Primary qualitative interviews were conducted with cancer screening data users (n = 8), including academic researchers and patient advocacy organizations, and data aggregators (n = 2) from national public health organizations. Secondary research included a structured review of publicly available survey-based, EHR-based, and claims-based screening data sources and relevant literature. Findings were synthesized and evaluated during a multistakeholder workshop with 20 participants representing researchers, clinicians, advocates, and industry experts to identify and prioritize solutions. Results: Across all major data sources, four pervasive categories of limitations were identified: data access, accuracy, consistency, and completeness. Screening data are dispersed across numerous sources with variable transparency, technical complexity, and cost, limiting accessibility and comparability. Accuracy is affected by recall and response bias in surveys, documentation and coding errors in EHRs and claims, and imperfect correction methods. Inconsistent tumor coverage, survey design changes, and irregular reporting cycles constrain longitudinal analysis. Data completeness gaps—including limited capture of underserved populations, risk factors, follow-up, and outcomes—restrict the ability to assess disparities or measure real-world impact. During the multistakeholder workshop, participants prioritized near-term solutions such as improved data education, standardized reporting practices, cross-source validation, and expanded data-sharing, alongside longer-term investments in registries and longitudinal infrastructure. Conclusions: Structural limitations in cancer screening data constrain the ability of stakeholders to optimize screening strategies, address inequities, and support innovation in early detection. Multistakeholder consensus suggests that targeted improvements, paired with longer-term system-level investments, can strengthen the screening data ecosystem. Improving data quality and usability is essential to ensuring screening data informs decisions that improve population-level cancer outcomes.