Advocacy - Lab Advocate
ADLM’s Board of Directors recently met with key offices on Capitol Hill to advocate for federal policies that would strengthen the quality, consistency, and future-readiness of laboratory medicine. The association’s message focused on three closely connected priorities: funding the Centers for Disease Control and Prevention (CDC) to improve pediatric reference intervals (PRIs), continued support for laboratory test harmonization efforts at CDC, and the need to ensure that healthcare artificial intelligence (AI) is built on reliable, standardized laboratory data.
Members emphasized that accurate laboratory testing is foundational to high-quality care, especially for children. While adult reference intervals are generally well established, pediatric reference intervals are often incomplete or outdated and may not reflect normal biological changes across infancy, childhood, and adolescence. ADLM is therefore urging Congress to provide $10 million in fiscal year 2027 funding to allow CDC to begin developing and disseminating higher-quality pediatric reference intervals using existing federal infrastructure.
Board members also highlighted the importance of continued federal investment in harmonization of clinical laboratory test results. When different methods or instruments produce different numeric values for the same analyte, it becomes harder for clinicians to apply guidelines consistently and compare results across institutions. ADLM’s FY27 testimony asks Congress to provide an additional $7.2 million for CDC’s harmonization work to support more consistent, cost-effective healthcare and expand standardization efforts across important disease areas and testing platforms.
A central message of the Hill meetings was that pediatric reference intervals, harmonization, and healthcare AI are not separate issues; they are deeply connected. As ADLM has noted in its recent advocacy, AI and machine learning tools are only as reliable as the laboratory data and standards on which they are built. If the underlying data are inconsistent, non-harmonized, or based on incomplete reference frameworks, AI-enabled tools risk reinforcing variability rather than improving care. By contrast, stronger federal support for harmonization and pediatric diagnostic infrastructure can help create the high-quality data environment needed for responsible, equitable, and clinically reliable use of AI in healthcare.
ADLM will continue working with congressional offices and allied stakeholders throughout the appropriations process to advance these priorities.