ALIGNMT AI Responds to White House RFI on AI Regulatory Reform

ALIGNMT AI has provided input about critical gaps in healthcare AI oversight in its response to the White House Office of Science and Technology Policy's request for information about AI regulatory reform. Introduced as part of America’s AI Action Plan, this RFI seeks cross-sector input on strategies to accelerate AI innovation while addressing regulatory challenges.

The company's submission focuses on the urgent need for federal guidance on post-market AI monitoring standards, particularly clear risk thresholds for when AI performance requires intervention and practical frameworks to help health systems and payers avoid massive financial losses from undetected AI risks. ALIGNMT AI highlighted how healthcare organizations deploying AI at scale face unprecedented challenges in determining when AI performance requires intervention, given that healthcare itself operates in gray areas where multiple valid treatments or coding combinations may exist for the same clinical scenario.

AI stakes can become life-threatening when AI errors affect patient safety. Consider sepsis prediction algorithms, which have been widely deployed despite documented performance concerns. Epic's sepsis prediction model, implemented at hundreds of health systems, was found in a peer-reviewed JAMA Internal Medicine study to have a sensitivity of only 33% at the recommended alert threshold—meaning it missed 67% of sepsis cases (source). Because sepsis requires immediate treatment and delays of even hours increase mortality risk, this type of monitoring failure has direct clinical consequences. A health system using such a model could experience preventable deaths for months before detecting that their AI system was failing to identify a life-threatening condition in two-thirds of cases.

ALIGNMT AI urges the administration to establish federal standards that focus on identifying risky AI behavior patterns rather than just performance thresholds. The company's seven recommendations include: defining domain-specific risk indicators, creating operational detection requirements, establishing behavioral safe harbors, mandating algorithmic transparency, setting clear intervention triggers, providing tiered implementation guidance for organizations of all sizes, and requiring vendor accountability standards.

In our submission, ALIGNMT AI particularly emphasized the capacity crisis facing smaller healthcare providers—rural hospitals, community health centers, and physician practices—who lack the technical expertise and resources to independently validate AI performance. Without accessible, standardized monitoring frameworks, these providers face an impossible choice between blind trust in AI systems or foregoing adoption entirely. Without federal leadership defining how to operationalize AI safety and performance monitoring—and making that operationalization accessible to providers of all sizes—the industry cannot build reliable AI operations.

ALIGNMT AI has indicated its readiness to share extensive field data on AI behavioral patterns and operational monitoring frameworks to support federal standard development. The company continues to advocate for comprehensive post-market monitoring standards that protect both patient safety and financial sustainability across all healthcare settings.