Control Strategies in Molecular Diagnostics: A Tiered, Risk-Based Framework for Accuracy, Reliability, and Real-World Use

Controls are fundamental to ensuring accuracy and reliability in molecular diagnostics, yet their roles are often oversimplified or conflated with broader quality assurance frameworks. As molecular testing expands from centralized laboratories to point-of-care (POC) and over the counter (OTC) settings, the design, implementation, and interpretation of controls must evolve to address diverse operational environments and clinical risks.This review introduces a comprehensive framework for understanding control strategies in molecular diagnostics, integrating internal, external, and orthogonal controls within a tiered, risk-based testing model. We categorize diagnostic systems into three tiers—screening (OTC/POC), confirmatory laboratory testing, and reference-level or adjudication testing—and examine how control requirements scale with analytical complexity, user variability, and clinical impact. Across these tiers, controls serve distinct but complementary roles, including verification of assay functionality, mitigation of contamination, maintenance of cross-platform consistency, and resolution of diagnostic uncertainty. We further analyze common failure modes in molecular diagnostics, including sample-related errors, inhibition, contamination, and interpretation challenges, and map how specific control strategies mitigate these risks. Regulatory perspectives from the U.S. Food and Drug Administration (FDA), Clinical Laboratory Improvement Amendments (CLIA), International Organization for Standardization (ISO), and World Health Organization (WHO) guidelines are discussed, highlighting the shift toward risk-based and context-dependent control design rather than rigid, one-size-fits-all requirements.Importantly, we address the balance between control burden and clinical utility, emphasizing that excessive control implementation may increase system complexity without proportionate gains in diagnostic value particularly in decentralized settings. Emerging trends, including artificial intelligence (AI)-assisted diagnostics and decentralized molecular platforms, are also explored as transformative approaches to enhancing control integration and result validation.We propose that a tier-adaptive, risk-based control framework is essential for next-generation molecular diagnostics, enabling accurate, scalable, and user-centered testing systems. This perspective supports the development of robust diagnostic platforms that maintain analytical integrity while improving accessibility and real-world performance.