Continuity Intelligence: Establishing a Theoretical and Methodological Framework for Digital Public Health

Artificial intelligence (AI) has advanced diagnostics, prediction, and automation, yet itsability to sustain continuity of care and adaptive ethical learning across populationsremains limited. This paper introduces Continuity Intelligence (CQ)—the first theoreticalframework to formalize continuity as a measurable dimension of intelligence within socio-technical health systems. CQ redefines intelligence as the sustained and ethicallygoverned capacity to remain contextually aware over time, moving beyond the short-termoptimization of current AI paradigms. Unlike frameworks such as Learning Health Systemsor adaptive AI, CQ positions continuity—not iteration—as the defining characteristic ofintelligent health infrastructures. Drawing on Donna Haraway’s situated knowledges¹⁰ andBruno Latour’s actor-network theory¹¹, CQ integrates three interdependent dimensions—clinical precision, behavioral reinforcement, and social trust—to transform AI fromepisodic prediction into continuous and participatory engagement. CQ thus offers aconcise, unified conceptual foundation for equitable and human-centered digital publichealth systems. Its novelty lies in defining continuity as the operational measure ofintelligence—linking ethical governance, behavioral adaptation, and clinical precision intoa sustained framework applicable to chronic disease management, mental health, andelder care.