From Trust Erosion to Health System Collapse: SEA-Based Framework of Trust Breakdown and Early Warning Signals

Abstract

Trust is increasingly recognized as a fundamental component of health systems; however, it is commonly regarded as an outcome of health system performance rather than a dynamic indicator of emerging system vulnerability. Existing performance assessment frameworks rarely incorporate trust erosion as an early warning signal preceding health system saturation and collapse.

This study aimed to find a conceptual SEA (Stability–Efficiency–Adaptability)-based framework explaining how trust progressively deteriorates across multiple levels of the health system and how this process can be translated into measurable early warning indicators.

A qualitative exploratory study using an abductive design was conducted through semi-structured interviews with 20 participants representing policymakers, healthcare managers, clinicians, researchers, digital health experts, and community representatives. Data were analyzed using open, axial, and selective coding before findings were organized within the SEA framework to identify trust dimensions, determinants, erosion mechanisms, and potential early warning indicators.

Trust emerged as a multidimensional system property operating across individual, interpersonal, professional, organizational, informational, public, and adaptive domains. Participants identified governance failures, workforce burnout, declining service quality, misinformation, financial pressures, and reduced institutional legitimacy as principal drivers of trust erosion. Progressive deterioration manifested through declining cooperation, increasing operational friction, reduced public engagement, and weakened adaptive capacity. These findings were translated into a preliminary framework of measurable trust-sensitive indicators and risk thresholds for monitoring health system vulnerability.Repositioning trust erosion as an early warning signal rather than a downstream outcome provides a novel systems-based approach for anticipating health system saturation and supporting timely policy interventions before irreversible system collapse occurs.