From Prediction to Decision: The Decision Integration Deficit Index (DIDI) and Structural Imbalance in AI-Driven Digital Health Systems

Abstract

Artificial intelligence (AI) has significantly advanced predictive capabilities in digital health systems; however, the structural integration of these predictions into formal decision-making processes remains insufficiently addressed. This study introduces the Decision Integration Deficit Index (DIDI), a structural diagnostic metric designed to assess the alignment between inference- and decision-oriented components in AI-driven health system architectures. A domain × domain integration matrix represents structurally possible and empirically observed relationships between system components, enabling the formal assessment of integration patterns. The framework suggests that apparent balance at an aggregated level may conceal substantial structural asymmetries, particularly in the limited integration of modelling outputs into formal evaluation and decision-support mechanisms. The results suggest that the analyzed corpus reflects structurally incomplete architectures, characterized by an imbalance in decision integration across domains. In contrast to performance-based evaluation metrics, the DIDI provides a system-level diagnostic perspective that identifies missing or weakly specified integration pathways within decision-process architectures. This study contributes to digital health and decision-support research by introducing a reproducible structural assessment framework that enables evaluation of decision-process completeness and supports the development of more coherent, transparent, and accountable AI-driven decision-support systems.

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