TY - JOUR
T1 - Maternal Risk Stratification from Prenatal Care Trajectories: A Continuity-Aware Modeling Framework for Preventable Harm
AU - Patrick O’Connor
AU - Sean Murphy
JF - Journal of Health Informatics and Digital Systems
JO - J. Health Inform. Digit. Syst.
SN - 3149-8973
Y1 - 2025
VL - 5
IS - 2
SP - 52
N2 - Maternal healthcare faces escalating challenges in identifying preventable harms during pregnancy, where fragmented prenatal care trajectories often obscure emerging risks. This conceptual manuscript introduces a novel continuity-aware modeling framework designed to stratify maternal risks by integrating longitudinal care trajectories into a cohesive analytical architecture. Drawing on theoretical principles from systems engineering and healthcare informatics, the framework emphasizes the orchestration of prenatal data streams to enhance risk detection without relying on empirical datasets or performance metrics. Key components include modular layers for trajectory mapping, continuity assessment, and harm anticipation, supported by interpretive formulas that model risk propagation and decision confidence. By prioritizing infrastructural resilience and governance integration, this approach theorizes improved alignment between clinical workflows and preventive strategies, potentially mitigating disparities in maternal outcomes. The discussion synthesizes literature on machine learning applications in perinatal risk prediction and midwifery continuity models, highlighting architectural innovations for sustainable deployment in diverse healthcare environments. Ultimately, this framework advocates for a paradigm shift toward proactive, continuity-centric systems in maternal risk management, fostering theoretical advancements in AI-driven healthcare analytics.
UR - https://cirpublications.com/d807010582
ER -