TY - JOUR
T1 - A Natural Language–Driven Clinical Risk Intelligence Layer for EHR Ecosystems
AU - Hiroshi Tanaka
AU - Yuki Sato
AU - Kenji Mori
AU - Rina Okabe
AU - Takashi Ito
JF - Journal of Artificial Intelligence for Healthcare Systems
JO - J. Artif. Intell. Healthc. Syst.
SN - 3149-8981
Y1 - 2022
VL - 1
IS - 1
SP - 3
N2 - The integration of natural language processing (NLP) into electronic health record (EHR) systems represents a pivotal advancement in clinical risk management, enabling real-time extraction of intelligence from unstructured clinical narratives. This conceptual manuscript proposes the natural language risk intelligence nexus (NLRIN), a layered architecture that embeds NLP-driven risk analytics within EHR infrastructures. By orchestrating semantic parsing, risk ontology mapping, and adaptive governance protocols, NLRIN facilitates proactive clinical decision support without relying on empirical models or performance metrics. We synthesize literature from 2017 to 2021 on AI-enabled healthcare systems, highlighting gaps in NLP integration for risk intelligence. The framework emphasizes interoperability with existing EHR workflows, privacy-preserving data flows, and human-AI collaboration dynamics. Conceptual formulas illustrate risk propagation through NLP layers and governance load in federated ecosystems. This work underscores the potential for NLRIN to enhance clinical vigilance, reduce diagnostic latency, and foster resilient health informatics infrastructures, while addressing ethical considerations in AI-augmented risk assessment. Ultimately, it advocates for a paradigm shift toward language-centric intelligence layers in healthcare analytics, promoting scalable, interpretable risk orchestration across diverse clinical settings.
UR - https://cirpublications.com/g729553429
ER -