2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 17 , Issue 2 , PP: 214-227, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Sasikumar M. S. S. 1 , Ranganayaki V. C. 2 , R. Suganthi 3 , Nalini Subramanian 4 , T. Sethukarasi 5 * , T. A. Mohanaprakash 6 *
Doi: https://doi.org/10.54216/JISIoT.170213
The integration of Clinical Informatics (NI) and Artificial Intelligence (AI) promises to transform healthcare by improving clinical decisions, optimizing workflows, and personalizing patient care. However, most current systems fail to incorporate contextual reasoning, real-time adaptation, or ethical sensitivity, leading to fragmented support and increased cognitive burden on clinicians. To address these limitations, we propose NI-AIH—a hybrid clinical-AI framework built on a Context-Enriched Hierarchical Attention Network (CE-HAN). This deep architecture employs dual-attention mechanisms to interpret structured and unstructured clinical data—including EHR entries, nursing notes, and real-time IoT sensor feeds—capturing temporal patterns and contextual cues essential to patient status. The NI-AIH framework consists of four core components: a Clinical Context Engine (CCE) that uses CE-HAN for semantic modeling; a Predictive Care Optimizer (PCO) that applies risk-stratified deep ensembles; an Adaptive Interaction Layer (AIL) that enables seamless nurse–AI collaboration; and an Ethical Decision Integrator (EDI) that uses fuzzy logic to ensure real-time ethical alignment. In a trial deployment within a smart geriatric care unit, NI-AIH demonstrated a 23% improvement in early sepsis detection (p<0.01), a 31% reduction in clinician cognitive load (measured via NASA-TLX survey), and a 19% increase in workflow efficiency compared to conventional rule-based systems. By uniting clinical precision with ethical and context-aware intelligence, NI-AIH establishes a new paradigm for compassionate and effective AI-assisted healthcare.
Clinical Decision Support Systems , Hierarchical Attention Network , Predictive Analytics , IoT in Healthcare , Ethical AI
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