Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/4109 2019 2019 Assistant Professor (Senior Grade), Department of Computer Science & Engineering School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, India T. T. Assitant professor , Department of Computer Science and Engineering, St.Joseph’s Institute of Technology, OMR, Chennai, Tamilnadu, India Ranganayaki V.. C. Associate Professor, Department of ECE, Panimalar Engineering College, Chennai, Tamilnadu, India R. Suganthi Associate Professor, Department of Information Technology, Rajalakshmi Engineering College, Thandalam, Chennai, Tamil Nadu, India Nalini Subramanian Professor & Head Department of Computer Science and Engineering, R.M.K Engineering College , Kavaraipettai, Chennai, India T. Sethukarasi Associate Professor, Department of Computer Science and Engineering, RMK Engineering College , Kavaraipettai , Chennai, India T. A. Mohanaprakash 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 2025 2025 214 227 10.54216/JISIoT.170213 https://www.americaspg.com/articleinfo/18/show/4109