Bedside data to clinical foresight: the emerging role of nurses in AI-enabled predictive care and early patient deterioration detection

R. Sheeba; N. Murugananandan; Tamilselvan; Tanusri Mandal; Israel Jeba Prabu D.; Jamunarani P.; Ilakkiya N.; Padmapriya N.; Nithin Manly S.; Golda Sahaya Rani R.; J. Sathya Shenbega Priya; Mohammed Umar

doi:10.18203/2320-6012.ijrms20263573

Authors

R. Sheeba Department of Obstetric and Gynaecological Nursing, KG College of Nursing, The Tamil Nadu Dr. M. G. R. Medical University, Coimbatore, Tamil Nadu, India
N. Murugananandan Department of Medical Surgical Nursing, College of Nursing, Madurai Medical College, The Tamil Nadu Dr. M. G. R. Medical University, Madurai, Tamil Nadu, India
Tamilselvan Department of Medical Surgical Nursing, Shri Babu Singh Daddu Ji Nursing and Paramedical College, Major S. D. Singh University, Farrukhabad, Uttar Pradesh, India
Tanusri Mandal Community Health Nursing, Government College of Nursing, Serampore Walsh SD/SS Hospital, The West Bengal University of Health Sciences, Serampore, Kolkata, West Bengal, India
Israel Jeba Prabu D. Department of Medical Surgical Nursing, BEE ENN College of Nursing, University of Jammu, Jammu, Jammu and Kashmir, India
Jamunarani P. Department of Psychiatric Nursing, KMCH College of Nursing, The Tamil Nadu Dr. M. G. R. Medical University, Coimbatore, Tamil Nadu, India
Ilakkiya N. Department of Psychiatric Nursing, Karpagam Nursing College, The Tamil Nadu Dr. M. G. R. Medical University, Coimbatore, Tamil Nadu, India
Padmapriya N. Department of Child Health Nursing, Karpagam Nursing College, The Tamil Nadu Dr. M. G. R. Medical University, Coimbatore, Tamil Nadu, India
Nithin Manly S. Department of Mental Health Nursing, School of Nursing, Takshashila University, Villupuram, Tamil Nadu, India
Golda Sahaya Rani R. Department of Medical Surgical Nursing, School of Nursing, Takshashila University, Villupuram, Tamil Nadu, India
J. Sathya Shenbega Priya Department of Medical Surgical Nursing, College of Nursing, Kannur Medical College, Kerala University of Health Sciences, Kannur, Kerala, India
Mohammed Umar Department of Nursing, Uttar Pradesh University of Medical Sciences (UPUMS), Uttar Pradesh University of Medical Sciences, Saifai, Etawah, Uttar Pradesh, India

DOI:

https://doi.org/10.18203/2320-6012.ijrms20263573

Keywords:

Artificial intelligence, Predictive analytics, Nursing, Early warning systems, Clinical deterioration, Machine learning, Patient safety, Digital health

Abstract

Early recognition of patient deterioration remains a cornerstone of high-quality nursing care and is essential for preventing avoidable morbidity, mortality, and unplanned intensive care admissions. Despite advances in physiological monitoring, delays in recognizing subtle clinical changes continue to contribute to adverse patient outcomes. Artificial intelligence (AI) has emerged as a transformative technology capable of integrating large volumes of clinical, physiological, laboratory, and electronic health record (EHR) data to generate predictive insights that support timely clinical decision-making. AI-enabled predictive care systems, including machine learning algorithms, deep learning models, wearable sensor analytics, and early warning systems, provide nurses with opportunities to identify high-risk patients before overt clinical deterioration occurs. Rather than replacing nurses, these technologies augment clinical judgment by facilitating continuous risk assessment, prioritization of care, and proactive intervention. This narrative review explores the evolving role of nurses in AI-enabled predictive care and early patient deterioration detection. It examines the integration of bedside clinical observations with AI-driven predictive analytics, highlights current applications across acute and critical care settings, and discusses implications for nursing education, leadership, ethical practice, and interdisciplinary collaboration. The review also addresses challenges related to algorithmic bias, explainability, data quality, digital literacy, and governance while identifying future opportunities for human-centered AI implementation. Strengthening nurses' competencies in digital health and AI is essential for maximizing patient safety and ensuring equitable, evidence-based care in increasingly technology-enabled healthcare environments.

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