An artificial intelligence-based intelligent postoperative nursing monitoring system for patient safety (OBG, CHN, MHN, MSN, community)

Postoperative patient safety depends heavily on timely recognition of physiological deterioration, accurate nursing assessment, effective communication and rapid escalation of care. Conventional postoperative monitoring frequently relies on intermittent measurement of vital signs and periodic clinical observations. Although this approach remains fundamental to nursing practice, subtle physiological changes may develop between observations and may be missed until the patient's condition has significantly deteriorated. Advances in Artificial Intelligence (AI), machine learning, wearable sensors, Electronic Health Records (EHRs), predictive analytics and clinical decision-support systems provide an opportunity to transform postoperative nursing surveillance from predominantly episodic observation into continuous, risk-adaptive and intelligent monitoring. This review discusses the conceptual development and potential clinical application of an AI-based intelligent postoperative nursing monitoring system designed to support patient safety across obstetric and gynaecological nursing (OBG), Community Health Nursing (CHN), Mental Health Nursing (MHN), Medical-Surgical Nursing (MSN) and community-based postoperative care. An intelligent monitoring system could integrate physiological measurements, nursing observations, medication information, laboratory findings, operative characteristics, patient-reported symptoms, mobility data and historical clinical information to identify deviations from an individual's expected postoperative trajectory. Machine-learning algorithms could generate dynamic risk scores for complications such as respiratory depression, haemodynamic instability, sepsis, postoperative haemorrhage, wound complications, delirium, falls, acute kidney injury, thromboembolic events and deterioration in mental status. Rather than replacing nurses, the system could prioritise patients, reduce information overload and provide interpretable alerts that support clinical judgement. Continuous postoperative monitoring has demonstrated improved detection of physiological abnormalities, particularly oxygen desaturation, although evidence that monitoring alone consistently reduces mortality or intensive care admission remains less certain. Therefore, AI should be implemented as part of a human-centred safety system rather than as an autonomous decision-maker. The review further examines specialty-specific applications in OBG, CHN, MHN and MSN, including maternal postoperative surveillance, caesarean-section recovery, surgical ward monitoring, psychiatric assessment, medication safety, discharge surveillance and community follow-up. Important challenges include false alarms, algorithmic bias, poor data quality, interoperability, cybersecurity, privacy, explainability, alert fatigue, unequal access and professional accountability. A proposed framework consisting of data acquisition, preprocessing, risk prediction, clinical interpretation, nursing action, escalation and continuous learning is presented. Responsible implementation requires prospective validation, external validation, human factors evaluation, nurse involvement in system design, appropriate governance and continuous post-deployment monitoring. AI-based postoperative nursing surveillance has considerable potential to strengthen patient safety, but its clinical value will depend not simply on predictive accuracy but on whether predictions lead to timely, appropriate and equitable nursing interventions.