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Abstract
Postoperative complications are a significant contributor to hospital readmissions and increased healthcare costs. Early detection of physiological deterioration during the recovery period can mitigate adverse outcomes. In recent years, wearable biosensors have emerged as a promising technology for real-time health monitoring. This paper proposes a real-time monitoring system utilizing wearable biosensor data to track key physiological parameters of postoperative patients. The system incorporates machine learning algorithms for anomaly detection and provides clinicians with alerts in case of deviations from expected recovery trajectories. Designed within the technological landscape, this approach emphasizes interoperability with existing hospital IT infrastructures and focuses on non-invasive, continuous patient observation.
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