Connected Postoperative Care Ecosystem for Remote Recovery Monitoring and Outcome-Oriented Clinical Management

Abstract

The delayed symptom identification, the lack of any follow up control and continuous monitoring of a patient after leaving the hospital still create a strong clinical and economical burden of the healthcare systems in the postoperative complications. This paper suggests a Connected Postoperative Care Ecosystem that is a combination of wearable biomedical sensors, Internet of Things (IoT)-based communication infrastructure, cloud based healthcare analytics, and artificial intelligence-assisted clinical decision support 
remote postoperative recovery support. The suggested system constantly checks such vital parameters as heart rate, oxygen saturation, body temperature, pain level, mobility activity, medication compliance, and signs of wound healing in real-time. A Long Short-Term Memory (LSTM) neural network predictive analytics module under the assistance of AI was developed to detect cases of abnormal recovery and possible postoperative problems at an early stage. Data on 180 patients who had an operation underwent experimental evaluation through data collected on these patients during a 30-day watch. The suggested ecosystem 
displayed better performance than traditional postoperative follow-up strategies, with recovery prediction accuracy of 96.2, sensitivity of 94.8 and specificity of 95.5 and F1-score of 95.1. Significant hospital readmission rates and greater compliance with and supervision efficiency of patients were statistically confirmed. The proposed connected healthcare ecosystem offers a clinically effective, intelligent, and scalable framework to provide personalized postoperative monitoring and outcome-based digital healthcare management.