Remote Patient Monitoring System for Post-Surgical Care (CABG - Cardiac Artery Bypass Graft Surgery)

Ensuring complete recovery after surgery is a challenging phase that requires continuous monitoring. Many existing approaches lack real-time intervention capabilities which results in less-than-ideal outcomes. This project proposes an RPM system intended for post-surgical care, particularly aimed at CABG recovery. It enables the combination of self-reported outcomes with the real-time tracking of their heart rate, oxygen saturation, and pain levels. Patients and healthcare providers can interact seamlessly through the web-based platform which allows real-time data visibility, automated alerting during critical deviation of monitored parameters, and follow-up management. Experimental results show their ability in improving recovery rates while Lessing the load on healthcare facilities. Improved Predictive analytics in ml will refine the post-Surgical care

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Publication Details

Published
2025-04-04
DOI
https://doi.org/10.1109/icaeca63854.2025.11012560
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
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article

Remote Patient Monitoring System for Post-Surgical Care (CABG - Cardiac Artery Bypass Graft Surgery)

S Sathyavathi, J Vinothini
IoT and Edge/Fog Computing
article

Remote Patient Monitoring System for Post-Surgical Care (CABG - Cardiac Artery Bypass Graft Surgery)

S Sathyavathi, J Vinothini
article en

Abstract

Ensuring complete recovery after surgery is a challenging phase that requires continuous monitoring. Many existing approaches lack real-time intervention capabilities which results in less-than-ideal outcomes. This project proposes an RPM system intended for post-surgical care, particularly aimed at CABG recovery. It enables the combination of self-reported outcomes with the real-time tracking of their heart rate, oxygen saturation, and pain levels. Patients and healthcare providers can interact seamlessly through the web-based platform which allows real-time data visibility, automated alerting during critical deviation of monitored parameters, and follow-up management. Experimental results show their ability in improving recovery rates while Lessing the load on healthcare facilities. Improved Predictive analytics in ml will refine the post-Surgical care

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IoT and Edge/Fog Computing
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