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A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

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AI-Enabled Remote Patient Monitoring for Heart Disease: A Conceptual Framework for Clinical Integration.

Abdulwahhab Alshammari^1,2,3, Abdulaziz Alamri^1,2,3

¹College of Public Health and Health Informatics, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.

Studies in Health Technology and Informatics

|July 3, 2026

Summary

Remote patient monitoring (RPM) enhances heart disease care by improving early detection. An AI framework addresses data challenges, enabling proactive, patient-centered health management and better clinical outcomes.

Keywords:

Artificial Intelligence Clinical Decision Support Heart Disease Remote Patient Monitoring

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Area of Science:

Cardiology
Health Informatics
Artificial Intelligence

Background:

Remote patient monitoring (RPM) is crucial for managing heart disease.
Existing RPM methods show benefits in early detection and intervention.
Challenges include data overload and clinical workflow integration.

Purpose of the Study:

To review evidence on RPM's impact on cardiac patient outcomes.
To propose an AI-enabled framework to overcome RPM challenges.
To discuss ethical and implementation considerations for AI in RPM.

Main Methods:

Review of current evidence on RPM for heart disease.
Proposal of an AI-enabled framework for data collection, analysis, and decision support.
Discussion of ethical, privacy, and implementation factors.

Main Results:

RPM demonstrates effectiveness in early detection and intervention for heart disease.
An AI framework can mitigate data overload and improve clinical integration.
The proposed framework supports earlier risk detection and enhanced clinical responses.

Conclusions:

Integrating AI into RPM offers a practical solution for proactive cardiac care.
AI-driven RPM can lead to more effective, patient-centered management.
Addressing ethical and implementation issues is key for successful AI adoption in digital health.