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Issues And Trends In Healthcare Delivery System

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...

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Related Experiment Video

Updated: Sep 9, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

AI-Powered Adaptive Disability Prediction and Healthcare Analytics Using Smart Technologies.

Malak Alamri^1,2, Mamoona Humayun³, Khalid Haseeb⁴

¹Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72311, Saudi Arabia.

Diagnostics (Basel, Switzerland)

|August 28, 2025

Summary

This summary is machine-generated.

This study introduces an adaptive framework using the Internet of Medical Things (IoMT), edge computing, and AI for accurate, real-time disability identification. It enhances data security and personalized healthcare interventions for better chronic disease management.

Keywords:

artificial intelligence disease diagnosis edge computing healthcare system wearable sensors

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Last Updated: Sep 9, 2025

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Published on: April 12, 2021

Area of Science:

Health Informatics
Biomedical Engineering
Artificial Intelligence in Healthcare

Background:

Healthcare Industry 5.0 utilizes wireless technologies for continuous patient monitoring via biosensors.
Existing systems face scalability and reliability challenges in timely personalized disease detection.
Continuous monitoring is crucial for accurate diagnosis, especially for chronic conditions.

Purpose of the Study:

To propose an adaptive and secure framework for disability identification using IoMT, edge computing, and AI.
To address limitations in timely disease detection and enhance personalized healthcare.
To improve the security and reliability of medical data analytics.

Main Methods:

Integration of lightweight edge computing for rapid data collection from biosensors.
Implementation of decentralized strategies for secure big data analytics and authentic data access.
Utilizing federated learning and lightweight encryption for data protection.

Main Results:

High accuracy in edge-based disability detection.
Enhanced prompt response times for cloud servers.
Guaranteed security of sensitive medical data through advanced techniques.

Conclusions:

The proposed framework provides a secure and efficient solution for disability identification.
Leverages IoMT, edge computing, and AI to overcome real-time monitoring challenges.
Enhances diagnostic accuracy and ensures sensitive medical data protection.