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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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Updated: Jul 1, 2026

A Thrombotic Stroke Model Based On Transient Cerebral Hypoxia-ischemia

A Thrombotic Stroke Model Based On Transient Cerebral Hypoxia-ischemia

Published on: August 18, 2015

Blockchain-enabled digital twin system for brain stroke prediction.

Venkatesh Upadrista¹, Sajid Nazir², Huaglory Tianfield²

¹Department of Computing, Glasgow Caledonian University, Glasgow, G4 0BA, Scotland. vupadr200@caledonian.ac.uk.

Brain Informatics

|January 14, 2025

Summary

This summary is machine-generated.

This study introduces a secure, machine learning digital twin for healthcare, achieving 98.28% accuracy in brain stroke prediction. The system enhances data security and scalability for predicting serious health conditions.

Keywords:

Extendibility Internet of medical things Machine learning Scalability Security and privacy

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Area of Science:

Digital Health
Machine Learning
Health Informatics

Background:

Digital twins offer real-time virtual modeling for health monitoring (diet, sleep, activity).
Current digital twin applications show limited accuracy in predicting severe conditions like heart attacks, strokes, and cancers.
Data security and privacy concerns hinder widespread adoption of healthcare digital twins.

Purpose of the Study:

To develop a secure, machine learning-powered digital twin application.
To enhance prediction accuracy for serious health conditions.
To strengthen data security and ensure scalability for broader healthcare applications.

Main Methods:

Developed a secure digital twin application integrating machine learning and consortium blockchain technology.
Focused on enhancing prediction accuracy, data security, and system scalability.
Utilized a selected dataset for evaluating brain stroke prediction accuracy.

Main Results:

Achieved 98.28% accuracy in predicting brain strokes using the developed application.
Enhanced data security through the integration of consortium blockchain with machine learning, ensuring tamper-proof data.
Demonstrated the application's capability to detect and correct data anomalies, maintaining robust data protection.

Conclusions:

The developed digital twin application significantly improves prediction accuracy for serious health conditions.
Integrated blockchain technology provides robust data security and tamper-proofing for healthcare digital twins.
The scalable architecture allows for extension to monitor various other pathologies with minimal adjustments.