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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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Role of Artificial Intelligence in TeleStroke: An Overview.

Faryal Ali¹, Umair Hamid², Osama Zaidat³

¹Department of Medicine, Dow University of Health Sciences, Karachi, Pakistan.

Frontiers in Neurology

|October 29, 2020

Summary

Artificial Intelligence (AI) enhances telestroke care through faster diagnosis and prognosis prediction. These technologies improve efficiency, reduce physician burnout, and lower costs for better patient outcomes.

Keywords:

artificial intelligence machine learning telehealth telemedicine teleneurology telestroke

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Area of Science:

Neurology
Medical Informatics
Artificial Intelligence

Background:

Teleneurology expands access to specialized neurological care, particularly for stroke patients.
The integration of Artificial Intelligence (AI) into telestroke services offers transformative potential.

Purpose of the Study:

To provide an overview of the multifaceted applications and benefits of AI in telestroke care.
To highlight how AI can revolutionize diagnosis, patient management, and research in telestroke.

Main Methods:

Utilizing AI and machine learning for enhanced medical imaging analysis in stroke diagnosis.
Implementing intelligent Electronic Medical Records (EMRs) with natural language processing for decision support.
Leveraging advanced speech recognition for improved clinical documentation and communication.
Employing smart communication and alert systems for optimized patient flow and outcomes.
Automating data collection and analysis for quality improvement and research initiatives.

Main Results:

AI facilitates more rapid and reliable diagnosis through advanced imaging analysis.
Predictive capabilities of AI assist in forecasting hospital course and long-term prognosis.
Intelligent EMRs enhance clinical decision-making by analyzing patient data.
AI-driven communication tools improve care efficiency and patient outcomes.
Automated data processes streamline quality improvement and research efforts.

Conclusions:

Synergistic AI technologies promise to enhance the clinician experience and reduce burnout.
AI integration in telestroke can lead to improved patient health outcomes.
These advancements offer the potential for more cost-effective stroke care delivery.