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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: Jun 20, 2025

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

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Automated algorithm for medical data structuring, and segmentation using artificial intelligence within secured

Varatharajan Nainamalai¹, Hemin Ali Qair¹, Egidijus Pelanis^1,2

¹The Intervention Centre, Rikshospitalet, Oslo University Hospital, Oslo, Norway.

European Journal of Radiology Open

|July 23, 2024

Summary

This summary is machine-generated.

This study introduces a semi-automatic workflow to manage medical data, leveraging artificial intelligence (AI) for structuring electronic health records and creating accurate AI-ground truth labels for research.

Keywords:

Artificial intelligence Electronic health records Ground truth creation Segmentation Structured data

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Published on: December 15, 2023

Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Data Management

Background:

Electronic health records (EHRs) contain vast amounts of unstructured data.
Artificial intelligence (AI) offers potential for structuring diverse data types.
Efficient medical data management is crucial for research and clinical applications.

Purpose of the Study:

To present a semi-automatic workflow for medical dataset management.
To demonstrate AI's role in structuring unstructured medical data.
To facilitate the creation of AI-ground truth labels for research.

Main Methods:

Developed a semi-automatic workflow for data structuring, research extraction, and AI-ground truth creation.
Implemented an algorithm that organizes data directories based on file name keywords.
Utilized an AI model for initial label generation, with manual verification for ground truth.

Main Results:

Successfully organized computed tomography (CT), magnetic resonance (MR) images, clinical data, and annotations.
Generated initial AI labels that were manually refined into ground truth labels.
Integrated verified ground truth labels into a structured dataset for future research.

Conclusions:

The presented workflow enables efficient medical dataset management.
AI models can be trained on local hospital data for tailored outputs.
Automated algorithms and AI can be securely implemented within hospitals for data processing.