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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 2, 2025

Author Spotlight: Enhancing Rheumatoid Arthritis Research Through HR-pQCT Imaging Analysis

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Published on: October 6, 2023

Advancing Rheumatology Care Through Machine Learning.

Thomas Hügle¹

¹Department of Rheumatology, University Hospital Lausanne (CHUV) and University of Lausanne, Avenue Pierre-Decker 4, 1001, Lausanne, Switzerland. Thomas.Hugle@chuv.ch.

Pharmaceutical Medicine

|February 29, 2024

Summary

This summary is machine-generated.

Machine learning and digital biomarkers are revolutionizing rheumatology by improving disease assessment and treatment. These technologies enhance clinical decision support and patient monitoring, paving the way for personalized care and more effective clinical trials.

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Area of Science:

Rheumatology
Digital Medicine
Artificial Intelligence in Healthcare

Background:

Rheumatologic diseases present complex challenges in assessing activity and achieving remission.
Traditional clinical trial outcome measures may not fully capture drug efficacy.
The COVID-19 pandemic accelerated digital health adoption, including telemonitoring and patient-reported data.

Purpose of the Study:

To explore the potential of machine learning, digital biomarkers, and advanced imaging in rheumatology.
To enhance clinical decision support and optimize treatment strategies for rheumatologic conditions.
To improve patient monitoring and facilitate decentralized clinical trials.

Main Methods:

Utilizing machine learning algorithms, including convolutional neural networks (CNNs), for radiological image analysis.
Implementing digital biomarkers from patient-reported outcomes and wearable devices.
Developing prediction models for disease activity and treatment response.
Employing clustering techniques for personalized patient care.

Main Results:

Machine learning aids in detecting specific rheumatologic lesions (e.g., erosions, sacroiliitis) via image analysis.
Digital biomarkers offer flexible insights into disease progression and treatment response outside clinical visits.
Prediction models can forecast disease activity and guide medication choices.
CNNs show FDA-approved applications in radiological assessment.

Conclusions:

Machine learning, digital biomarkers, and advanced imaging significantly promise to improve rheumatology clinical decision support and trials.
Integration of these technologies requires a multidisciplinary approach and ongoing validation.
These advancements support a shift towards patient-centered, decentralized rheumatologic care.