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Using Clinical Decision Intelligence Applications to Improve Pathways For Earlier Detection Of Underrecognized

A S Khachaturian1, B Cassin2, G R Finney3

  • 1Executive Vice-President, Campaign to Prevent Alzheimer's Disease 20/20.

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PubMed
Summary
This summary is machine-generated.

Global dementia care costs are soaring, straining healthcare systems. Implementing artificial intelligence/machine learning (AI/ML)-driven clinical decision intelligence applications (CDIA) requires ensuring action on insights for better dementia care preparedness.

Keywords:
AlzheimerDementiaartificial intelligenceclinical decision applicationshealthcare system preparednesshealthcare systemsmachine learningtechnology adoption

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Area of Science:

  • Gerontology
  • Healthcare Informatics
  • Artificial Intelligence in Medicine

Background:

  • Global costs for dementia care are escalating, projected to reach $1 trillion by 2025.
  • Existing healthcare infrastructure faces challenges in timely dementia identification, especially in underserved areas.
  • Undiagnosed cognitive impairments pose a significant risk to healthcare system capacity.

Purpose of the Study:

  • To highlight the growing global burden of dementia and cognitive impairments.
  • To identify critical barriers in current healthcare systems for dementia patient identification.
  • To explore the potential of healthcare bioinformatics and AI/ML-driven CDIA for improved preparedness.

Main Methods:

  • Review of current global healthcare infrastructure limitations.
  • Analysis of cost projections for dementia care.
  • Exploration of AI/ML-driven clinical decision intelligence applications (CDIA) as a solution.

Main Results:

  • Healthcare systems worldwide are ill-equipped for the rising tide of dementia cases.
  • Lack of personnel, infrastructure, and diagnostics hinders early detection.
  • AI/ML-CDIA present a promising avenue, but require user adoption for effectiveness.

Conclusions:

  • Urgent implementation of preparedness plans is necessary to meet future demands.
  • Healthcare bioinformatics and AI/ML-CDIA can enhance access and efficiency.
  • Ensuring patient and practitioner action on AI/ML insights is paramount for successful implementation.