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Issues And Trends In Healthcare Delivery System01:29

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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.
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Machine Learning Applied to Routinely Collected Health Administrative Data.

Laura C Rosella1, Vinyas Harish2

  • 1The site director at ICES UofT and an associate professor and education lead at the Temerty Centre for Artificial Intelligence Research and Education in Medicine, University of Toronto in Toronto, ON. Laura may be contacted by e-mail at laura.rosella@utoronto.ca.

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Summary
This summary is machine-generated.

Machine learning models are increasingly used in healthcare. This study reviews recent advancements in machine learning using health administrative data for clinical applications and health systems.

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Area of Science:

  • Health Informatics
  • Machine Learning
  • Clinical Applications

Background:

  • Machine learning algorithms are rapidly advancing for clinical use.
  • Large health administrative databases are crucial for developing these models.

Purpose of the Study:

  • To survey recent machine learning models developed using health administrative data.
  • To highlight key areas of development for health system applications.

Main Methods:

  • Review of machine learning models.
  • Analysis of data from health administrative databases at ICES.

Main Results:

  • Identification of recent machine learning models in clinical applications.
  • Highlighting three critical areas of ongoing development.

Conclusions:

  • Machine learning shows significant promise for improving health system applications.
  • Continued development is essential for leveraging health administrative data effectively.