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Effective resource management using machine learning in medicine: an applied example.

Alan Williams1, Ann-Marie Mekhail2,3, James Williams4

  • 1Canterbury University, Canterbury District Health Board, Christchurch, New Zealand.

BMJ Simulation & Technology Enhanced Learning
|May 6, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning optimizes laboratory testing programs, demonstrating potential for 5-14% annual savings. This application of artificial intelligence enhances healthcare delivery through efficient community patient lab sample processing.

Keywords:
clinical informaticshealthcare resource utilizationinefficiency in healthprimary careresource management big data

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Area of Science:

  • Healthcare Informatics
  • Applied Machine Learning
  • Health Services Research

Background:

  • Healthcare is increasingly digital, generating vast amounts of data.
  • Adoption of machine learning and data analytics in daily healthcare delivery has been slow.
  • This study presents machine learning application to optimize laboratory testing programs.

Purpose of the Study:

  • To demonstrate the benefits of machine learning in healthcare.
  • To optimize a laboratory testing program for community patient sample processing.
  • To showcase practical collaboration between clinicians and machine learning engineers.

Main Methods:

  • Developed a prototype transport scheduling platform using machine learning techniques.
  • Simulated the platform's efficiency and cost impact using historical data from Canterbury District Health Board.
  • Focused on urgent lab sample processing in the community to reduce hospital emergency presentations.

Main Results:

  • The simulation demonstrated procedural efficiencies.
  • Potential for annual cost savings between 5% and 14% was identified.
  • Key advantages included a forward job list for the laboratory, expected time-to-result, and a streamlined transport request process.

Conclusions:

  • Healthcare has numerous opportunities for improving care delivery using large datasets.
  • Machine learning techniques can significantly improve the efficiency of community patient lab sample processing.
  • This work exemplifies practical applications of machine learning in healthcare settings.