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Effective resource management using machine learning in medicine: an applied example
  1. Alan Williams1,
  2. Ann-Marie Mekhail2,3,
  3. James Williams4,
  4. Johanna McCord5,
  5. Vanessa Buchan6
  1. 1 Canterbury University, Canterbury District Health Board, Christchurch, New Zealand
  2. 2 Intensive Care Unit, Barts Health NHS Trust, London, UK
  3. 3 School of Medicine, University of Otago, Wellington, New Zealand
  4. 4 Yale University; Canterbury District Health Board, New Haven, Connecticut, USA
  5. 5 Via Innovations, Canterbury District Health Board, Christchurch, New Zealand
  6. 6 Canterbury Health Laboratories, Christchurch, New Zealand
  1. Correspondence to Dr Ann-Marie Mekhail, Intensive Care Unit, Barts Health NHS Trust, Whitechapel E1 1BB, UK; apamekhail{at}


Background The field of medicine is rapidly becoming digitised, and in the process passively amassing large volumes of healthcare data. Machine learning and data analytics are advancing rapidly, but these have been slow to be taken up in the day-to-day delivery of healthcare. We present an application of machine learning to optimise a laboratory testing programme as an example of benefiting from these tools.

Methods Canterbury District Health Board has recently implemented a system for urgent lab sample processing in the community, reducing unnecessary emergency presentations to hospital. Samples are transported from primary care facilities to a central laboratory. To improve the efficiency of this service, our team built a prototype transport scheduling platform using machine learning techniques and simulated the efficiency and cost impact of the platform using historical data.

Results Our simulation demonstrated procedural efficiency and potential for annual savings between 5% and 14% from implementing a real-time lab sample transport scheduling platform. Advantages included providing a forward job list to the laboratory, an expected time to result and a streamlined transport request process.

Conclusion There are a range of opportunities in healthcare to use large datasets for improved delivery of care. We have described an applied example of using machine learning techniques to improve the efficiency of community patient lab sample processing at scale. This is with a view to demonstrating practical avenues for collaboration between clinicians and machine learning engineers.

  • inefficiency in health
  • resource management big data
  • primary care
  • healthcare resource utilization
  • clinical informatics
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  • JMC and VB contributed equally.

  • Contributors AW: Machine learning model design and build, data analysis and manuscript write up. A-MM: Manuscript write up and contexualisation. JM: Project initiation, advice, and review JW: Model validation and manuscript review VB: Project initiation, advice, and review

  • Funding This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests AW and JW are co-founders of the software company Isogonal.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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