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Considerations for Quality Control Monitoring of Machine Learning Models in Clinical Practice.

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Maintaining machine learning (ML) model performance in clinical settings requires robust monitoring. This study presents a practical platform and guidelines for integrating ML model monitoring into healthcare workflows, addressing real-world implementation challenges.

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Area of Science:

  • Clinical informatics
  • Machine learning in healthcare
  • Software engineering for medical AI

Background:

  • Machine learning (ML) models in clinical practice require ongoing performance monitoring to ensure efficacy.
  • Existing literature often focuses on performance decline detection, with less emphasis on broader integration and maintenance challenges.
  • Real-world deployment necessitates practical solutions for monitoring ML model performance over time.

Purpose of the Study:

  • To detail the development and use of a platform for monitoring a production-level ML model at Mayo Clinic.
  • To provide considerations and guidelines for integrating ML model monitoring platforms into technical infrastructure and workflows.
  • To document experiences, challenges, and solutions related to the real-world implementation and maintenance of such platforms.

Main Methods:

  • Developed an R Shiny application as a monitoring platform over six months.
  • Documented the integration process, focusing on feasibility, design, implementation, and policy considerations.
  • Included source code for the monitoring platform to facilitate adoption.

Main Results:

  • The monitoring platform has been in use and maintained for two years (as of July 2023).
  • Identified four key pillars for implementation: feasibility, design, implementation, and policy.
  • Highlighted challenges beyond methodological performance change detection for successful real-world deployment.

Conclusions:

  • Successful integration of ML monitoring platforms requires addressing practical aspects like resources, design, IT infrastructure, and policy.
  • The developed platform and documented guidelines offer a practical approach to maintaining ML model efficacy in clinical settings.
  • Further work is needed to address the broader implementation and maintenance challenges of ML monitoring solutions.