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DEPLOYR: a technical framework for deploying custom real-time machine learning models into the electronic medical

Conor K Corbin1, Rob Maclay2, Aakash Acharya3

  • 1Department of Biomedical Data Science, Stanford, California, USA.

Journal of the American Medical Informatics Association : JAMIA
|June 27, 2023
PubMed
Summary

We developed DEPLOYR, a technical framework for deploying machine learning models into electronic medical record systems. This framework enables real-time monitoring and prospective evaluation, addressing the gap in clinical translation of AI tools.

Keywords:
artificial intelligenceclinical decision supportcomputational infrastructurehealthcare organizationsmachine learningorganizational readiness

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Area of Science:

  • Clinical Informatics
  • Machine Learning in Healthcare
  • Health IT

Background:

  • Healthcare institutions need robust frameworks for implementing machine learning (ML) models in clinical workflows.
  • Existing governance frameworks require technical solutions for efficient, safe, and high-quality model deployment.
  • Bridging the gap between ML research and clinical application is crucial for improving patient care.

Purpose of the Study:

  • To introduce DEPLOYR, a technical framework for real-time deployment and monitoring of researcher-created ML models.
  • To enable seamless integration of ML models into widely used electronic medical record (EMR) systems.
  • To inform best practices for ML model deployment in healthcare settings.

Main Methods:

  • DEPLOYR facilitates model deployment triggered by EMR actions and real-time data collection for inference.
  • It includes mechanisms for displaying inferences within clinician workflows and monitoring model performance over time.
  • The framework supports silent deployment and prospective evaluation of deployed models.

Main Results:

  • DEPLOYR was used to silently deploy and evaluate 12 ML models predicting laboratory diagnostic results in an EMR system.
  • Models were triggered by clinician actions (button-clicks) within the EMR.
  • Prospective evaluation demonstrated variations from retrospective performance estimates.

Conclusions:

  • Silent deployment of ML models in healthcare is feasible and necessary due to performance discrepancies between retrospective and prospective estimates.
  • Prospective performance measures should guide final decisions for model deployment.
  • DEPLOYR aims to facilitate the translation of ML models into clinical practice.