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An integration engineering framework for machine learning in healthcare.

Azadeh Assadi1,2, Peter C Laussen3,4, Andrew J Goodwin1,5

  • 1Department of Critical Care Medicine, Hospital for Sick Children, Toronto, ON, Canada.

Frontiers in Digital Health
|August 22, 2022
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Summary
This summary is machine-generated.

A new systems engineering framework guides the integration of Machine Learning models in healthcare, addressing challenges in clinical adoption and improving patient outcomes. This approach ensures efficient and safe implementation of AI in the complex healthcare system.

Keywords:
Integration engineeringartificial intelligencedigital healthhealthcare (MeSH)human factors engineering (HFE)machine learningsystem of systems (SoS)

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Area of Science:

  • Healthcare Systems Engineering
  • Clinical Machine Learning Integration
  • Artificial Intelligence in Medicine

Background:

  • Machine Learning (ML) in healthcare offers significant potential for improving patient outcomes, team performance, and cost reduction.
  • However, a low rate of successful clinical integration of ML models leads to inefficiencies, increased costs, and potential patient harm due to a lack of standardized guidelines.
  • Systems engineering principles, widely used in industry for complex system integration, provide a robust model for addressing these challenges in healthcare.

Purpose of the Study:

  • To propose a novel framework for the development and integration of Machine Learning models in healthcare, grounded in systems engineering principles.
  • To address the limitations and challenges identified in applied systems engineering, software engineering, and healthcare ML development.
  • To create a harmonized approach that integrates ML software development with systems engineering practices for the healthcare domain.

Main Methods:

  • A critical appraisal of applied systems engineering, software engineering, and healthcare ML software development practices was conducted.
  • Principles of systems engineering were adapted and applied to develop solutions for identified integration problems.
  • The developed framework was aligned with the ML software development lifecycle.

Main Results:

  • An integration framework for healthcare Artificial Intelligence (AI) was developed, considering the entire 'system of systems'.
  • The framework employs a combined software and integration engineering approach, structured into four phases: Inception, Preparation, Development, and Integration.
  • Each phase addresses critical elements within the domains of The Human, The Technical System, and The Environment, including their interactions.

Conclusions:

  • Clinical ML models are technical systems requiring careful integration into the broader healthcare 'system of systems'.
  • A systems engineering approach ensures that essential elements are considered throughout the model design and integration process.
  • The proposed framework serves as a guide for ML model development, enhancing the probability of successful clinical translation and integration.