An integration engineering framework for machine learning in healthcare

Azadeh Assadi; Peter C Laussen; Andrew J Goodwin; Sebastian Goodfellow; William Dixon; Robert W Greer; Anusha Jegatheeswaran; Devin Singh; Melissa McCradden; Sara N Gallant; Anna Goldenberg; Danny Eytan; Mjaye L Mazwi

doi:10.3389/fdgth.2022.932411

An integration engineering framework for machine learning in healthcare

Front Digit Health. 2022 Aug 4:4:932411. doi: 10.3389/fdgth.2022.932411. eCollection 2022.

Authors

Azadeh Assadi^{1

2}, Peter C Laussen^{3

4}, Andrew J Goodwin^{1

5}, Sebastian Goodfellow⁶, William Dixon¹, Robert W Greer¹, Anusha Jegatheeswaran⁷, Devin Singh^{8

9}, Melissa McCradden^{10

11

12}, Sara N Gallant¹, Anna Goldenberg^{12

13

14

15}, Danny Eytan^{1

16

17}, Mjaye L Mazwi^{1

3

8}

Affiliations

¹ Department of Critical Care Medicine, Hospital for Sick Children, Toronto, ON, Canada.
² Institute of Biomaterials and Biomedical Engineering, Department of Engineering and Applied Sciences, University of Toronto, Toronto, ON, Canada.
³ Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
⁴ Executive Vice President for Health Affairs, Boston Children's Hospital, Boston, MA, United States.
⁵ School of Biomedical Engineering, University of Sydney, Sydney, NSW, Australia.
⁶ Department of Civil and Mineral Engineering, Faculty of Applied Science and Engineering, University of Toronto, Toronto, ON, Canada.
⁷ Department of Surgery, Division of Paediatric Cardiac Surgery, Hospital for Sick Children, Toronto, ON, Canada.
⁸ Translational Medicine, Peter Gilgan Centre for Research & Learning, Toronto, ON, Canada.
⁹ Department of Emergency Medicine, The Hospital for Sick Children, Toronto, ON, Canada.
¹⁰ Department of Bioethics, The Hospital for Sick Children, Toronto, ON, Canada.
¹¹ Division of Clinical and Public Health, Dalla Lana School of Public Health, Toronto, ON, Canada.
¹² Genetics & Genome Biology, Peter Gilgan Centre for Research & Learning, Toronto, ON, Canada.
¹³ Department of Computer Science, University of Toronto, Toronto, ON, Canada.
¹⁴ Vector institute for Artificial Intelligence, University of Toronto, Toronto, ON, Canada.
¹⁵ CIFAR, Toronto, ON, Canada.
¹⁶ Department of Medicine, Technion, Haifa, Israel.
¹⁷ Department of Pediatric Critical Care, Rambam Medical Center, Haifa, Israel.

Abstract

Background and objectives: Machine Learning offers opportunities to improve patient outcomes, team performance, and reduce healthcare costs. Yet only a small fraction of all Machine Learning models for health care have been successfully integrated into the clinical space. There are no current guidelines for clinical model integration, leading to waste, unnecessary costs, patient harm, and decreases in efficiency when improperly implemented. Systems engineering is widely used in industry to achieve an integrated system of systems through an interprofessional collaborative approach to system design, development, and integration. We propose a framework based on systems engineering to guide the development and integration of Machine Learning models in healthcare.

Methods: Applied systems engineering, software engineering and health care Machine Learning software development practices were reviewed and critically appraised to establish an understanding of limitations and challenges within these domains. Principles of systems engineering were used to develop solutions to address the identified problems. The framework was then harmonized with the Machine Learning software development process to create a systems engineering-based Machine Learning software development approach in the healthcare domain.

Results: We present an integration framework for healthcare Artificial Intelligence that considers the entirety of this system of systems. Our proposed framework utilizes a combined software and integration engineering approach and consists of four phases: (1) Inception, (2) Preparation, (3) Development, and (4) Integration. During each phase, we present specific elements for consideration in each of the three domains of integration: The Human, The Technical System, and The Environment. There are also elements that are considered in the interactions between these domains.

Conclusion: Clinical models are technical systems that need to be integrated into the existing system of systems in health care. A systems engineering approach to integration ensures appropriate elements are considered at each stage of model design to facilitate model integration. Our proposed framework is based on principles of systems engineering and can serve as a guide for model development, increasing the likelihood of successful Machine Learning translation and integration.

Keywords: Integration engineering; artificial intelligence; digital health; healthcare (MeSH); human factors engineering (HFE); machine learning; system of systems (SoS).