A Perspective on a Quality Management System for AI/ML-Based Clinical Decision Support in Hospital Care

Richard Bartels*, Jeroen Dudink, Saskia Haitjema, Daniel Oberski, Annemarie van ‘t Veen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Although many artificial intelligence (AI) and machine learning (ML) based algorithms are being developed by researchers, only a small fraction has been implemented in clinical-decision support (CDS) systems for clinical care. Healthcare organizations experience significant barriers implementing AI/ML models for diagnostic, prognostic, and monitoring purposes. In this perspective, we delve into the numerous and diverse quality control measures and responsibilities that emerge when moving from AI/ML-model development in a research environment to deployment in clinical care. The Sleep-Well Baby project, a ML-based monitoring system, currently being tested at the neonatal intensive care unit of the University Medical Center Utrecht, serves as a use-case illustrating our personal learning journey in this field. We argue that, in addition to quality assurance measures taken by the manufacturer, user responsibilities should be embedded in a quality management system (QMS) that is focused on life-cycle management of AI/ML-CDS models in a medical routine care environment. Furthermore, we highlight the strong similarities between AI/ML-CDS models and in vitro diagnostic devices and propose to use ISO15189, the quality guideline for medical laboratories, as inspiration when building a QMS for AI/ML-CDS usage in the clinic. We finally envision a future in which healthcare institutions run or have access to a medical AI-lab that provides the necessary expertise and quality assurance for AI/ML-CDS implementation and applies a QMS that mimics the ISO15189 used in medical laboratories.

Original languageEnglish
Article number942588
Number of pages8
JournalFrontiers in Digital Health
Volume4
DOIs
Publication statusPublished - 6 Jul 2022

Keywords

  • AI
  • clinical decision support
  • implementation
  • ISO15189
  • machine learning (ML)
  • quality management system

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