Clinical deployment environments: Five pillars of translational machine learning for health - Harris, Steve; Bonnici , Tim; Keen, Thomas; Lilaonitkul, Watjana; White, Mark J.; Swanepoel, Nel (Frontiers in Digital Health, 2022)
Machine Learning for Health (ML4H) has demonstrated efficacy in computer imaging and similarly self-contained digital workflows, but has failed to substantially impact routine clinical care. Digital maturity is no longer a barrier where Electronic Health Record Systems (EHRS) are widely adopted. ML4H falls short because it needs an infrastructure for development, deployment and evaluation within the healthcare institution. In this paper, we propose a design pattern called a Clinical Deployment Environment (CDE). We sketch the five pillars of the CDE: (1) real world development supported by live data where ML4H teams can iteratively build and test at the bedside (2) an ML-Ops platform that brings the rigour and standards of continuous deployment to ML4H (3) design and supervision by those with expertise in AI safety (4) the methods of implementation science that enable the algorithmic insights to influence the behaviour of clinicians and patients and (5) continuous evaluation that uses randomisation to avoid bias but in an agile manner. Our CDE is derived from our experience of delivering machine learning models to the bedside in a large academic teaching hospital, and aims to answer for ML4H the same challenge that translational medicine brought to bear for drug discovery