Bridging the implementation gap of machine learning in healthcare

Bridging the implementation gap of machine learning in healthcare [Commentary]
BMJ Innovations 2020;6:45-47, 27 March 2020

• A recent systematic review of deep learning applications using electronic health records data highlighted the need to focus on the last mile of implementation: ‘for direct clinical impact, deployment and automation of deep learning models must be considered’. The typical life-cycle of an algorithm remains: train on historical data, publish a good receiver-operator curve and then collect dust in the ‘model graveyard’.
• User-experience design ought to be considered as a fundamental part of any health machine learning pipeline—the way to merge an algorithm into the ‘socio-technical’ milieu of the clinic. Safety Patient safety must also become a foundational part of model design. Moving forward, there will be much to learn from the rich field of implementation science, which has developed frameworks for the design of complex health service interventions.