Health care professionals’ perceptions of machine learning based clinical decision support systems for oesophageal cancer management

Abstract

Oesophageal cancer (OC) causes significant morbidity and mortality. Multiple treatment regimens are available, and multidisciplinary team (MDT) decisions over which to offer are complex, multi-faceted and subject to logistical constraints and human factors. A machine learning (ML) model-based clinical decision support system (CDSS) for OC has been developed, trained on historical treatment decisions. However, clinician trust in such systems is not yet established. This study surveyed clinicians in OC MDTs in the UK and Ireland to investigate which clinical and sociodemographic factors influence conscious decision-making in OC, comparing their relative subjective importance to those derived from the ML model (reflecting previous real-world practice). It also sought to explore clinicians' views on the potential use of artificial intelligence-based CDSSs in OC. There was agreement between clinicians and the model in many of the most influential factors in decision making, although age and gender had greater influence on the model than their conscious importance to clinicians would support. Clinicians identified a wide range of additional clinical and holistic factors outside the current model which factor into their decision-making, including further investigations, symptoms, nutrition and social factors. The prospect of utilising an ML CDSS in future received generally positive feedback, although opinions varied widely. However, barriers to implementation were identified, including concerns around perceived clinician superiority over ML CDSSs, patient individuality, transparency and safeguarding, the need for evidence, and additional input requirements. As ML CDSSs are increasingly offered in practice, clinicians' reservations must be addressed and their need for transparency and evidence met.

Ähnliche Arbeiten