Algorithm aversion to mobile clinical decision support among clinicians: a choice-based conjoint analysis

Abstract

ABSTRACT Artificial intelligence has pervaded clinical decision support systems driven by algorithms. In healthcare, algorithmic decision-making is said to outperform clinicians in their treatment choices and recommendations. Yet, relying on an algorithm outcome for clinical decisions comes with uncertainties resulting from the opacity, inconsistency and inaccuracy usurping the epistemic authority of clinicians and ultimately creating aversion. This study explores latent factors that constitute aversion to using algorithm-augmented mobile clinical decision support (mCDS) applications (apps) by clinicians at the point of care. Given that aversion encompasses a tendency to avoid potential risks, we modelled clinicians’ preferences using choice-based conjoint analysis with Hierarchical Bayes (HB) estimation methods to examine 362 clinicians’ aversion algorithm-augmented mCDS apps. In order of magnitude, the findings indicate that clinicians using mCDS apps are averse to algorithms undermining their epistemic authority with persistently inconsistent results at the point of care. In essence, clinicians will at the point-of-care resist algorithms that restrict them from modifying the result in line with their professional responsibilities. The study has several implications for both research and practice by helping improve potential drawbacks in clinical algorithms while promoting effective frameworks to enhance decision-making in medical practice.