Clinical considerations when applying machine learning to decision-support tasks versus automation

Abstract

The future role of clinical automation in healthcare is a matter of debate, from commenters who claim that artificially intelligent clinical entities could relatively easily replace 80% of what physicians do1 to those who see a future of a “well-informed, empathetic clinician armed with good predictive tools and unburdened from clerical drudgery”.2 While the extent to which clinicians will be able to be replaced by machines is a larger topic than will be covered here, what is clear is that artificial intelligence will transform the way healthcare is delivered.3 4 In this issue of BMJ Quality and Safety , for example, we see a report on a randomised controlled trial (RCT) of the use of a robot to capture historical information from older adults.5 Boumans et al randomised 42 community-dwelling seniors to have a 52-item questionnaire captured by a nurse or a social robot, allowing for the generation of three indices of frailty, well-being and resilience. In this small pilot, the robot completed the vast majority of interviews without assistance (92.8%) and the interview time and index scores were comparable, although it would be incorrect to suggest that the performance was interchangeable. The robot interviews showed much less variation in duration. Nurse interviews lasted an average of 15 min but with a wide SD of 8.5 min. The robot interviews lasted an average of 16.6 min (p=0.2 for comparison with nurse interviews) but with a SD of only 1.5 min. In other words, assigning these interviews to a robot would result in a much more predictable time commitment for patients. In their Discussion, Boumans and colleagues write that because “Many people are concerned about robots taking over human jobs…”, it is more palatable to introduce the robot as an assistant rather than as a replacement. Nonetheless, …