AI and Automated Decision Making

Indra: AI is successful when answering a problem, rather than just doing something fun with a pile of data. For example, we've seen it working well in triage where we have a huge number of images that need reporting on and, if you're helping people know that something is normal and fine, versus quite urgently not normal, then you're solving a real problem and people are more likely to adopt it. Afzal: There's a great deal of opportunity from basic decision support tools directing people to order sets of investigations based upon the patient's chart, to patient care examples such as AI predicting patient readiness for discharge or likelihood of readmission, to higher level uses around planning a service for coming months and years (though these predictions have been disrupted by COVID-19). Two big challenges are the quality of data that feeds these tools in the first place, but also the required level of confidence in the nature of the algorithms among professionals, patients and society as a whole. Arvind: In primary care there are lots of opportunities for AI to assist.