Participants rarely drop out without warning. Their data often carries signals beforehand: a run of missed appointments, a drop in how often they use a study app or return a diary, a change in how far they travel to visits, or a pattern of side effects reported in check-ins. Individually these signals are weak, but together they form a profile. AI can read these profiles across the whole group and rank participants by how likely they are to drop out, so staff can see where attention is most needed. The ranking is not a prediction of any single person's future. It is a way to sort a long list of participants into those who look stable and those who look at risk, so limited staff time goes to the people most likely to need it. This is the same pattern-finding idea from earlier in the course, applied to retention instead of recruitment: the value is in narrowing attention, not in being right about every individual.
Learn MotionHow AI Makes Clinical Trials Faster: A High-Level Overview
Keeping Participants in the Trial
Spotting Who Is Likely to Leave
2 / 4
Look at the ranked list. Each participant has a small set of signals next to them: missed appointments, how often they use the study app, how far they travel, and what they report in check-ins. No single signal decides anything. A person who missed one visit might be perfectly fine. But when several weak signals line up, the score rises, and that person moves toward the top of the list. The point of the ranking is not to predict exactly who will leave. It is to sort a long list so that staff, who cannot call everyone every week, spend their time on the people who look most at risk. Notice that the score is a sorting tool, not a verdict.
0:00 / 0:00