Predicting the future course of critical conditions involves personal experience, heuristics and statistical models. Although these methods may perform well for some cases and population averages, they suffer from substantial shortcomings when applied to individual patients. The reasons include methodological problems of statistical modeling as well as limitations of cross-sectional data sampling. Accurate predictions for individual patients become crucial when they have to guide irreversible decision-making. This notably applies to triage situations in response to a lack of healthcare resources. We will discuss these issues and argue that analysing longitudinal data obtained from time-limited trials in intensive care can provide a more robust approach to individual prognostication.