  A new trend in medicine is the use of algorithms to analyze big datasets,
e.g. using everything your phone measures about you for diagnostics or
monitoring. However, these algorithms are commonly compared against weak
baselines, which may contribute to excessive optimism. To assess how well an
algorithm works, scientists typically ask how well its output correlates with
medically assigned scores. Here we perform a meta-analysis to quantify how the
literature evaluates their algorithms for monitoring mental wellbeing. We find
that the bulk of the literature ($\sim$77%) uses meaningless comparisons that
ignore patient baseline state. For example, having an algorithm that uses phone
data to diagnose mood disorders would be useful. However, it is possible to
over 80% of the variance of some mood measures in the population by simply
guessing that each patient has their own average mood - the patient-specific
baseline. Thus, an algorithm that just predicts that our mood is like it
usually is can explain the majority of variance, but is, obviously, entirely
useless. Comparing to the wrong (population) baseline has a massive effect on
the perceived quality of algorithms and produces baseless optimism in the
field. To solve this problem we propose "user lift" that reduces these
systematic errors in the evaluation of personalized medical monitoring.
