Precision medicine incorporates patient‐level covariates to tailor treatment decisions, seeking to improve outcomes. In longitudinal studies with time‐varying covariates and sequential treatment decisions, precision medicine can be formalized with dynamic treatment regimes (DTRs): sequences of covariate‐dependent treatment rules. To date, the precision medicine literature has not addressed a ubiquitous concern in health research—measurement error—where observed data deviate from the truth. We discuss the consequences of ignoring measurement error in the context of DTRs, focusing on challenges unique to precision medicine. We show—through simulation and theoretical results—that relatively simple measurement error correction techniques can lead to substantial improvements over uncorrected analyses, and apply these findings to the sequenced treatment alternatives to relieve depression study.