Laboratory test-ordering patterns carry an early prognostic signal in electronic health records

Clinical care generates patient measurements and records physician decisions, workflows, and measurement practices. This study evaluated physician laboratory test-ordering behavior as an early prognostic signal in care. The primary analysis used 305,732 admissions from MIMIC-IV, a publicly available hospital electronic health records database. Laboratory and vital-sign value features were extracted from the first 48 hours, together with test-ordering pattern features capturing frequency, intensity, breadth, and temporal escalation. These features were used to train machine-learning models based on measured values, ordering patterns, and their combination. The ordering-pattern model, containing no measured laboratory or vital-sign values, predicted adverse outcomes with AUROC values approaching those of the laboratory and vital-sign values model, although measured values retained a small, statistically significant advantage. For mortality, AUROC was 0.905 versus 0.932; for sepsis, 0.912 versus 0.924. Ordering patterns ranked among the most informative data streams, identified a high-risk "deceptively normal" phenotype, provided information beyond severity scores, and remained informative across subgroup, dose-response, and sensitivity analyses. Within four hours of admission, when many laboratory results remained unavailable, test-ordering patterns approached value-based predictive performance. External validation in the eICU-CRD and MC-MED datasets suggested generalizability despite domain shift. Laboratory test-ordering patterns may support earlier risk stratification if prospectively validated.