Objectives: The Pediatric Logistic Organ Dysfunction-2 is a validated score that quantifies organ dysfunction severity and requires complex data collection that is time-consuming and subject to errors. We hypothesized that a computer algorithm that automatically collects and calculates the Pediatric Logistic Organ Dysfunction-2 (aPELOD-2) score would be valid, fast and at least as accurate as a manual approach (mPELOD-2). Design: Retrospective cohort study. Setting: Single center tertiary medical and surgical pediatric critical care unit (Sainte-Justine Hospital, Montreal, Canada). Patients: Critically ill children participating in four clinical studies between January 2013 and August 2018, a period during which mPELOD-2 data were manually collected. Interventions: None. Measurements and Main Results: The aPELOD-2 was calculated for all consecutive admissions between 2013 and 2018 (n = 5,279) and had a good survival discrimination with an area under the receiver operating characteristic curve of 0.84 (95% CI, 0.81–0.88). We also collected data from four single-center studies in which mPELOD-2 was calculated (n = 796, 57% medical, 43% surgical) and compared these measurements to those of the aPELOD-2. For those patients, median age was 15 months (interquartile range, 3–73 mo), median ICU stay was 5 days (interquartile range, 3–9 d), mortality was 3.9% (n = 28). The intraclass correlation coefficient between mPELOD-2 and aPELOD-2 was 0.75 (95% CI, 0.73–0.77). The Bland-Altman showed a bias of 1.9 (95% CI, 1.7–2) and limits of agreement of –3.1 (95% CI, –3.4 to –2.8) to 6.8 (95% CI, 6.5–7.2). The highest agreement (Cohen’s Kappa) of the Pediatric Logistic Organ Dysfunction-2 components was noted for lactate level (0.88), invasive ventilation (0.86), and creatinine level (0.82) and the lowest for the Glasgow Coma Scale (0.52). The proportion of patients with multiple organ dysfunction syndrome was higher for aPELOD-2 (78%) than mPELOD-2 (72%; p = 0.002). The aPELOD-2 had a better survival discrimination (area under the receiver operating characteristic curve, 0.81; 95% CI, 0.72–0.90) over mPELOD-2 (area under the receiver operating characteristic curve, 0.70; 95% CI, 0.59–0.82; p = 0.01). Conclusions: We successfully created a freely available automatic algorithm to calculate the Pediatric Logistic Organ Dysfunction-2 score that is less labor intensive and has better survival discrimination than the manual calculation. Use of an automated system could greatly facilitate integration of the Pediatric Logistic Organ Dysfunction-2 score at the bedside and within clinical decision support systems.