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Background and Objectives: Pediatric inpatients with communication impairment may experience inadequate pain and symptom management. Research regarding potential variation in care among patients with and without communication impairment is hampered because existing pediatric databases do not include information about patient communication ability per se, even though these data sets do contain information about diagnoses and medical interventions that are probably correlated with the probability of communication impairment. Our objective was to develop and evaluate a classification model to identify patients in a large administrative database likely to be communication impaired., Methods: Our sample included 236 hospitalized patients aged ≥12 months whose ability to communicate about pain had been assessed. We randomly split this sample into development (n = 118) and validation (n = 118) sets. A priori, we developed a set of specific diagnoses, technology dependencies, procedures, and medications recorded in the Pediatric Health Information System likely to be strongly associated with communication impairment. We used logistic regression modeling to calculate the probability of communication impairment for each patient in the development set, assessed the model performance, and evaluated the performance of the 11-variable model in the validation set., Results: In the validation sample, the classification model showed excellent classification accuracy (area under the receiver operating characteristic curve 0.92; sensitivity 82.6%; 95% confidence interval, 74%-100%; specificity 86.3%; 95% confidence interval, 80%-97%). For the complete sample, the predicted probability of communication impairment demonstrated excellent calibration with the observed communication impairment status., Conclusions: Hospitalized pediatric patients with communication impairment can be accurately identified in a large hospital administrative database., (Copyright © 2016 by the American Academy of Pediatrics.)