Models for predicting admission of patients from an emergency department (ED) have been suggested as a device for reducing ED crowding; however, such models are not widely implemented. The objective of this study is to validate an existing ED admission model on data from an outside hospital, as a first step to identifying a generalizable means to develop such models. Previously, an ED admission model was developed on retrospective observational data at a large urban hospital in Singapore. Data from the electronic health record (EHR) of a tertiary medical center in Durham, North Carolina (NC ED) were curated and transformed to align closely with the features of the Singapore model. A stepwise logistic regression model was built on these features to predict the probability of inpatient admission for patients presenting to the NC ED at the time of triage. A randomly selected 30% subset of the data was withheld from training and used to validate the model. Performance metrics were compared to those attained by the Singapore team. A total of 60, 064 encounters from January to December 2019 were included in the analysis; these patients had an admission rate of 28.2%. Variables used for modeling were first identified by the Singapore team and included age, sex, day of week, Emergency Severity Index (ESI), race, shift time, mode of arrival, time of year, postal code, fever status, and number of prior ED visits. The model we trained on the NC ED EHR data, using the features of the Singapore team’s model, had an area under the ROC curve (AUROC) of 0.821 (95% CI, 0.804-0.837), comparable to the AUROC of 0.825 (95% CI, 0.824-0.827) obtained by the Singapore team’s on their data. The most significant features associated with discharge included an ESI Score above 3 and ED arrival by law enforcement; the most significant features associated with admission included ESI Score below 3, fever status, and advanced age. We have been able to map elements from the NC ED EHR to the Singapore triage model elements to develop a model that performs comparably. Thus, a general method for building simple, effective ED admission models at various independent hospitals is attainable and scalable.