Summary
Use of the hospital information system for early prediction of hospital admissions among patients in the Emergency Department of a Tertiary Referral Center
Guillermo José Ortega Rabbione1-3, Guillermo Fernández Jiménez4, Inés Aguilar Foguet5, Manuel Junquera Crespo6, José Julián Díaz Melguizo7, Carmen del Arco Galán6
Affiliation of the authors
1Unidad de Análisis de Datos. Instituto de Investigación Sanitaria del Hospital Universitario de La Princesa, Madrid, Spain. 2Consejo Nacional de Investigaciones Científicas y Técnicas, CONICET, Argentina. 3Departamento de Ciencia y Tecnología, Universidad Nacional de Quilmes, Argentina. 4Unidad de Información Clínico-Asistencial, Hospital Universitario de La Princesa, Madrid, Spain. 5Universidad Alfonso X El Sabio, Madrid, Spain. 6Servicio de Urgencias, Hospital Universitario de La Princesa, Madrid, Spain. 7Gerencia, Hospital Universitario de La Princesa, Madrid, Spain.
DOI
Quote
Ortega Rabbione GJ, Fernández Jiménez G, Aguilar Foguet I, Junquera Crespo M, Díaz Melguizo JJ, del Arco Galán C. Use of the hospital information system for early prediction of hospital admissions among patients in the Emergency Department of a Tertiary Referral Center. Rev Esp Urg Emerg. 2026;5:194-200
Summary
OBJECTIVE. To develop and evaluate the predictive capacity of a risk scale for hospital admission from the emergency department, using variables from the hospital clinical information system (HCIS) and the Minimum Basic Data Set (MBDS) in a tertiary referral center.
METHODS. We conducted a retrospective observational cohort study in a tertiary referral center, including all patients treated in the emergency department between January and December 2022. A total of 214 clinical and administrative variables were collected from 96,284 patients, who were divided into a training set (75 %) and a test set (25 %). After an initial selection using random forests, a logistic regression model with categorized variables was implemented, generating an interpretable risk scale. Performance was assessed using the ROC curve and calibration curve.
RESULTS. The final model included a total of 64 variables with 117 regressors. The scale showed high discriminative capacity (AUC = 0.913) and a low Brier score (0.057). Calibration was globally adequate, although risk overestimation was observed in the high range (0.8-0.95). Three risk regions, low, intermediate, and high, were defined with cutoff points [28;72] on the scale, associated with admission probabilities < 0.1 and > 0.9, respectively.
CONCLUSIONS. The proposed risk scale offers an interpretable, accurate, and applicable tool for the early prediction of hospital admissions from the emergency department. Although it still requires external validation, its implementation in any clinical information system, such as HCIS, HC3, Selene, etc., is straightforward because it uses variables integrated into these systems.