Design of a Clinical Decision Support Model for Predicting Pneumonia Readmission

Readmission is considered as an indicator for evaluating the overall health care environment of a hospital. Existing models developed to predict readmissions for pneumonia lack discriminative ability. In this study, we aim to determine the risk factors to predict readmission and to design a clinical decision support system (CDSS) to predict if a patient will be readmitted for pneumonia within 30 days after discharge. The data of 17,222 patients who had been admitted within the period from January to December 2013 were used for analysis. The study cohort consisted of 520 index admissions for pneumonia in a general hospital situated at Tao-Yuan area of Taiwan. Variables including demographic information, treatment and clinical factors, and health care utilization factors were collected. The selected variables are then applied for CDSS design using the RBF-SVM. Of the 520 index admissions for pneumonia, 86 (16.2%) patients were readmitted within 30 day. Six variables, including age, gender, number of medication, length of admission, number of comorbidities, and total admission cost were observed to be significant (p<;0.05) in predicting readmission. The predictive model was constructed using the RBF-SVM with 6 significant variables and all 20 variables, respectively. By testing models with different combinations of SVM parameters, C and γ, the predictive accuracy for two different models achieved 83.85% and 82.24% respectively. The model can be effective in identifying pneumonia patients at high risk for readmission.