Predicting return visits to the emergency department for pediatric patients: Applying supervised learning techniques to the Taiwan National Health Insurance Research Database. (June 2017)

• Record Type: Journal Article
• Authors: Hu, Ya-Han; Tai, Chun-Tien; Chen, Solomon Chih-Cheng; Lee, Hai-Wei; Sung, Sheng-Feng
• Is Part Of: Computer methods and programs in biomedicine. Volume 144(2017)
• Page Start: 105
• Page End: 112
• Publication Date: 2017-06
• Subjects: Data mining -- Emergency department -- NHIRD -- Pediatric emergency care -- Return visit; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.03.022
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Unexpected return visits (RVs) to the emergency department (ED) greatly consume medical resources and may represent a patient safety issue. Previous studies were mainly single-site studies in Western countries and might suffer from generalization problems. We identified several factors which are associated with RVs to the ED in children. Decision tree techniques outperformed other data mining techniques in predicting RVs to the ED. We developed a decision tree using the CART technique to help identify high risk children in the ED. Abstract: Background and objective: Return visits (RVs) to the emergency department (ED) consume medical resources and may represent a patient safety issue. The occurrence of unexpected RVs is considered a performance indicator for ED care quality. Because children are susceptible to medical errors and utilize considerable ED resources, knowing the factors that affect RVs in pediatric patients helps improve the quality of pediatric emergency care. Methods: We collected data on visits made by patients aged ≤18 years to EDs from the National Health Insurance Research Database. The outcome of interest was a RV within 3 days of the initial visit. Potential factors were categorized into demographics, medical history, features of ED visits, physician characteristics, hospital characteristics, and treatment-seeking behavior. A multivariate logistic regression was used to identify independent predictors of RVs. We compared the performance of various data mining techniques, including Naïve Bayes, classification and regression tree (CART), random forest, and logistic regression, in predicting RVs. Finally, we developed a decision tree to stratify the risk of RVs. Results: Of 125, 940 visits, 6, 282 (5.0%) were followed by a RV within 3 days. Predictors of RVs included younger age, higher acuity, intravenous fluid, more examination types, complete blood count, consultation, lower hospital level, hospitalization within one week before the initial visit, frequent ED visits in the past one year, and visits made in Spring or on Saturdays. Patients with allergic diseases and those underwent ultrasound examination were less likely to return. Decision tree models performed better in predicting RVs in terms of area under curve. The decision tree constructed using the CART technique showed that the number of ED visits in the past one year, diagnosis category, testing of complete blood count, and age were important discriminators of risk of RVs. Conclusions: We identified several factors which are associated with RVs to the ED in pediatric patients. The knowledge of these factors may help assess risk of RVs in the ED and guide physicians to reevaluate and provide interventions to children belonging to the high risk groups before ED discharge.