Predicting return visits to the emergency department for pediatric patients: Applying supervised learning techniques to the Taiwan National Health Insurance Research Database
| Autor: | Ya Han Hu, Chun Tien Tai, Sheng-Feng Sung, Hai Wei Lee, Solomon Chih-Cheng Chen |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
medicine.medical_specialty
National Health Programs Quality Assurance Health Care Psychological intervention Decision tree Taiwan Health Informatics Logistic regression Patient Readmission Pediatrics 03 medical and health sciences Patient safety 0302 clinical medicine 030225 pediatrics Medicine Data Mining Humans Medical history Child medicine.diagnostic_test business.industry Supervised learning Decision Trees Complete blood count 030208 emergency & critical care medicine Bayes Theorem Emergency department Computer Science Applications Emergency medicine Supervised Machine Learning business Emergency Service Hospital Software Forecasting |
| Zdroj: | Computer methods and programs in biomedicine. 144 |
| ISSN: | 1872-7565 |
| Popis: | 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 Naive 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. |
| Databáze: | OpenAIRE |
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