Int J Med Inform - Application of data mining to the identification of critical factors in patient falls using a web-based reporting system.

Tópicos

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Resumo

RPOSE: The implementation of an information system has become a trend in healthcare institutions. How to identify variables related to patient safety among accumulated data has been viewed as a main issue. The purpose of this study was to identify critical factors related to patient falls through the application of data mining to available data through a hospital information system.METHOD: Data on a total of 725 patient falls were obtained from a web-based nursing incident reporting system at a medical center in Taiwan. In the process of data mining, feature selection was applied as the first step, after which 10 critical factors were selected to predict the dependent variables (injury versus non-injury). An artificial neural network (ANN) analysis was applied to develop a predictive model and a multivariate stepwise logistic regression was performed for comparison purposes.RESULTS: The ANN model produced the following results: a Receiver-Operating-Character (ROC) curve indicated 77% accuracy, the positive predictive value (PPV) was 68%, and the negative predictive value (NPV) was 72%; while the multivariate stepwise logistic regression only identified 3 variables (fall assessment, anti-psychosis medication and diuretics) as significant predictors with ROC curve of 42%, PPV of 26.24%, and NPV of 87.12%.CONCLUSION: In addition to medication use such as anti-psychotic and diuretics, nursing intervention where a fall assessment is conducted could represent a critical factor related to outcomes of fall incidence.

Resumo Limpo

rpose implement inform system becom trend healthcar institut identifi variabl relat patient safeti among accumul data view main issu purpos studi identifi critic factor relat patient fall applic data mine avail data hospit inform systemmethod data total patient fall obtain webbas nurs incid report system medic center taiwan process data mine featur select appli first step critic factor select predict depend variabl injuri versus noninjuri artifici neural network ann analysi appli develop predict model multivari stepwis logist regress perform comparison purposesresult ann model produc follow result receiveroperatingcharact roc curv indic accuraci posit predict valu ppv negat predict valu npv multivari stepwis logist regress identifi variabl fall assess antipsychosi medic diuret signific predictor roc curv ppv npv conclus addit medic use antipsychot diuret nurs intervent fall assess conduct repres critic factor relat outcom fall incid

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