J Med Syst - Utilization of electronic medical records to build a detection model for surveillance of healthcare-associated urinary tract infections.


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In this study, we propose an approach to build a detection model for surveillance of healthcare-associated urinary tract infection (HA-UTI) based on the variables extracted from the electronic medical records (EMRs) in a 730-bed, tertiary-care teaching hospital in Taiwan. Firstly we mapped the CDC's HA-UTI case definitions to a set of variables, and identified the variables whose values could be derived from the EMRs of the hospital automatically. Then with these variables we performed discriminant analysis (DA) on a training set of the EMRs to construct a discriminant function (DF) for the classification of a patient with or without HA-UTI. Finally, we evaluated the sensitivity, specificity, and overall accuracy of the function using a testing set of EMRs. In this study, six surveillance variables (fever, urine culture, blood culture, routine urinalysis, antibiotic use, and invasive devices) were identified whose values could be derived from the EMRs of the hospital. The sensitivity, specificity and overall accuracy of the built DF were 100 %, 94.61 %, and 94.65 %, respectively. Since most hospitals may adopt their EMRs piece-by-piece to meet their functional requirements, the variables that are available in the EMRs may differ. Our approach can build a detection model with these variables to achieve a high sensitivity, specificity and accuracy for automatically detecting suspected HA-UTI cases. Therefore, our approach on one hand can reduce the efforts in building the model; on the other hand, can facilitate adoption of EMRs for HAI surveillance and control.

Resumo Limpo

studi propos approach build detect model surveil healthcareassoci urinari tract infect hauti base variabl extract electron medic record emr bed tertiarycar teach hospit taiwan first map cdcs hauti case definit set variabl identifi variabl whose valu deriv emr hospit automat variabl perform discrimin analysi da train set emr construct discrimin function df classif patient without hauti final evalu sensit specif overal accuraci function use test set emr studi six surveil variabl fever urin cultur blood cultur routin urinalysi antibiot use invas devic identifi whose valu deriv emr hospit sensit specif overal accuraci built df respect sinc hospit may adopt emr piecebypiec meet function requir variabl avail emr may differ approach can build detect model variabl achiev high sensit specif accuraci automat detect suspect hauti case therefor approach one hand can reduc effort build model hand can facilit adopt emr hai surveil control

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