There are a number of clinical guidelines and studies about hospital-acquired catheter-associated urinary tract infections (CAUTIs), but the rate of CAUTI occurrence is still rising. Hospitals are focusing on preventing hospital-acquired CAUTI, as the Centers for Medicare and Medicaid Services (CMS) does not provide payment for hospital-acquired infections anymore. There is a need to explore additional factors associated with hospital-acquired CAUTI and develop a predictive model to detect patients at high risk. This study developed a predictive model for hospital-acquired CAUTIs using electronic health records (EHRs) and nurse staffing data from multiple data sources. Research using large amounts of data could provide additional knowledge about hospital-acquired CAUTI. The first aim of the study was to create a quality, de-identified dataset combining multiple data sources for machine learning tasks. To address the first aim of the study, three datasets were combined into a single dataset. After integrating the datasets, data were cleaned and prepared for analysis. The second aim of the study was to develop and evaluate predictive models to find the best predictive model for hospital-acquired CAUTI. For the second aim of the study, three predictive models were created using the following data mining method: decision trees (DT), logistic regression (LR), and support vector machine (SVM). The models were evaluated and DT model was determined as the best predictive model for hospital-acquired CAUTI. The findings from this study have presented factors associated with hospital-acquired CAUTI. The study results demonstrated that female gender, old adult (≥56), Charlson comorbidity index score ≥ 3, longer length of stay, glucose lab result > 200 mg/dl, present of rationale for continued use of catheter, higher percent of direct care RNs with associate’s degree in nursing, less total nursing hours per patient day, and lower percent of direct care RNs with specialty nursing certification was related to CAUTI occurrence. Implications for future research include the use of different analytic software to investigate detailed results for LR model, adding more factors associated with CAUTI in modeling, using a larger sample with more patients with CAUTI, and patient outcomes research using nursing-sensitive indicators. This study has important implications for nursing practice. According to the study results, nurse specialty certification, nurse’s education at the baccalaureate level or higher, and more nursing hours per patient day were associated with better patient outcomes. Therefore, considerable efforts are needed to promote possession of nurse specialty certification and higher level of nursing education, as well as enough supply of nursing workforce.
University of Minnesota Ph.D. dissertation. August 2016. Major: Nursing. Advisor: Bonnie Westra. 1 computer file (PDF); x, 186 pages.
Park, Jung In.
Developing a Predictive Model for Hospital-Acquired Catheter-Associated Urinary Tract Infections Using Electronic Health Records and Nurse Staffing Data.
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