Nurses spend on average one third of their time on direct patient care, and most on indirect care in hospital settings. However, nursing workload is challenging to measure in a systemized, accurate, and timely way. Patient classification systems are designed to capture information about nursing workload based on the illness severity of the patient (acuity) to determine staffing resources and balance nursing workload. These systems are instruments that group nursing care activities in different categories to determine patient acuity. Electronic health records provide opportunities to use available data sets to accurately, and in real-time, document many direct and indirect patient care activities that can be used to generate an acuity score. Several fully integrated systems are available, of which some have demonstrated a decrease in resource utilization and staffing cost. Many studies found a relationship between nursing workload and patient outcomes, but few have used a PCS to predict medication errors. The purpose of this dissertation was to explore the relationship between nurse workload measurements: productivity, acuity level and nursing degree levels and nurse medication errors. It was hypothesized that understaffing resulted in increased nursing medication errors. The study site provided an ideal situation to conduct the research as the same proprietary patient classification system had been deployed at four hospitals. The Patient Care Delivery Model informed the variables for the input, throughput and output concepts of the model. The predictor variable was productivity level (nurse staffing) and the outcome variable was nursing medication errors. Covariates were proportion of nurses with baccalaureate degree and higher, and patient acuity derived from the patient classification system. Findings from generalized estimating equations analyses revealed that productivity was not a predictor of nursing medication errors. Nevertheless, productivity may be clinically significant. A higher proportion of BSN and higher degrees was associated with the odds of less medication errors. Medication errors may have been underreported, which may have influenced the findings. A review of the literature was conducted to examine research related to the research and to identify gaps. This review is presented in the final dissertation manuscript. The knowledge gained from this research indicates that although findings were non-significant, patient classification systems should be explored further to measure the effect of productivity on nursing sensitive indicators.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1423 |
| Date | 01 January 2020 |
| Creators | Waterval, Egidius Maria Eugene |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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