Medical error, patient safety and nurses’ performance are some of the critical concerns within healthcare systems. Several factors contribute to nurses’ performance and patient safety including fatigue, sleepiness and work system parameters.
Furthermore, because of a shortage of nurses, working nurses are often experiencing high workloads. They often work in 12- hour shifts and/or consecutive night shifts without receiving enough sleep or recovery. Thus, they frequently are fatigued and suffer from sleep deprivation, which again is negatively associated with patient safety. Therefore, health care researchers and decision makers are interested in developing policies and tools that help decrease nurses’ errors and increase their performance.
Thus, there is a need for a promising approach to understanding nurse fatigue and its causes and consequences that is able to capture dynamic nature of the problem. This study aimed to address this need. In the first step, data were collected from a private hospital. Next, a data mining technique was applied to uncover the patterns and associations among contributing factors that affect performance and patient safety. Finally, a model was developed to measure nurses’ responses to different work system parameters and stressors.
|Advisor:||Steege, Linsey M|
|School:||University of Missouri - Columbia|
|Department:||Industrial and Manufacturing Systems Engineering|
|School Location:||United States -- Missouri|
|Source:||DAI-B 78/02(E), Dissertation Abstracts International|
|Subjects:||Statistics, Industrial engineering, Nursing, Health care management|
|Keywords:||Data mining, Fatigue, Human factors, Patient safety, Work system parameters|
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