Nurse’s Achilles Heel: Using Big Data to Determine Workload Factors That Impact Near Misses

Abstract

Purpose To explore how big data can be used to identify the contribution or influence of six specific workload variables: patient count, medication count, task count call lights, patient sepsis score, and hours worked on the occurrence of a near miss (NM) by individual nurses. Design A correlational and cross‐section research design was used to collect over 82,000 useable data points of historical workload data from the three unique systems on a medical‐surgical unit in a midsized hospital in the southeast United States over a 60‐day period. Data were collected prior to the start of the Covid‐19 pandemic in the United States. Methods Combined data were analyzed using JMP Pro version 12. Mean responses from two groups were compared using a t‐test and those from more than two groups using analysis of variance. Logistic regression was used to determine the significance of impact each workload variable had on individual nurses’ ability to administer medications successfully as measured by occurrence of NMs. Findings The mean outcome of each of the six workload factors measured differed significantly (p < .0001) among nurses. The mean outcome for all workload factors except the hours worked was found to be significantly higher (p < .0001) for those who committed an NM compared to those who did not. At least one workload variable was observed to be significantly associated (p < .05) with the occurrence or nonoccurrence of NMs in 82.6% of the nurses in the study. Conclusions For the majority of the nurses in our study, the occurrence of an NM was significantly impacted by at least one workload variable. Because the specific variables that impact performance are different for each individual nurse, decreasing only one variable, such as patient load, will not adequately address the risk for NMs. Other variables not studied here, such as education and experience, might be associated with the occurrence of NMs. Clinical Relevance In the majority of nurses, different workload variables increase their risk for an NM, suggesting that interventions addressing medication errors should be implemented based on the individual’s risk profile.

Fatigue in nurses and medication administration errors: A scoping review

“…The shifts worked in this study were planned 12‐hour shifts, and the RNs were not working overtime. Additionally, Campbell et al (2021) found that the number of additional 2‐h time periods worked during a one‐week timeframe did not statistically predict the occurrence of a NMs ( p = 0.7001). A study of 252 RNs in South Korea found no significant statistical difference in MAE rates between those working 12‐h shifts and those working 8‐h shifts (Hong et al, 2021).…”

Influence of different protection levels of PPE on nurses’ physical fatigue during the COVID-19 pandemic

“…As a result, the issue of the increase in nurses' fatigue cannot be ignored [11,24,25]. An assessment of the previous studies on nurses' fatigue shows that they focused mainly on physiological and psychological factors such as work flow, and organizational and management structures [11,26,27]. During the COVID-19 pandemic, related studies have indicated that nurses' fatigue is increasing, but the cause for this increase has been attributed mainly to longer working hours, higher workload, and psychological stress [28,29].…”

Effects of workload on medication administration errors in nursing: an analysis based on system dynamics modeling

“…Keers et al 28 validated the effect of workloads on medical errors based on medical error case studies, but no categorical discussion of workloads and types of medical errors was conducted. In addition, Fagerstro ¨m et al, 21 Jin et al, 16 and Campbell et al 55 have also verified the effects of workload on different types of errors using experimental approaches or big data techniques, but the simulation of medical error systems is inadequate, and it is highly difficult to predict the probability of occurrence and error improvement effects of medical errors quantitatively. SD theory is also frequently applied in healthcare.…”