Identification of Predictive Nursing Workload Factors: A Six Sigma Approach

Abstract

A balanced nursing workload is crucial for patient and staff safety. Although there are several nursing planning models, there is no generic methodology to identify critical workload factors and their relative impact on different healthcare environments. We propose Six Sigma (SS) as a generic methodology and its DMAIC (Define, Measure, Analyze, Improve, Control) framework to identify statistically proven factors that affect nursing workload (NW) in any healthcare environment. Additionally, using a regression model, we estimated their relative importance. For our case study, we found that the number of patients per ward, the number of times medication was administered per shift, the number of nurses and the type of shifts were significant factors in predicting nursing workload. Using their relative importance as input for the nursing planning process, we improved the nursing assignment process performance from 0.09 to 1.05, with an increase in the sigma level from −0.34 to 2.97. Also, we reached the 55% target for the percentage of NW, from a baseline of 50.3%. We also reached the percentage target of NW set by the management of 55%, from the baseline of 50.3%. This study shows that SS can be used effectively to estimate the importance of the main factors that affect nursing workload, providing a methodology to improve the nurse–patient assignment process.