Intensive workload for nurses due to high demands directly impacts the quality of care and nurses’ health. To better manage workload, it is necessary to understand the drivers of workload. This multidisciplinary research provides an adaptable nurse-focused approach to discrete event simulation (DES) modelling that can quantify the effects of changing technical design and operational policies in terms of nurse workload and quality of care.
In the first phase of this research, a demonstrator model was developed that explored the impact of nurse-patient ratios. As the number of patients per nurse (nurse-patient ratio) increased, nurse workload increased, and the quality of care deteriorated. In the second phase of this research, the DES model tested the interaction of patient acuity and nurse-patient ratios. As the levels of patient acuity and number of patients per nurse increased, nurse workload increased, and quality of care deteriorated – a result that was not surprising but an ability to quantify this proactively, was conceived. In the third phase of this research, the DES model was validated by means of an external field validation study by adapting the model to a real-world unit. The DES model showed excellent consistency between modelling and real-world outcomes (Intraclass iv Correlation Coefficient = 0.85 to 0.99; Spearman Rank-order Correlation Coefficient = 0.78). The fourth phase of this research used the validated simulation model to test the design implication of geographical patient bed assignment. As nurses were assigned to patient beds further away from the center of the unit or spread further apart, nurse workload increased as the nurse had to walk more leading to a deterioration in the quality of care. The DES modelling capability showed that both aspects of assignment were important for patient bed assignment. The fifth phase of this research combined Digital Human Modelling (DHM) and DES to produce a time-trace of biomechanical load and peak biomechanical load (‘activity’) for a full shift of nursing work. As the nurse was assigned to beds further away from the center of the unit, the cumulative biomechanical load decreased as the nurse spent more time walking yielding a reduced biomechanical load in comparison to the task group ‘activity’. As patient acuity is increased, a decrease in L4/L5 moment is observed as the task duration and frequency of most care task increase. Due to increased care demands, nurses must now spend more time delivering care. Since the care demands are much higher than the current capability of nurses, quality of care is deteriorated. As number of patients per nurse, increased a ‘ceiling’ effect on biomechanical load can be observed as nurses do not have the time to attend to this extra demand for care. The use of this adaptable DES modeling approach can assist decision makers by providing quantifiable information on the potential impact of these decisions on nurse workload and quality of care. Thereby, assisting decision makers to create technical design and operational policies for hospital units that do not compromise patient safety and health of nurses.
Keywords: Behavioural operations research; Discrete Event Simulation; Nurse Workload; Quality of care; Healthcare Ergonomics; Human Factors Engineering; Nurses; Healthcare policy
Discrete event simulation for problem solving in the context of an emergency department