Developing a modelling approach to quantify quality of care and nurse workload — Field validation study

Abstract

Background:

The effect of policy and managerial decisions on nurse-workload, and subsequent quality-of-care, are difficult to quantify in advance. A tool is needed that can proactively test these changes — Discrete Event Simulation (DES) may help. While computerized simulation models have existed before, there remains a gap to affirm the validity of these models.

Objective:

Develop an approach to creating a valid computerized simulation model that quantifies the effects of operational decisions on nurse-workload and quality-of-care.

Methods:

The DES model simulates the process of care delivery for nurses on a task-by-task basis. In an effort to validate this approach, the DES model was adapted to a real-world medical-surgical unit. Model inputs include: historical patient-care data; unit-layout; and programming logic, developed via focus-groups. Nurse-workload outcomes were distance-walked, task-in-queue, direct-care time, and nurse-movement. Quality-of-care outcomes included missed-care; and care-task waiting-time. The model is validated via internal validity checks and a field study that consisted of a ‘step-counter study’, a ‘MISSCARE survey’, ‘nurse job shadowing’, and a ‘time and motion study’. An Intraclass-correlation (ICC) and Spearman ranked correlation analysis were used to compare modelling outcomes to field-study outcomes.

Results:

The DES model, when adapted to a real-world medical-surgical unit, has been validated. The ICC coefficients show an “excellent” agreement of 0.99, 0.99, 0.85, 0.85, 0.84 between simulation and real-world outcomes, along with a “good” agreement of 0.86 for Spearman ranked correlation. Specific modelling results include a ‘distance walked’ of 7 to 10.6 km with a ‘direct care time’ of 8.3 to 10.4 h with a total of 77 to 84 trips for an average of 12 to 15 ‘tasks in queue’. Quality-of-care was represented by a ‘care task waiting time’ of 0.9 to 1 h that lead to 25 to 31 ‘missed-care’ tasks, where, 27% were ‘non-patient care’; and ‘missed-care delivery time’ was 2 to 2.9 h.

Conclusion:

This research provides a decision support-system that can help test and inform healthcare system policies that support both care quality and safety. By validating the DES model of a medical-surgical unit, we suggest that the modelling approach will also yield valid result when applied in similar settings. However, the modelling approach needs to be adapted to other healthcare settings and tested before concluding that this approach will consistently yield valid models.