PT - JOURNAL ARTICLE AU - Holden, Richard J AU - Scanlon, Matthew C AU - Patel, Neal R AU - Kaushal, Rainu AU - Escoto, Kamisha Hamilton AU - Brown, Roger L AU - Alper, Samuel J AU - Arnold, Judi M AU - Shalaby, Theresa M AU - Murkowski, Kathleen AU - Karsh, Ben-Tzion TI - A human factors framework and study of the effect of nursing workload on patient safety and employee quality of working life AID - 10.1136/bmjqs.2008.028381 DP - 2011 Jan 01 TA - BMJ Quality & Safety PG - 15--24 VI - 20 IP - 1 4099 - http://qualitysafety.bmj.com/content/20/1/15.short 4100 - http://qualitysafety.bmj.com/content/20/1/15.full SO - BMJ Qual Saf2011 Jan 01; 20 AB - Background Nursing workload is increasingly thought to contribute to both nurses' quality of working life and quality/safety of care. Prior studies lack a coherent model for conceptualising and measuring the effects of workload in healthcare. In contrast, we conceptualised a human factors model for workload specifying workload at three distinct levels of analysis and having multiple nurse and patient outcomes.Methods To test this model, we analysed results from a cross-sectional survey of a volunteer sample of nurses in six units of two academic tertiary care paediatric hospitals.Results Workload measures were generally correlated with outcomes of interest. A multivariate structural model revealed that: the unit-level measure of staffing adequacy was significantly related to job dissatisfaction (path loading=0.31) and burnout (path loading=0.45); the task-level measure of mental workload related to interruptions, divided attention, and being rushed was associated with burnout (path loading=0.25) and medication error likelihood (path loading=1.04). Job-level workload was not uniquely and significantly associated with any outcomes.Discussion The human factors engineering model of nursing workload was supported by data from two paediatric hospitals. The findings provided a novel insight into specific ways that different types of workload could affect nurse and patient outcomes. These findings suggest further research and yield a number of human factors design suggestions.