Burnout in the neonatal intensive care unit and its relation to healthcare-associated infections.

J Perinatol

Perinatal Epidemiology and Health Outcomes Research Unit, Division of Neonatology, Department of Pediatrics, Stanford University School of Medicine and Lucile Packard Children's Hospital, Palo Alto, CA, USA.

Published: March 2017

Objective: To examine burnout prevalence among California neonatal intensive care units (NICUs) and to test the relation between burnout and healthcare-associated infection (HAI) rates in very low birth weight (VLBW) neonates.

Study Design: Retrospective observational study of provider perceptions of burnout from 2073 nurse practitioners, physicians, registered nurses and respiratory therapists, using a validated four-item questionnaire based on the Maslach Burnout Inventory. The relation between burnout and HAI rates among VLBW (<1500 g) neonates from each NICU was evaluated using multi-level logistic regression analysis with patient-level factors as fixed effects.

Results: We found variable prevalence of burnout across the NICUs surveyed (mean 25.2±10.1%). Healthcare-associated infection rates were 8.3±5.1% during the study period. Highest burnout prevalence was found among nurses, nurse practitioners and respiratory therapists (non-physicians, 28±11% vs 17±19% physicians), day shift workers (30±3% vs 25±4% night shift) and workers with 5 or more years of service (29±2% vs 16±6% in fewer than 3 years group). Overall burnout rates showed no correlation with risk-adjusted rates of HAIs (r=-0.133). Item-level analysis showed positive association between HAIs and perceptions of working too hard (odds ratio 1.15, 95% confidence interval 1.04-1.28). Sensitivity analysis of high-volume NICUs suggested a moderate correlation between burnout prevalence and HAIs (r=0.34).

Conclusion: Burnout is most prevalent among non-physicians, daytime workers and experienced workers. Perceptions of working too hard associate with increased HAIs in this cohort of VLBW infants, but overall burnout prevalence is not predictive.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5334140PMC
http://dx.doi.org/10.1038/jp.2016.211DOI Listing

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