Demonstrating the relationships of length of stay, cost and clinical outcomes in a simulated NICU.

OBJECTIVE: Health-care leaders place significant focus on reducing the average length of stay (ALOS). We examined the relationships among ALOS, cost and clinical outcomes using a neonatal intensive care unit (NICU) simulation model. STUDY DESIGN: A discrete-event NICU simulation model based on the Duke NICU was created. To identify the relationships among ALOS, cost and clinical outcomes, we replaced the standard probability distributions with composite distributions representing the best and worst outcomes published by the National Institutes of Health Neonatal Research Network. RESULT: Both average cost per patient and average cost per ⩽28 week patient were lower in the best NICU ($16,400 vs $19,700 and $56,800 vs $76,700, respectively), while LOS remained higher (27 vs 24 days). CONCLUSION: Our model demonstrates that reducing LOS does not uniformly reduce hospital resource utilization. These results suggest that health-care leaders should not simply rely on initiatives to reduce LOS without clear line-of-sight on clinical outcomes as well.

Neonatal intensive care unit handoffs: a pilot study on core elements and epidemiology of errors.

OBJECTIVE: To define the core data elements of a neonatal intensive care unit (NICU) handoff compare NICU residents' written and verbal handoff data with real-time, auto-populated data and identify the epidemiology of handoff errors. STUDY DESIGN: We defined nine core data elements for a NICU patient handoff. We then compared residents' written and verbal handoffs against real-time, auto-populated data for each core element. RESULT: A total of 101 NICU patient handoffs (31 unique patients) were analyzed. Per patient, residents made more written errors for infants in critical-care beds than for infants in step-down beds (2.33 vs 1.67, P=0.04). Replacing residents' written handoffs with the gold-standard, auto-populated data would have prevented 92% of written errors. CONCLUSION: NICU infants are subjected to many handoff errors. Sicker infants are at higher risk for error. Auto-population can reduce written handoff errors and allow residents more time for training and educational opportunities.