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Research article

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Few tools accurately assess PICU nursing workload.

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High PICU workload leads to adverse outcomes and burnout.

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The QuantI2S tool shows strong reliability and predictive validity in the PICU.

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The QuantI2S tool was validated and implemented in a PICU at CHU Sainte-Justine.

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The QuantI2S supports informed staffing and safer PICU care.

AbstractIntroduction

Nursing workload in pediatric intensive care units is complex and increasingly demanding. Effective staffing is essential for positive patient outcomes, as inadequate coverage correlates with higher mortality and readmission rates. Current staffing tools have limitations and fail to account for the unique challenges of pediatric care.

Objective

To develop and validate a workload prediction tool for pediatric intensive care nurses to enhance decision-making and resource allocation.

Methods

The QuantI2S tool was developed through literature review and expert consensus, then implemented in a 24-bed Pediatric Intensive Care Unit. Validation involved: 1) correlation with the current gold standard, 2) inter-rater reproducibility, and 3) predictive accuracy. The bedside nurse and clinical nurse specialist completed the QuantI2S two hours before shift end (prospective score), while an independent reviewer calculated a retrospective score from chart reviews. Agreement was assessed using Bland-Altman plots and Intra-Class Correlation (ICC).

Results

A total of 172 patient-observations involving 45 patients were analyzed (July–August 2016). Compared with the gold standard, QuantI2S showed excellent reliability (rs = 0.738, 95% CI [0.624–0.822], p = 0.001). ICC for prospective scores was strong (0.990, 95% CI [0.985–0.993]). Bland-Altman analysis revealed near-perfect agreement (mean difference −0.03). Prospective and retrospective scores also showed excellent concordance (ICC = 0.916, 95% CI [0.872–0.946]).

Discussion and conclusion

QuantI2S accurately predicts pediatric intensive care nursing workload, demonstrating excellent reliability and ease of use. By integrating nursing activities and child-specific factors, it provides a robust framework for optimizing staffing and improving patient care.

Keywords

Pediatric nursing workload

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Workforce planning

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Nursing administration research

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Intensive care unit

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Validation studies

AbbreviationsAHN,

Assistant Head Nurse

; CHU,

Centre Hospitalier Universitaire

; CNC,

Clinical Nurse Consultant

; ICC,

Intra-Class Correlation

; PICU,

Pediatric Intensive Care Unit

;

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© 2026 The Authors. Published by Elsevier Inc.