Volume 94, June 2026, 104354Author links open overlay panelKoen Van den Heede, Nicolas Bouckaert, Carine Van de VoordeShow moreAccess through your organization

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Readmissions are clinically detrimental and financially burdensome, and they are profoundly disruptive for patients and their families in terms of experience. They also strain scarce ICU bed capacity, a particularly pressing issue in the UK, where ICU bed availability per capita is among the lowest in Europe [6]. Investing in adequate NHPPD is therefore not only a matter of patient safety but also of system efficiency. But what is adequate staffing?

The next step: Adjust staffing metrics for nursing workload

NHPPD is a useful measure, but even after adjusting for patient acuity through the ICNARC model, it does not capture the true nursing workload. Workload differs from shift-to-shift (highest workload during morning shifts) and over the course of an ICU stay (peaking at admission) [7]. Risk-adjustment scores such as ICNARC are essential for correcting outcome measures for case-mix differences; however, they do not adjust nurse staffing metrics for variations in workload. Beyond severity of

Context matters: Highly regulated vs. more liberal organized systems

NHS England operates under a highly regulated system with levels of care (Level 2/3) and strict staffing standards, including 1:1 nursing ratio for the most complex patients [9]. This, combined with limited ICU capacity, means that admissions are largely restricted to the most critically ill during the most critical days of their hospital stays. Belgium, by contrast, has greater ICU capacity per capita and lacks formal stratification into levels [9].

In Belgium’s less regulated system, nursing

Conclusion

The study by Nwanosike at al. (2026) [4] represents a significant advance: it links nursing hours to a clinically relevant quality indicator (ICU readmission) using rigorous and ICU specific risk adjustment. It reinforces the message that investing in ICU nursing capacity pays off, clinically and economically. The next step is to refine this investment by systematically weighting staffing metrics for nursing workload.

CRediT authorship contribution statement

Koen Van den Heede: Conceptualization, Methodology, Writing – original draft. Nicolas Bouckaert: Formal analysis, Methodology, Writing – review & editing. Carine Van de Voorde: Conceptualization, Validation, Writing – review & editing.

Funding

This editorial was based on a study that was funded by the Belgian Health Care Knowledge Centre (KCE). The KCE is a federal institution which is financed by the National Institute for Health and Disability Insurance (NIHDI, RIZIV/INAMI), the Federal Public Service of Health, Food Chain Safety and Environment, and the Federal Public Service of Social Security. The development of health services research studies is part of the legal mission of the KCE. Although the development of the studies is

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

We would like to thank Diego-Castanares Zapatero, Mélanie Lefèvre, Jens Detollenaere.

Declaration of generative AI and AI-assisted technologies in the manuscript preparation process

During the preparation of this work the author(s) used Co-pilot in order to improve the readability of the text. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

Ethics approval

Not applicable (Editorial, including a figure with analysis of administrative data).

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