Quantitative Insights From Nursing Workload Measurement: An Artificial Intelligence–Driven Approach

Victoria L Tiase, Katherine A Sward, Jianrong Li, Julio C Facelli

Open source

DOI: 10.1093/milmed/usag168
Published: 2026-07
Container: Military Medicine
Publisher: Oxford University Press (OUP)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1093/milmed/usag168,
  title = {Quantitative Insights From Nursing Workload Measurement: An Artificial Intelligence–Driven Approach},
  author = {Victoria L Tiase and Katherine A Sward and Jianrong Li and Julio C Facelli},
  year = {2026},
  journal = {Military Medicine},
  doi = {10.1093/milmed/usag168},
  url = {https://doi.org/10.1093/milmed/usag168}
}

RIS

TY  - JOUR
TI  - Quantitative Insights From Nursing Workload Measurement: An Artificial Intelligence–Driven Approach
AU  - Victoria L Tiase
AU  - Katherine A Sward
AU  - Jianrong Li
AU  - Julio C Facelli
PY  - 2026
JO  - Military Medicine
DO  - 10.1093/milmed/usag168
UR  - https://doi.org/10.1093/milmed/usag168
ER  -

APA

Tiase, V. L., Sward, K. A., Li, J., & Facelli, J. C. (2026). Quantitative Insights From Nursing Workload Measurement: An Artificial Intelligence–Driven Approach. Military Medicine. https://doi.org/10.1093/milmed/usag168

Source records

crossref · retrieved 2026-09-25T12:47:29.779Z