Predicting nursing workload in digestive wards based on machine learning: A prospective study
This study introduces a quantitative model designed to evaluate nursing workload throughout the duration of hospitalization. By employing the model, nursing managers can consider multiple factors that impact workload comprehensively, resulting in enhanced comprehension and interpretation of workload variations. Through the application of a random forest algorithm for workload prediction, nursing managers can anticipate and estimate workload in a proactive and precise manner, thereby facilitating more efficient human resource planning.
Authors
- Jia‐Rui Shi (ORCID: https://orcid.org/0000-0001-7505-1281)
- Guihua Xu (ORCID: https://orcid.org/0000-0001-5245-3253)
- Ye Wang (ORCID: https://orcid.org/0009-0007-3670-4308)
- Dan Luo (ORCID: https://orcid.org/0000-0002-8202-7420)
- Yamei Bai (ORCID: https://orcid.org/0000-0003-1550-516X)
- Haiyan Yin (ORCID: https://orcid.org/0000-0002-9680-4219)
- Xueqing Zhang
- Qiongqiong Zang (ORCID: https://orcid.org/0009-0000-7250-0379)
- Yulei Song (ORCID: https://orcid.org/0009-0007-8970-8812)
Institutions
- Nanjing University of Chinese Medicine (CN)
Publication Details
- Journal
- BMC Nursing
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1186/s12912-024-02570-z
- Citations
- 5
- Primary Topic
- Chronic Disease Management Strategies
- Type
- article
- Field-Weighted Citation Impact
- 1.93
Funders
- National Natural Science Foundation of China