Evaluation of Predictive Models of Inpatient Nursing Workloads
Heinrich E, Bleisinger B, Ganslandt T, Toddenroth D (2026)
Publication Type: Conference contribution
Publication year: 2026
Journal
Publisher: IOS Press BV
Book Volume: 338
Pages Range: 683-687
Conference Proceedings Title: Studies in Health Technology and Informatics
Event location: Athens
ISBN: 9781643686684
DOI: 10.3233/SHTI260932
Abstract
The efficient allocation of medical personnel to clinical care requires adapting to fluctuating requirements. Nursing workloads at hospital wards depend not just on patient numbers (occupancy), but are also associated with individual features that are increasingly available in Electronic Health Records (EHRs). These EHRs thus promise to support human resource management with automated predictions of patient-specific nursing needs. To evaluate EHR-based workload estimates, we trained Random Forest (RF) and Gradient Boosting (GB) models in an anonymous dataset of 27,178 inpatient cases and 53 attributes from an acute-care hospital. The calculated statistical metrics demonstrated that GB models outperformed RF models, which appeared to suffer from overfitting. Reducing input data to 25 relevant attributes had little effect on predictive performance. Future investigations might explore how well such methods would work in different settings, or study other predictive algorithms such as neural networks.
Authors with CRIS profile
Thomas Ganslandt
Professur für Medical Data Science
Dennis Toddenroth
Professur für Medical Data Science
How to cite
APA:
Heinrich, E., Bleisinger, B., Ganslandt, T., & Toddenroth, D. (2026). Evaluation of Predictive Models of Inpatient Nursing Workloads. In John Mantas, Arie Hasman, Parisis Gallos, Reinhold Haux, Konstantinos Karitis (Eds.), Studies in Health Technology and Informatics (pp. 683-687). Athens, GR: IOS Press BV.
MLA:
Heinrich, Elisabeth, et al. "Evaluation of Predictive Models of Inpatient Nursing Workloads." Proceedings of the 24th International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2026, Athens Ed. John Mantas, Arie Hasman, Parisis Gallos, Reinhold Haux, Konstantinos Karitis, IOS Press BV, 2026. 683-687.
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