Estimating Nurse Workload Using a Predictive Model From Routine Hospital Data: Algorithm Development and Validation
Bibliographic record
Abstract
Background: Managing nurse staffing is complex due to fluctuating demand based on ward occupancy, patient acuity, and dependency. Monitoring staffing adequacy in real time has the potential to inform safe and efficient deployment of staff. Patient classification systems (PCSs) are being used for per shift workload measurement, but they add a frequent administrative task for ward nursing staff. Objective: The objective of this study is to explore whether an algorithm could estimate ward workload using existing routinely recorded data. Methods: Anonymized admission records and assessments from a PCS supporting the safer nursing care tool were used to determine nursing care demand in medical and surgical wards in a single UK hospital between February 2017 and February 2020. Records were linked by ward and time. The data were split into a training set (75%) and a test set (25%). We built a predictive model of ward workload (as measured by the PCS) using routinely recorded administrative data and admission National Early Warning Score. The outcome variable was ward workload derived from the patient classifications, measured as the number of whole-time equivalent (WTE) nursing staff per patient. Results: In a test set of 11,592 ward assessments from 42 wards with a mean WTE per patient of 1.64, the model's mean absolute error was 0.078, with a mean percentage error of 4.9%. A Bland-Altman plot of the differences between the predicted values and the assessment values showed 95% of them within 0.21 WTE per patient. Conclusions: Predictions of nursing workload from a relatively small number of routinely collected variables showed moderate accuracy for general wards in 1 English hospital. This demonstrates the potential for automating assessments of nurse staffing requirements from routine data, reducing time spent on this nonclinical overhead, and improving monitoring of real-time staffing pressures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".