Nurse Rostering, Staffing & Patient Acuity Data
Model workforce capacity and roster pressure without exposing employee or patient information.
What you can build
- Nurse workforce and capacity dashboards
- Roster-gap, overtime and agency-use analysis
- Acuity-to-staffing scenario modelling
- Understaffing classification experiments
- Workforce-software and data-pipeline QA
Designed analytical signals
The generator models relationships between acuity, recommended ratios, sick leave, agency backfill, overtime and understaffing. Night and weekend shifts experience greater modelled pressure, while overtime and agency use rise when staffing gaps occur.
Schema preview
| Field | Meaning |
|---|---|
| average_acuity_score | Modelled average patient acuity |
| recommended_nurse_patient_ratio | Target staffing ratio |
| actual_rn_count / actual_en_count | Actual shift staffing |
| agency_staff_used | Agency backfill indicator |
| overtime_hours | Shift overtime hours |
| understaffed_flag | Whether staffing fell below target |
Files supplied
Complete CSV, data dictionary, README, licence, methodology and limitations, summary statistics, SQL practice queries, Python analysis notebook, offline dashboard and case study in the supported package.
Responsible-use statement
Every employee, patient and facility is fictional. The dataset contains no personal information from real people and is not a validated workforce-planning tool. It is intended for research, education, analytics, AI/ML development and software testing.