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Basic Treatment Equivalent (BTE): a new measure of linear accelerator workload
G P Delaney1, V Gebski, A D Lunn
1Westmead Hospital, NSW, Australia.
Summary
A new model, Basic Treatment Equivalent (BTE), accurately measures radiation oncology linear accelerator workload by accounting for treatment complexity, unlike previous methods. This offers a realistic basis for cost assessment and patient throughput comparisons.
Area of Science:
- Radiation Oncology
- Medical Physics
- Health Services Research
Background:
- Current linear accelerator workload measurement in radiation oncology relies on treatment fields per unit time, neglecting complexity.
- This method provides an inaccurate basis for cost assessment and patient throughput comparisons between departments.
Purpose of the Study:
- To develop a simple, reproducible model for measuring linear accelerator workload in Australasian radiation oncology departments.
- To account for variations in treatment complexity, providing a realistic basis for cost assessment and patient throughput comparison.
Main Methods:
- Prospective study measuring the duration of 2371 radiotherapy fractions over 4 weeks.
- Multivariate modeling identified factors influencing fraction duration, including treatment complexity variables.
- Derived and validated the 'Basic Treatment Equivalent' (BTE) model at Westmead Hospital.
Main Results:
- Fraction duration was significantly influenced by the number of fields, shielding blocks, first treatment fraction, need for anesthesia, and performance status.
- The derived BTE model accurately reflected predicted resource utilization during a subsequent 1-week study.
- The BTE model demonstrated a more accurate reflection of linear accelerator workload compared to previous measures.
Conclusions:
- The developed BTE model offers a more realistic assessment of linear accelerator workload by incorporating treatment complexity.
- Further prospective testing and Australasian validation are required before widespread adoption.
- The BTE model has significant implications for workload reporting, cost analysis, and patient scheduling in radiation oncology.