Breast cancer treatment pathway improvement using time‐driven activity‐based costing
Bibliographic record
Abstract
Time-driven activity-based costing (TDABC) is increasingly used to establish more accurate and time-dependent costs for complex health care pathways. We propose to extend this approach to detect the specific improvements (eg, lean methods) that can be introduced into a care process. We analyzed a care trajectory in radiation oncology for breast cancer patients at major Canadian urban hospital. This approach allowed us to identify the activities and resource groups related to the execution of each activity, and to estimate the execution time for each. Based on the model, we were able to extract financial data with which we could evaluate process costs. The total cost of the care trajectory was $2383.82 for 2015 to 2016. Out of a total of 1389 trajectories, only 268 were completed. The implementation of TDABC gives users a clearer idea of costs and encourages managers to understand how they break down over the course of a care trajectory. Once these costs are understood, decisions can be made regarding resource allocation and waste elimination, enabling lean methods to be implemented. The result is better reorganization of work by allocating resources differently, optimizing the care trajectory, and thereby reducing its costs.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".