Patient and work flow and costs associated with staff time and facility usage at a comprehensive cancer centre in Quebec, Canada – a time and motion study
Bibliographic record
Abstract
BACKGROUND: Mapping patient and work flow and cost analysis studies can help determine the most efficient and cost effective way of providing health services while still maintaining the best standards of care. This study used both time and motion methodology and hospital data to assess the contribution of staff time and facility usage to the overall cost of cancer care during patient visits to a comprehensive cancer centre in Quebec, using metastatic colorectal cancer as a model. METHODS: A workflow diagram was created mapping direct and indirect steps involved during a patient's physician or treatment (FOLFOX/bevacizumab or XELOX/bevacizumab) visit. Staff were timed as they performed each task and this data together with compensation amounts were used to calculate personnel costs. Mean work times and 95% confidence intervals (CI) were calculated. Operation and maintenance (O&M) costs for the Centre were calculated using information from hospital databases. All costs were presented in constant Canadian dollars for the 2010-2011 fiscal year period. RESULTS: For physician visits, direct and indirect personnel costs were $9.25 (95%CI:$7.00-$11.51) and O&M costs were $60.21, for a total of $69.46 (95%CI:$67.21-$71.72). For treatment visits, personnel and O&M costs were $71.91 (95%CI:$45.53-$98.29) and $62.00 respectively for a total of $133.91 (95%CI:$107.53-$160.29). When calculated for treatment alone, the total cost was $136.06 (95%CI:$109.16-$162.95) for FOLFOX/bevacizumab and $119.94 (95%CI:$96.89-$142.99) for XELOX/bevacizumab. The highest cumulative personnel costs were for the pharmacists and nurses ($38.87 and $34.82 respectively). Regarding patient flow, total time in between steps was 77.6 and 49.5 minutes for a physician or treatment visit respectively. CONCLUSIONS: This study from a health care provider's perspective, demonstrated that in the context of increasingly expensive therapies, costs associated with staff time and facility usage do not contribute greatly to the overall cost of treating cancer at this cancer centre. It also illustrated the need for improvements in patient and work flow to reduce wait times in the clinic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".