The use of IT to improve practice quality within a radiation oncology program.
Bibliographic record
Abstract
170 Background: Our centre has used ARIA as its oncology information system since 2005. Anecdotally, since 2008, dosimetrists have identified a high rate of replanned treatment plans. In 2010, a 2% replan rate was estimated however, it was felt that the results did not capture the true rework impact. A definition for replan was created to distinguish it from plan revisions and an attempt to standardize replan naming conventions was initiated. The replan workload was again raised as an issue in 2013 and an interdisciplinary review workgroup was formed with the objective to develop a process to capture workload more accurately. Methods: Rework was further categorized into replan, rework and revision. The nomenclature for the ARIA plan name data field was standardized to identify replans and plan revisions. In addition, rework task activities were created to identify the discipline initiating the rework. Embedding these changes into our process allowed for efficient and accurate workload data extraction. Results of monthly data analysis are shared with the dosimetrists and actionable items are sent to department heads for program engagement. Results: In March 2014, we achieved 100% compliance to the plan naming convention. Data showed 6% replan and 6.5% revision rates. Table 1 shows the rework numbers initiated by discipline. Conclusions: A paperless environment lends itself well to an organization’s ability to capture data. Our experience shows that if processes are designed to support and be supported by the information system, the input of data points can be embedded seamlessly and practically in the clinician’s workflow. This results in improved data capture where data becomes more meaningful, and can then be used for continuous quality improvement, operational decision-making and research projects, all of which can lead to practice improvements. [Table: see text]
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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 | 0.085 | 0.203 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".