Moving to secondary use of IT solutions: A provincial “near miss indicator” for chemotherapy.
Bibliographic record
Abstract
161 Background: In Ontario, 93% of outpatient IV chemotherapy is prescribed through Systemic Treatment Computerized Order Entry Systems (ST CPOE). To support this, Cancer Care Ontario (CCO) has developed guidelines, evaluation tools, a Community of Practice (COP) and quality indicators. Using data collected in ST CPOE systems a “Near Miss Indicator” is being developed. Methods: Clinical consensus indicated that intercepted order rate, a proxy for a near miss indicator, would further the provincial safety agenda. This is defined as the percent of course 1, day 1 intravenous (IV) chemotherapy drug orders that were changed post-first time prescribing a new regimen (excludes clinical trials). An extraction standard for dose adjustments was developed encompassing clinical and laboratory values and body surface area (BSA). Three months of data was collected from 3 centres using the same ST CPOE system. Results: A total of 6822 orders were reviewed for possible near misses; 1,446 (21.2%) were course 1 orders. For course 1, day 1 IV chemotherapy drug orders, 209 (3.2%) were changed. Most orders were changed several hours to days post prescribing and hence were not felt to represent normal clinical workflows. Physicians modified 65 and pharmacy modified 169 orders. “Other” was chosen as the reason for change in 95 (45.5%) of orders and despite multidisciplinary team review, these records were not interpretable. A near miss rate of 2.2% resulted (32/1,446). Clinical and laboratory values and BSA were equally responsible. A fourth category of “dose rounding for ease” by pharmacy was identified and felt to possibly represent poor regimen builds and pharmacy workload. Conclusions: A near miss indicator is under development via a process of identifying a need, exploration of data and socialization of the results through a COP. Initial results show a low near miss indicator rate of 2.2% which is felt to be clinically actionable. Future system modifications may facilitate capture of this indicator. The feasibility of extending the indicator to multiple vendor ST CPOEs is under study.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".