Improving the Safety and Quality of Systemic Treatment Regimens in Computerized Prescriber Order Entry Systems
Bibliographic record
Abstract
PURPOSE: Systemic treatment (ST) computerized prescriber order entry (CPOE) and preprinted orders (PPO) are proven to reduce errors. There is no known guidance in oncology to facilitate high-quality, accurate regimen development and review; hence, this was identified as a system-wide gap. This provincial initiative aimed to improve the quality of oncology regimens through a comprehensive review of systemic treatment (ST) regimens and the development of standards. METHODS: A system-wide analysis of all active regimens (both CPOE and PPO) to ensure they were built as intended was conducted in 2015. Thirty-five hospitals (on behalf of 75 treatment facilities) were asked to report any unintentional discrepancies and details of the maintenance review process. Discrepancies were compiled, categorized, and analyzed for potential to cause harm. In addition, a multidisciplinary expert working group was formed to create best practice recommendations. RESULTS: The review yielded a 94% response rate and took a total of 18 months to complete (70% completed within 9 months). The average number of regimens reviewed was 336 (range, 15 to 700; n = 9). Unintentional discrepancies were reported by nine hospitals (27%). A total of 369 discrepancies were reported (average, 55 per hospital), and 28 were deemed to have a moderate potential for harm. Only two hospitals (6%) had an established maintenance process; now, all have standard processes for review. Consensus-based recommendations for ST-CPOE and PPO regimen development and maintenance were developed. CONCLUSION: The review identified unintentional discrepancies and, because of the potential for patient harm, corrective action has been taken. Identified discrepancies have been amended, and standard regimen development and maintenance review processes are now implemented system-wide to improve the quality and safety of systemic treatment delivery.
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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 | 0.114 | 0.343 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".