020 Best Practices and Performance Measures for Systemic Treatment Perscriber Order Entry Systems (STCPOE) in Chemotherapy Delivery
Bibliographic record
Abstract
Background While information technology (IT) has the potential to improve the quality and safety of patient care, solutions such as computerised physician order entry (CPOE) are often designed and executed without end-user involvement and lack performance measures for monitoring quality and impact. To address this gap, an evidence based guideline for systemic treatment (ST) CPOE was developed incorporating both clinical and technological best practices. Performance measures for monitoring clinical impacts and system functionality were also developed. Context The ST CPOE guideline was developed by a panel of physicians, nurses, pharmacists, IT specialists and human factors experts. Two Expert Panels (i.e. Clinical and Technology) were convened, to review and provide feedback on guideline content. Description of Best Practice The guideline contains two distinct yet interconnected parts: clinical practice (e.g. error prevention, utilisation, clinical decision support), and technology requirements (e.g. usability, system integration, effective alerts). Also included are evidence based indicators to support the evaluation of ST CPOE systems and indicators reflecting clinician practice and patient outcomes. Quality monitoring of ST CPOE utilisation reveal that 75.5% of all chemotherapy visits are being supported by an ST CPOE system. A provincial evaluation of existing ST CPOE systems against the technology best practices is currently underway. Lessons for Guideline Developers, Adaptors, Implementers, and/or Users This innovative guideline focuses on clinical practice driving IT solutions, not the other way around. A priori commitment to indicator development allowed for expanding beyond describing best practices to including indicators for monitoring progress toward achieving best practice, thus increasing relevance and uptake by end users.
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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 | 0.112 | 0.295 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| 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".