Evaluating an Oncology Systemic Therapy Computerized Physician Order Entry System Using International Guidelines
Bibliographic record
Abstract
Chemotherapy is prone to medication error resulting from complexities in ordering and administration. Computerized physician order entry (CPOE) has been established as an important tool to minimize such errors and hence improve patient safety. As a leading Canadian advisory body in oncology, Cancer Care Ontario (CCO) has been a champion in developing and implementing its own cancer systemic therapy CPOE, the Oncology Patient Information System (OPIS). This article reviews and consolidates principles for oncology CPOE systems as found in the literature and in guidelines created by three international oncology organizations (American Society of Clinical Oncology, Clinical Oncological Society of Australia, and CCO). It then evaluates OPIS by these standards and provides a working example of what a cancer CPOE system should look like. This document can therefore be used as a framework to help develop and evaluate cancer CPOE platforms in different national settings. As end users, oncologists are considered key stakeholders in developing such systems and thus should be well informed about CPOE principles to help make decisions on the appropriate implementation of these platforms in their local practice settings. In addition, oncologists are also important champions for the successful uptake of oncology CPOE platforms and would benefit from a better understanding of whether proposed or existing local CPOE systems meet established standards.
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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 | 0.021 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".