Cancer Care Ontario's Computerized Physician Order Entry System: A Province-wide Patient Safety Innovation
Bibliographic record
Abstract
More than one-third of all women and men in Canada will develop cancer during their lifetimes. Cancer patients typically require complex chemotherapy regimens, specific to their type and stage of disease, to slow or stop cancer cells from growing, multiplying, or spreading to other parts of the body. Despite the complexity of managing medication regimens for cancer patients and the associated risks to patient safety, current medical oncology practice throughout most of Canada is still to use paper-based tools, policies and procedures. To increase patient safety by reducing prescription errors and to offer clinical decision support to medical oncologists across the province, Cancer Care Ontario (CCO) developed and implemented Canada's first, cancer-specific computerized physician order entry (CPOE) system. This e-health innovation is currently in use in 11 cancer centres, and represents the largest ambulatory oncology CPOE implementation in Canada, with a 100% implementation success rate, and greater than 90% physician adoption. This paper describes the critical success factors in the design and implementation of CCO's CPOE system, including Web-based training and ease of administration to maximize physician adoption, incorporating point-of-care access to clinical practice guidelines into the tool, and the use of CPOE data to monitor and increase access to anti-cancer drugs and patient safety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".