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 Webbased 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. Cancer Treatment: Prime Candidate for a CPOE SolutionWhen it comes to medication safety, few diseases pose as big a challenge as cancer.Cancer encompasses over one hundred distinct diseases, and roughly half of all cancer patients will require chemotherapy in the course of treatment.A regimen of chemotherapy may be prescribed to destroy cancer cells, slow or control the growth and spread of a tumour, or relieve symptoms and improve a patient's quality of life.Chemotherapy is inherently toxic to cells and can cause a host of moderate to severe side effects.Since much of chemotherapy is infused intravenously, where the impact on the body is rapid and direct, there is little room for error, particularly in dosing.This is all the more important as cancer patients are likely to receive repeated infusions over time.To be both safe and effective, these regimens must be carefully tailored to the patient.If a dose is too low, it will not be strong enough to attack cancer cells; if too high, it could prove intolerable or even fatal.Determining a safe and effective chemotherapy regimen is dependent on a patient's type of cancer; the size, spread and genetic expression of the tumour; the patient's age, body surface area (calculated from their height and weight), medication allergies and general health status; and other factors.Factors affecting the appropriateness of a given regimen include the intent of treatment (curative or palliative); the right medications; dosing schedule, and timing of treatment relative to surgery and radia- Cancer Care Ontario's Computerized Physician Order Entry System: A Province-wide Patient Safety Innovation
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".