Computerized Prescriber Order Entry in the Outpatient Oncology Setting: From Evidence to Meaningful Use
Bibliographic record
Abstract
BACKGROUND: Chemotherapy is an effective treatment in the fight against many cancers. Medication errors in oncology can be particularly serious given the narrow therapeutic window of antineoplastic drugs and their high toxicities. Computerized prescriber order entry (cpoe) has consistently been shown to reduce medication errors and adverse drug events in various settings, but its use in the oncology setting has not been well established. To gain a better understanding of the meaningful use of cpoe systems in the outpatient chemotherapy setting, we undertook a systematic review of systemic therapy cpoe. METHODS: A province-wide expert panel consisting of clinical experts, health information professionals, and specialists in human factors design provided guidance in the development of the research questions, search terms, databases, and inclusion criteria. The systematic review was undertaken by a core team consisting of a medical oncologist, nurse, pharmacist, and methodologist. The medline, embase, cinahl, and compendex databases were searched for relevant evidence. RESULTS: The database searches resulted in 5642 hits, of which 9 met the inclusion criteria and were retained. In the oncology setting, cpoe systems generally reduce chemotherapy medication errors; however, specific types of errors increase with the use of cpoe. These systems affect practice both positively and negatively with respect to time, workload, and productivity. CONCLUSIONS: Despite the paucity of oncology-specific research, cpoe should be used in outpatient chemotherapy delivery to reduce chemotherapy-related medication errors. Adoption by clinicians will be enhanced by cpoe processes that complement current practice and workflow processes.
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 | 0.068 | 0.352 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".