Toward Successful Migration to Computerized Physician Order Entry for Chemotherapy
Bibliographic record
Abstract
BACKGROUND: Computerized physician order entry (cpoe) systems allow for medical order management in a clinical setting. Use of a cpoe has been shown to significantly improve chemotherapy safety by reducing the number of prescribing errors. Usability of these systems has been identified as a critical factor in their successful adoption. However, there is a paucity of literature investigating the usability of cpoe for chemotherapy and describing the experiences of cancer care providers in implementing and using a cpoe system. METHODS: A mixed-methods study, including a national survey and a workshop, was conducted to determine the current status of cpoe adoption in Canadian oncology institutions, to identify and prioritize knowledge gaps in cpoe usability and adoption, and to establish a research agenda to bridge those gaps. Survey respondents were representatives of cancer care providers from each Canadian province. The workshop participants were oncology clinicians, human factors engineers, patient safety researchers, policymakers, and hospital administrators from across Canada, with participation from the United States. RESULTS: A variety of issues related to implementing and using a cpoe for chemotherapy were identified. The major issues concerned the need for better understanding of current practices of chemotherapy ordering, preparation, and administration; a lack of system selection and procurement guidance; a lack of implementation and maintenance guidance; poor cpoe usability and workflow support; and other cpoe system design issues. An additional three research themes for addressing the existing challenges and advancing successful adoption of cpoe for chemotherapy were identified: The need to investigate variances in workflows and practices in chemotherapy ordering and administrationThe need to develop best-practice cpoe procurement and implementation guidance specifically for chemotherapyThe need to measure the effects of cpoe implementation in medical oncology. CONCLUSIONS: Addressing the existing challenges in cpoe usability and adoption for chemotherapy, and accelerating successful migration to cpoe by cancer care providers requires future research focusing on workflow variations, chemotherapy-specific cpoe procurement needs, and implementation guidance needs.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".