User-Centered Design and Comparison of Two Electronic Health Record Tools to Support the Ordering of Crisantaspase Recombinant Chemotherapy
Bibliographic record
Abstract
PURPOSE Chemotherapy ordering errors can have serious safety implications in pediatric oncology. Computerized provider order entry systems may reduce chemotherapy ordering errors. Crisantaspase recombinant (crisantaspase) is a chemotherapy drug used in pediatric leukemia that poses significant safety risk when ordering because of complex dosing and monitoring. The aim of this study was to compare errors, satisfaction, and efficiency between two approaches to ordering in our electronic health record: namely, the standard treatment plan order group (OG) and a novel supportive care plan (SCP). METHODS We recruited oncology providers and nurses at an academic pediatric institution. Providers were asked to complete two simulated chemotherapy ordering sessions using the treatment plan OG and the SCP. Order entry errors were assessed in seven domains, and the total number of order entry errors was calculated. Satisfaction was assessed using a five-point Likert scale, and satisfaction was defined as answering “Agree” or “Strongly Agree” to all five satisfaction questions. Efficiency was compared by measuring the time to complete the task. Errors, satisfaction, and efficiency were compared between the two tools. RESULTS We enrolled 14 providers and five nurses. The proportion of chemotherapy ordering errors was significantly lower with the SCP (5 of 98, 5.1%) compared with the treatment plan OG (11 of 98, 11.2%; P < .01). The SCP significantly improved provider efficiency, reducing the time taken to complete order entry from 16.3 minutes with the OG to 7.7 minutes with the SCP (mean difference, 8.6 minutes; P < .001). Provider satisfaction was significantly higher with the SCP (12 of 14, 85.7%) compared with the treatment plan OG (2 of 14, 14.2%; P < .001). CONCLUSION Use of a novel SCP instead of a tradition treatment plan OG improved provider efficiency and satisfaction while decreasing order entry errors. Thoughtful design and usability testing of chemotherapy order tools is needed to maximize their utility.
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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 | 0.026 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".