Optimizing the Design of Preprinted Orders for Ambulatory Chemotherapy: Combining Oncology, Human Factors, and Graphic Design
Bibliographic record
Abstract
PURPOSE: To establish a set of guidelines for developing ambulatory chemotherapy preprinted orders. METHODS: Multiple methods were used to develop the preprinted order guidelines. These included (A) a comprehensive literature review and an environmental scan; (B) analyses of field study observations and incident reports; (C) critical review of evidence from the literature and the field study observation analyses; (D) review of the draft guidelines by a clinical advisory group; and (E) collaboration with graphic designers to develop sample preprinted orders, refine the design guidelines, and format the resulting content. RESULTS: The Guidelines for Developing Ambulatory Chemotherapy Preprinted Orders, which consist of guidance on the design process, content, and graphic design elements of ambulatory chemotherapy preprinted orders, have been established. CONCLUSION: Health care is a safety critical, dynamic, and complex sociotechnical system. Identifying safety risks in such a system and effectively addressing them often require the expertise of multiple disciplines. This study illustrates how human factors professionals, clinicians, and designers can leverage each other's expertise to uncover commonly overlooked patient safety hazards and to provide health care professionals with innovative, practical, and user-centered tools to minimize those hazards.
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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 | 0.060 | 0.173 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".