Lead User Design: Medication Management in Electronic Medical Records
Bibliographic record
Abstract
Improvements in medication management may lead to a reduction of preventable errors. Usability and user experience issues are common and related to achieving benefits of Electronic Medical Records (EMRs). This paper reports on a novel study that combines the lead user method with a safety engineering review to discover an innovative design for the medication management module in EMRs in primary care. Eight lead users were recruited that represented prescribers and clinical pharmacists with expertise in EMR design, evidence-based medicine, medication safety and medication research. Eight separate medication management module designs were prototyped and validated, one with each lead user. A parallel safety review of medicaiton management was completed. The findings were synthesized into a single common set of goals, activities and one interactive, visual prototype. The lead user method with safety review proved to be an effective way to elicit diverse user goals and synthesize them into a common design. The resulting design ideas focus on meeting the goals of quality, efficiency, safety, reducing the cognitive load on the user, and improving communication wih the patient and the care team. Design ideas are being adapted to an existing EMR product, providing areas for further work.
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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 | 0.032 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".