Designing a medication timeline for patients and physicians
Bibliographic record
Abstract
Objective: Most electronic health records display historical medication information only in a data table or clinician notes. We designed a medication timeline visualization intended to improve ease of use, speed, and accuracy in the ambulatory care of chronic disease. Materials and Methods: We identified information needs for understanding a patient medication history, then applied human factors and interaction design principles to support that process. After research and analysis of existing medication lists and timelines to guide initial requirements, we hosted design workshops with multidisciplinary stakeholders to expand on our initial concepts. Subsequent core team meetings used an iterative user-centered design approach to refine our prototype. Finally, a small pilot evaluation of the design was conducted with practicing physicians. Results: We propose an open-source online prototype that incorporates user feedback from initial design workshops, and broad multidisciplinary audience feedback. We describe the applicable design principles associated with each of the prototype's key features. A pilot evaluation of the design showed improved physician performance in 5 common medication-related tasks, compared to tabular presentation of the same information. Discussion: There is industry interest in developing medication timelines based on the example prototype concepts. An open, standards-based technology platform could enable developers to create a medication timeline that could be deployable across any compatible health IT application. Conclusion: The design goal was to improve physician understanding of a patient's complex medication history, using a medication timeline visualization. Such a design could reduce temporal and cognitive load on physicians for improved and safer care.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".