DESIGNING A PHYSICIAN-FRIENDLY INTERFACE FOR AN ELECTRONIC MEDICAL RECORD SYSTEM
Bibliographic record
Abstract
An Electronic Medical Record (EMR) system enables a physician to record patients’ health information, request reports from third partly health care providers and retrieve these reports when they are ready. Despite the numerous benefits of EMRs, several factors have inhibited their widespread adoption. An underappreciated but critical factor has been the proliferation of inferior user interfaces which are confusing to navigate and disruptive to a physician’s workflow. To be useful, an EMR must allow physicians to record and query information in a natural manner that accommodates the non-linear nature of their workflow. In particular, an interface must permit a physician to record the minutiae of a patient’s condition while at the same time preserving the physician’s overview of a patient’s record so that any aspect of the patient’s health can be effortlessly queried and inspected. This paper proposes an interface design that attempts to address several of the usability deficiencies associated with current electronic medical record systems in use today.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".