Usability of a mobile electronic medical record prototype: a verbal protocol analysis
Bibliographic record
Abstract
BACKGROUND: Point of care access to electronic medical records may provide clinicians with the information they want when they need it and may in turn improve patient safety. Yet providing an electronic medical record on handheld devices presents many usability challenges, and it is unclear whether clinicians will use them. METHODS: An iterative design process for the development and evaluation of a prototype of a mobile electronic medical record was performed. Usability sessions were conducted in which physicians were asked to 'think aloud' while working through clinical scenarios using the prototype. Verbal protocol analysis, which consists of coding utterances, was conducted on the transcripts from the sessions and common themes were extracted. RESULTS: Usability sessions were held with five family physicians and four internists with varying levels of computer expertise. Physicians were able to use the device to complete 52 of 54 required tasks. Users commented that it was intuitive (9/9), would increase accessibility (5/9) but for them to use it, it would need the system to be fast and time-saving (5/9). Users had difficulty entering information (5/9) and reading the screen (4/9). In terms of functionality, users had concerns about completeness of information (6/9), details of ordering (5/9) and desired billing functionality (5/9) and integration with other systems (4/9). CONCLUSIONS: While physicians can use mobile electronic medical records in realistic scenarios, certain requirements likely need to be met including a fast system with easy data selection, easy data entry and improved display before widespread adoption occurs.
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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 | 0.231 | 0.361 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".