Effect of voice recognition on radiologist reporting time.
Bibliographic record
Abstract
OBJECTIVE: To study the effect that voice recognition (VR) has on radiologist reporting efficiency in a clinical setting and to identify variables associated with faster reporting time. METHODS: Five radiologists were observed during the routine reporting of 402 plain radiograph studies using either VR (n = 217)or conventional dictation (CD) (n = 185). Two radiologists were observed reporting 66 computed tomography (CT) studies using either VR (n = 39) or CD (n = 27). The time spent per reporting cycle, defined as the radiologist's time spent on a study from report finalization to the subsequent report finalization, was compared. As well, characteristics about the radiologist and their reporting style were collected and correlated against reporting time. RESULTS: For plain radiographs, radiologists took 13.4% (P= 0.048) more time to produce reports using VR, but there was significant variability between radiologists. Significant association with faster reporting times using VR included: English as a first language (r = -0.24), use of a template (r = -0.34), use of a headset microphone (r = -0.46), and increased experience with VR (r= -0.43). Experience as a staff radiologist and having a previous study for comparison did not correlate with reporting time. For CT, there was no significant difference in reporting time identified between VR and CD (P = 0.61). CONCLUSIONS: Overall, VR slightly decreases the reporting efficiency of radiologists. However, efficiency may be improved if English is a first language, a headset microphone, and macros and templates are used.
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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 | 0.008 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".