The effect of seniority and education on departmental dictation utilization
Bibliographic record
Abstract
BACKGROUND: Electronic medical records (EMR) are considered the best solution to improved dissemination of health information for patients. The associated transcription caused a significant cost increase in an academic pediatric center. An educational campaign was implemented to achieve cost-effective transcriptions without compromising the number of EMR transcriptions. METHODS: We analyzed the effect of seniority on transcription times over a 4-month period. We also compared the dictation volume before and 4 months after educational interventions. This study was performed in a pediatric academic center with both inpatient and outpatient transcription utilization analyzed. All clinicians providing pediatric care and utilizing the hospital-based transcription over the study time period were analyzed. Interventions included targeted education about efficiencies in transcription, time-based dictation costs, avoidance of lengthy pauses and unnecessary detail, shortening of total transcriptions, superfluous phrases as well as structured templates. Level of training by postgraduate year of training and seniority within faculty were measured for impact on dictation time and effect of education to improve times. RESULTS: Learners in year one had an average dictation time of 7.5 ± 2.2 minutes, which decreased with seniority to an average of 4.1 ± 2.2 minutes for senior faculty (0.0007, ANOVA). After educational initiatives were implemented, there was progressive decline in dictation utilization. The total dictation time decreased from 8,750 minutes per month in August 2009 to 4,296 minutes in December of 2009 (p = 0.0045, unpaired t-test). CONCLUSION: We identified a substantial need for education in dictation utilization and demonstrated that relatively simple interventions can result in substantial costs savings.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".