Point-of-care clinical documentation: assessment of a bladder cancer informatics tool (<i>eCancerCare<sup>Bladder</sup></i>): a randomized controlled study of efficacy, efficiency and user friendliness compared with standard electronic medical records
Bibliographic record
Abstract
OBJECTIVE: To compare the use of structured reporting software and the standard electronic medical records (EMR) in the management of patients with bladder cancer. The use of a human factors laboratory to study management of disease using simulated clinical scenarios was also assessed. DESIGN: eCancerCare(Bladder) and the EMR were used to retrieve data and produce clinical reports. Twelve participants (four attending staff, four fellows, and four residents) used either eCancerCare(Bladder) or the EMR in two clinical scenarios simulating cystoscopy surveillance visits for bladder cancer follow-up. MEASUREMENTS: Time to retrieve and quality of review of the patient history; time to produce and completeness of a cystoscopy report. Finally, participants provided a global assessment of their computer literacy, familiarity with the two systems, and system preference. RESULTS: eCancerCare(Bladder) was faster for data retrieval (scenario 1: 146 s vs 245 s, p=0.019; scenario 2: 306 vs 415 s, NS), but non-significantly slower to generate a clinical report. The quality of the report was better in the eCancerCare(Bladder) system (scenario 1: p<0.001; scenario 2: p=0.11). User satisfaction was higher with the eCancerCare(Bladder) system, and 11/12 participants preferred to use this system. LIMITATIONS: The small sample size affected the power of our study to detect differences. CONCLUSIONS: Use of a specific data management tool does not appear to significantly reduce user time, but the results suggest improvement in the level of care and documentation and preference by users. Also, the use of simulated scenarios in a laboratory setting appears to be a valid method for comparing the usability of clinical software.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".