Slicer Dicer as a Potential Tool for Self-Assessment
Bibliographic record
Abstract
Background: Self-assessment is a crucial competency for physicians, yet the evidence suggests that physicians have limited ability to do so and assessment of competence requires external feedback. Slicer Dicer is a self-service reporting tool on Epic that enables efficient patient data collection, providing a potential tool to self-assess practice patterns. Our study aims to explore whether Slicer Dicer can collect meaningful data for physicians to practise self-assessment. We piloted this question collecting data on prescribing patterns of sodium-glucose co-transporter 2 inhibitors (SGLT2i) in nephrology clinic patients with diabetes mellitus (DM) and chronic kidney disease, aligning with current care standards for this cohort. Methods: Slicer Dicer was used to collect data from July 1, 2023 to December 31, 2023. Patients were captured using filters by provider, context, visit type, and medical history. “Slices” by medications were created to determine the number of patients on an SGLT2i. Results: Slicer Dicer captured 1357 patients in the nephrology clinic amongst thirteen nephrologists with a diagnosis of DM who may benefit from an SGLT2i. Of these, 627 (46.2%) of these patients were found to be on an SGLT2i. The percentage of patients on an SGLT2i ranged from 32.9% to 61.4%. Conclusion: We highlight the potential for Slicer Dicer as an innovative method to collect data using easily accessible EMR tools. Future directions will be aimed at how physicians can use this objective data to self-assess their practice by closer analysis of patients who are not meeting current care standards. - Patients on SGLT2i Total number of patients Percentage (%) of patients on SGLT2i Nephrologist #1 53 161 32.9 Nephrologist #2 94 225 41.8 Nephrologist #3 55 108 50.9 Nephrologist #4 87 205 42.4 Nephrologist #5 33 63 52.4 Nephrologist #6 62 101 61.4 Nephrologist #7 64 151 42.4 Nephrologist #8 37 91 40.7 Nephrologist #9 77 168 45.8 Nephrologist #10 58 105 55.2 Nephrologist #11 88 166 53.0 Nephrologist #12 54 115 47.0 Nephrologist #13 20 41 48.8 Total 627 1357 46.2 Patients in nephrology clinic with DM from Jul 1-Dec 31, 2023 by nephrologist
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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 | 0.030 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".