Improving Discrete Documentation of Cancer Staging—An Alert-Free Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cancer staging is integral to ensuring cancer patients receive appropriate risk-adapted therapy. Discrete cancer staging using a structured staging form helps ensure accurate staging, provides a single source of truth for staging information, and allows for reporting to regulatory authorities. Our institution created pediatric oncology specific discrete staging forms that have been shared with the broader Epic community. By November 2023, baseline utilization of the staging form for patients with leukemia or lymphoma was 43%, and the override rate for our existing alert was 99.9%.Improve discrete documentation of cancer stage for patients with leukemia or lymphoma within 60 days following initiation of chemotherapy to >80% by July 2024 as measured by signed staging form.Model for improving plan-do-study-act (PDSA) cycles was implemented, and statistical process control charts were used to evaluate impact. The first intervention was educational training to oncology providers. The second PDSA cycle involved sharing monthly individual completion data with the primary oncologist regarding their personal patient metrics. The third PDSA cycle involved removing the interruptive alert.Within 6 months, documentation of primary oncologist improved from 86 to 100%, and initiation of staging form improved from 57 to 90%. Completion of signed cancer staging form reached 80%. Patients marked as not needing staging increased from 5 to 17%.Completion of a digital cancer staging form is important for continuity of care, and to facilitate reporting to regulatory authorities, though frequent interruptive alerts were an ineffective method for improving documentation. Education and data sharing increased staging completion to near target, with ongoing efforts to reach the goal of 80%.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it