Improving the measurement of clinician response to symptom screening: Auditing the symptom management of cancer patients in Ontario.
Bibliographic record
Abstract
218 Background: Since 2007, Cancer Care Ontario has provincially mandated symptom screening of cancer patients using the Edmonton Symptom Assessment Scale (ESAS). Screening rates are the primary performance metric used across 14 Regional Cancer Centres (RCCs), however this indicator does not detail how symptoms are managed during the clinic visit. The objective is to share our accomplishments using a chart audit process and share upcoming changes aimed to improve the measurement of clinician response to symptom screening. Methods: The initial chart audit tool was created by regional stakeholders who oversee cancer symptom management. The tool measures the concordance between clinician response to the patient symptom screen and best practices outlined in the Cancer Care Ontario Symptom Management Guides. RCCs conduct an annual chart audit for seven of the ESAS symptoms, and sample at least 20 patients with high symptom scores (>=4). Chart documentation is analyzed to determine whether the patient received appropriate assessment and interventions. Results: 7,952 chart audits have been completed since 2012. Patients reporting symptom burden in FY 2015/16 often received an assessment (72-93%) and intervention (48-79%) during the clinic visit, depending on the symptom. Annual provincial aggregate results are intended to support quality improvement, inform clinician symptom management education, and provide a snapshot of resource utilization. Shortcomings of the audit have been identified over time. These highlight the need for more actionable items and a standardized process to ensure representative sampling, as aggregate results can be biased when RCCs co-opt the audit to measure local projects. This prompted an overhaul of the audit process: new audit tool created; results linkable to administrative data; and objectives clearly defined to balance local and provincial needs. Conclusions: Measuring the frequency of symptom screening is important, but the most clinically relevant question is whether the patient’s symptoms are being managed. Changing the chart audit process will ease the burden on auditors, improve data quality, and ensure data utility at all levels.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".