A tale of 2,000 charts: Measuring provider response to patient-reported outcome measures.
Bibliographic record
Abstract
157 Background: Patients undergoing cancer treatment often experience physical and psychosocial symptoms that go undetected by clinicians, which highlights the need to incorporate patient-reported outcome measures (PROMs) in routine care. Systematic symptom screening for cancer patients using the Edmonton Symptom Assessment System (ESAS) is standard practice in Ontario. However, provider response to PROMs is essential to addressing symptom burden. To measure provider response, Regional Cancer Centre (RCC) Leads and Cancer Care Ontario developed a chart audit process. The objective was to determine whether the clinical team acknowledged, assessed and/or addressed symptoms identified by ESAS screening. Methods: RCCs received a chart audit tool with preset options and a data dictionary. Sites audited at least 140 charts for seven of the ESAS symptoms. Sites used a business intelligence tool to access patient charts based on sampling parameters. RCCs were required to audit charts of patients whose ESAS symptom scores were moderate to severe (4-10), with at least five charts in the moderate range (4-6). Results: 2,380 charts from 13 RCCs were audited based on ESAS scores from September to December 2016. Symptoms were most often acknowledged when the intensity was severe (69.9%), regardless of symptom type. Acknowledgement (71.5%), assessment (67.7%) and intervention (55.8%) were most often offered to patients reporting pain. Patients reporting depression and anxiety were the least likely to have the symptom acknowledged (44.5%, 45.0%, respectively) and be offered assessments (45.8%, 50.1%, respectively) and interventions (35.7%, 36.6%, respectively). Patients reporting moderate to severe depression and anxiety most commonly declined interventions (7.8%, 7.7%, respectively). Conclusions: These data show that providers disproportionately respond to physical symptoms, which may be easiest to treat due to clear management plans and referral pathways. To truly offer person-centred care, the emotional burden related to cancer must also be addressed, and providers must be trained to properly respond to psychosocial symptoms. Chart audits identify gaps in symptom management and areas for quality improvement.
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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 | 0.018 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".