Improving symptom screening rates for patients with head and neck cancer.
Bibliographic record
Abstract
e23246 Background: Effective symptom management in cancer care relies on regular and accurate reporting using validated patient-reported outcome measures (PROMs), such as the Edmonton Symptom Assessment System (ESAS). The ESAS measures nine common cancer symptoms, rating them on a scale from 0 to 10 to improve monitoring and communication regarding patient symptoms and prevent adverse outcomes such as emergency department visits. There is a need to enhance completion rates to ensure that head and neck cancer (HNC) symptoms are effectively addressed. High completion rates provide valuable data for cancer centers and clinicians to intervene early and support patients through their cancer journey. This makes it an important target for quality improvement (QI) with wide-ranging implications for patient outcomes and care quality. Our objectives were to: Investigate underlying reasons for poor ESAS completion rates. Lay the groundwork for strategic interventions aimed at bolstering symptom screening among HNC patients. Methods: This was a QI project targeting the HNC patient population at a regional cancer centre (RCC). Our approach included stakeholder engagement meetings alongside the expertise of QI specialists. Comprehensive data collection was conducted to analyze symptom screening rates and identify potential barriers to ESAS completion. We tracked ESAS completion on weekly HNC clinic days from 2022-2023 via manual and automated chart abstraction. Patient, staff, and volunteer interviews, along with direct clinic observations, provided data for Ishikawa diagrams, facilitating a root cause analysis. Results: We identified a marked decline in ESAS completion rates, steadily decreasing from an average of 29.4% during the 2022-2023 fiscal year to an average of 7.6% between April-November 2023, with rates dropping as low as 3.3% in September 2023. Root cause analysis pinpointed several barriers to ESAS completion, including the transition from paper-based assessments used during the pandemic to electronic formats, low patient awareness of the purpose of symptom screening, perceived lack of value in screening by patients and staff, and the absence of direct guidance from staff in helping patients complete their ESAS. Variable communication practices and inconsistencies in registration staff directing patients to kiosks was also observed. Improving infrastructure and support for volunteer services was identified as a potential solution, which may optimize their effectiveness in symptom screening roles. Conclusions: The project has laid the foundation for targeted QI interventions aimed at improving ESAS screening adherence. By addressing the identified barriers, this work endeavors to re-engage patients in active symptom reporting and to integrate this critical aspect of care into the daily routine of the outpatient hospital setting, thereby enhancing the overall management of HNC symptoms.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".