Selecting, implementing and evaluating patient-reported outcome measures for routine clinical use in cancer: the Cancer Care Ontario approach
Bibliographic record
Abstract
BACKGROUND: The use of Patient-Reported Outcome Measures (PROMs) in routine clinical care can help ensure symptoms are identified, acknowledged and addressed. In 2007, the provincial cancer agency, Cancer Care Ontario, began to implement routine symptom screening with the Edmonton Symptom Assessment System (ESAS) for ambulatory cancer patients. Having had a decade of experience with ESAS, the program developed a strategic interest in implementing new and/or additional measures. This article describes the development of a streamlined PROM selection and implementation evaluation process with core considerations. METHODS: Development of the PROM selection and implementation evaluation process involved analysis of quantitative and qualitative data as well as consensus building through a multi-stakeholder workshop. Core PROM selection considerations were developed through a literature scan, review and refinement by a panel of methodological experts and patient advisors, and testing via a test case. Core PROM implementation evaluation considerations were developed through analysis of PROM evaluation frameworks, and review and refinement by a committee of provincial implementation leads. RESULTS: Core PROM selection considerations were identified under three overarching themes: symptom coverage, usability and psychometric properties. The symptom coverage category assesses each PROM to determine how well the PROM items address the most prevalent and burdensome symptoms in the target patient population. The usability category aims to assess each measure on characteristics key to successful implementation in the clinical setting. The psychometric properties category assesses each PROM to ensure the data collected is credible, meaningful and interpretable. A scoring system was developed to rate PROM performance by assigning a grade of "weak", "average" or "good" for each category. The process results in a summary matrix which illustrates the overall assessment of each PROM. Implementation evaluation considerations were identified under three overarching concepts: acceptability, outcomes, and sustainability. A consensus building exercise resulted in the further identification of patient, provider, and clinic specific indicators for each consideration. CONCLUSION: To address the need for a systematic, evidence-based approach to selection, implementation and evaluation of PROMs in the clinical setting, Cancer Care Ontario defined a process with embedded core considerations to facilitate decision-making and encourage standardization.
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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 | 0.249 | 0.255 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".