Measuring Patient-Reported Outcomes to Improve Cancer Care in Canada: An Analysis of Provincial Survey Data
Bibliographic record
Abstract
Patient-reported outcomes measures (proms) are an important component of the shift from disease-centred to person-centred care. In oncology, proms describe the effects of cancer and its treatment from the patient perspective and ideally enable patients to communicate to their providers the physical symptoms and psychosocial concerns that are most relevant to them. The Edmonton Symptom Assessment System-revised (esas-r) is a commonly used and validated tool in Canada to assess symptoms related to cancer. Here, we describe the extent to which patient-reported outcome programs have been implemented in Canada and the severity of symptoms causing distress for patients with cancer. As of April 2017, 8 of 10 provinces had implemented the esas-r to assess patient-reported outcomes. Data capture methods, the proportion of cancer treatment sites that have implemented the esas-r, and the time and frequency of screening vary from province to province. From October 2016 to March 2017 in the 8 reporting provinces, 88.0% of cancer patients were screened for symptoms. Of patients who reported having symptoms, 44.3% reported depression, with 15.5% reporting moderate-to-high levels; 50.0% reported pain, with 18.6% reporting moderate-to-high levels; 56.2% reported anxiety, with 20.4% reporting moderate-to-high levels; and 75.1% reported fatigue, with 34.4% reporting moderate-to-high levels. There are some notable areas in which the implementation of proms could be improved in Canada. Findings point to a need to increase the number of cancer treatment sites that screen all patients for symptoms; to standardize when and how frequently patients are screened across the country; to screen patients for symptoms during all phases of their cancer journey, not just during treatment; and to assess whether giving cancer care providers real-time patient-reported outcomes data has led to appropriate interventions that reduce the symptom burden and improve patient outcomes. Continued measurement and reporting at the system level will allow for a better understanding of progress in proms activity over time and of the areas in which targeted quality improvement efforts could ensure that patient symptoms and concerns are being addressed.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".