Real-World Patient-Reported Outcome Measure Implementation: Challenges and Successes of a Pan-Canadian Initiative to Improve the Future of Patient-Centered Cancer Care
Bibliographic record
Abstract
PURPOSE: Implementation of patient-reported outcome measures (PROMs) in routine care continues to be limited, despite their demonstrated efficacy and substantial investments. We report on the lessons learned and the challenges of the concerted implementation of the same PROMs across teams in nine provinces/territories (jurisdictions) in Canada, as well as the solutions to move implementation forward despite cost containments and the COVID-19 pandemic. METHODS: Each team from nine jurisdictions submitted a final report describing their PROM implementation project. Reports were analyzed for themes on lessons learned, challenges, and solutions. Themes were compared for similarities and differences. The Standards for Quality Improvement Reporting Excellence (SQUIRE) guidelines were used. RESULTS: Six key lessons learned were identified from eight challenges. To address these challenges, 27 solutions were used. The six lessons learned were as follows: Harness the power of change management, ensure consistent stakeholder engagement at all levels for success, establish buy-in as soon as possible, plan to maintain buy-in through changing circumstances, identify ways to make technology the solution, and optimal implementation includes a sustainability plan. Examples of solutions included the following: develop a multipronged, multilevel communication plan; include change management experts on the team; identify champions; restructure and reprioritize as needed; leverage existing technology; and leave a permanent trace of the project. CONCLUSION: To our knowledge, this is the first analysis to synthesize lessons learned from real-world PROM implementation across geographically diverse jurisdictions. We identified generalizable solutions that other health care managers and policymakers can use to accelerate PROM implementation, despite pervasive implementation barriers. Future studies can integrate these solutions with methods and tools from implementation science (eg, theoretical frameworks, implementation strategies) for more successful spread and scale of PROMs.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".