Personalized symptom management: a quality improvement collaborative for implementation of patient reported outcomes (PROs) in ‘real-world’ oncology multisite practices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Little research has focused on implementation of electronic Patient Reported Outcomes (e-PROs) for meaningful use in patient management in 'real-world' oncology practices. Our quality improvement collaborative used multi-faceted implementation strategies including audit and feedback, disease-site champions and practice coaching, core training of clinicians in a person-centered clinical method for use of e-PROs in shared treatment planning and patient activation, ongoing educational outreach and shared collaborative learnings to facilitate integration of e-PROs data in multi-sites in Ontario and Quebec, Canada for personalized management of generic and targeted symptoms of pain, fatigue, and emotional distress (depression, anxiety). PATIENTS AND METHODS: We used a mixed-methods (qualitative and quantitative data) program evaluation design to assess process/implementation outcomes including e-PROs completion rates, acceptability/use from the perspective of patients/clinicians, and patient experience (surveys, qualitative focus groups). We secondarily explored impact on symptom severity, patient activation and healthcare utilization (Ontario sites only) comparing a pre/post population cohort not exposed/exposed to our implementation intervention using Mann Whitney U tests. We hypothesized that the iPEHOC intervention would result in a reduction in symptom severity, healthcare utilization, and higher patient activation. We also identified key implementation strategies that sites perceived as most valuable to uptake and any barriers. RESULTS: Over 6000 patients completed e-PROs, with sites reaching 51%-95% population completion rates depending on initial readiness. e-PROs were acceptable to patients for communicating symptoms (76%) and by clinicians for treatment planning (80%). Patient experience was better than the provincial average. Compared to the pre-population, we observed a significant reduction in levels of anxiety (p = 0.008), higher levels of patient activation (p = 0.045), and reduced hospitalization rates (12.3% not exposed vs 10.1% exposed, p = 0.034). A pre/post population trend towards significance for reduced emergency department visit rates (14.8% not exposed vs 12.8% exposed, p = 0.081) was also noted. CONCLUSION: This large-scale pragmatic quality improvement project demonstrates the impact of implementation strategies and a collaborative improvement approach on acceptability of using PROs in clinical practice and their potential for reducing anxiety and healthcare utilization; and improving patient experience and patient activation when implemented in 'real-world' multi-site oncology practices.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 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 it