Stakeholders’ perspectives on implementing and integrating patient-reported outcome measures (PROMs) in health systems - insights from Alberta, Canada
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcome measures (PROMs) are valuable tools for evaluating outcomes of healthcare interventions and have been increasingly used in health systems around the world. While PROMs adoption has grown globally, variations exist in their use across the health system. This stakeholder engagement and consultation activity aims to understand factors impacting PROMs adoption, implementation, and utilization, and identify strategies for enhancing utilization of PROMs data within the healthcare system in Alberta, Canada. METHODOLOGY: Key stakeholders from various roles were interviewed, including PROMs programs, health system improvement, data analytics, clinical practice, evaluation, health economics, and research, from diverse clinical areas, including cancer, primary care, epilepsy, rehabilitation, arthroplasty, cardiovascular surgery, and rheumatology. Interviews were recorded, transcribed and analyzed using thematic analysis. Results were synthesized using the Consolidated Framework for Implementation Research (CFIR) and Diffusion of Innovation Framework (DOI). RESULTS: A total of 25 interviews, lasting between 45 and 60 minutes, were completed. Factors impacting the adoption, implementation and utilization of PROMs within the Alberta healthcare system included: (1) Intervention characteristics: shared understanding of PROMs, challenges in capturing relevant patient outcomes, standardization and integration of PROMs, modalities of data collection, making PROMs actionable at the patient-clinician level, and interpreting PROMs data; (2) Inner setting: Cultural shift towards patient-centered care, change management and clinician mindset, organizational commitment and support, integration into broader measurement frameworks, access to PROMs data, potential for replication and adaptation, and importance of incentives and requirements; (3) Outer setting: Resource constraints, policy and systemic challenges, focus on value-based care, and responsible use of PROMs data; (4) Characteristics of individuals: Expertise and understanding of PROMs, and stakeholder engagement and education; and (5) Process: Balancing bottom-up and top-down approaches, workflow integration, patient engagement, and continuous evaluation and quality improvement. CONCLUSIONS: The study highlights factors influencing PROMs adoption in Alberta, including the need for a unified understanding, workflow integration, and electronic data use. Key strategies involve fostering patient-centered care, ensuring organizational support, addressing resource and policy issues, and providing targeted education. Engaging early adopters and offering incentives can improve PROMs integration and patient outcomes.
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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 | 0.025 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.025 | 0.007 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".