Using an implementation science approach to implement and evaluate patient-reported outcome measures (PROM) initiatives in routine care settings
Bibliographic record
Abstract
PURPOSE: Patient-reported outcome and experience measures (PROMs/PREMs) are well established in research for many health conditions, but barriers persist for implementing them in routine care. Implementation science (IS) offers a potential way forward, but its application has been limited for PROMs/PREMs. METHODS: We compare similarities and differences for widely used IS frameworks and their applicability for implementing PROMs/PREMs through case studies. Three case studies implemented PROMs: (1) pain clinics in Canada; (2) oncology clinics in Australia; and (3) pediatric/adult clinics for chronic conditions in the Netherlands. The fourth case study is planning PREMs implementation in Canadian primary care clinics. We compare case studies on barriers, enablers, implementation strategies, and evaluation. RESULTS: Case studies used IS frameworks to systematize barriers, to develop implementation strategies for clinics, and to evaluate implementation effectiveness. Across case studies, consistent PROM/PREM implementation barriers were technology, uncertainty about how or why to use PROMs/PREMs, and competing demands from established clinical workflows. Enabling factors in clinics were context specific. Implementation support strategies changed during pre-implementation, implementation, and post-implementation stages. Evaluation approaches were inconsistent across case studies, and thus, we present example evaluation metrics specific to PROMs/PREMs. CONCLUSION: Multilevel IS frameworks are necessary for PROM/PREM implementation given the complexity. In cross-study comparisons, barriers to PROM/PREM implementation were consistent across patient populations and care settings, but enablers were context specific, suggesting the need for tailored implementation strategies based on clinic resources. Theoretically guided studies are needed to clarify how, why, and in what circumstances IS principles lead to successful PROM/PREM integration and sustainability.
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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 | 0.450 | 0.450 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".