Enhancing provider adoption of patient-reported outcome measures (PROMs) through implementation science: insights from two international workshops
Bibliographic record
Abstract
BACKGROUND: Although the use of patient-reported outcome measures (PROMs) in practice is increasing, successful implementation is contingent on engaging healthcare providers (HCPs). Using Implementation Science (IS), we present the content of two workshops hosted at the International Society for Quality-of-Life annual conferences for individuals seeking to implement PROMs collection and use in their settings. Our goals were to provide workshop participants with knowledge, tools, and resources to prepare HCPs for PROM adoption and to demonstrate tailored strategies to meet context-specific needs. METHODS: An interdisciplinary team with diverse expertise in PROMs implementation delivered two workshops guided by the Capability, Opportunity, Motivation - Behavior (COM-B) model and the Theoretical Domains Framework (TDF). Using dotmocracy, participants were asked to consider, for their local context, the factors most important for changing HCPs' behaviors to adopt PROMs in daily practice. RESULTS: The workshops incorporated IS theories, models, and frameworks (TMFs) to identify barriers faced by HCPs, support behavior change, and apply tailored theory-informed implementation strategies to prepare HCPs for PROM integration and evaluate adoption success. The factors rated the most important by workshop participants (n = 53) were woven into the discussions to illustrate the most common barriers encountered by HCPs adopting PROMs. Presenters drew on real-world practice and research experiences to identify promising implementation strategies, including education, training, behavioral modeling, persuasion, environmental restructuring, enablement, and audit and feedback to increase the capability, opportunity, and motivation of HCPs. CONCLUSIONS: Given the increasing evidence base supporting the role of PROMs in patient-centered care, it is imperative to understand the mechanisms and best practices for increasing provider adoption of PROMs. This work offers a roadmap for understanding determinants more important to HCPs and systematically selecting theory-informed implementation strategies that may increase the likelihood of HCP adoption of PROMs. Offering tailored HCP training/education programs and implementation strategies can prepare HCPs for timely and effective PROM implementation.
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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 | 0.052 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| 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".