Using Implementation Science to Evaluate the Implementation of Patient-Reported Outcome Measures (PROMs) in a Clinical Heart Failure Care Setting
Bibliographic record
Abstract
Background: Patients with heart failure (HF) can experience a poor quality-of-life (QOL), recurring hospitalizations, and progressive disease symptoms. Patient-reported outcome measures (PROMs) integrate patients' voices into clinical care, by assessing patient symptoms, function, and QOL. In 2022, PROMs were incorporated into the electronic health record system (Epic) at a large academic hospital in Toronto, Ontario, Canada. The purpose of this study was to use implementation-science frameworks to systematically evaluate the uptake and integration of PROMs into clinical HF care. Methods: The Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework guided this mixed-methods, 1-year, quality-improvement project. Data sources included the following: clinician use of PROMs; patient-level data on completed PROMs; and semistructured interviews with clinicians. The PROM was the Kansas City Cardiomyopathy Questionnaire-12, which captures 4 domains related to HF-symptom frequency, physical limitations, social limitations, and QOL (KCCQ-12 is used as an example case of PROMs in general). Quantitative data were analyzed using descriptive statistics; qualitative data were analyzed using behaviour-change frameworks and latent content analysis. Results: Over the course of 1 year, more patients were assigned to PROMs, a higher proportion of patients completed PROMs, and approximately 80% of patients had high scores on the questionnaire. Clinicians experience barriers-related to attention and decision processes, the environmental context, and their professional role-to integrating PROMs into practice. Suggested changes to improve PROM uptake include adding language licenses for PROM translations, reducing cognitive load for clinicians who are assigning and interpreting PROMs in the Epic system, and championing modelling of use of PROMs in practice. Conclusions: This study demonstrates the benefit of using implementation science frameworks, to evaluate the implementation of PROMs in practice and provide actionable recommendations to health systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".