The uses of Patient Reported Experience Measures in health systems: A systematic narrative review
Bibliographic record
Abstract
BACKGROUND: Many governments have programmes collecting and reporting patient experience data, captured through Patient Reported Experience Measures (PREMs). Our study aims to capture and describe all the ways in which PREM data are used within healthcare systems, and explore the impacts of using PREMs at one level (e.g. national health system strategy) on other levels (e.g. providers). METHODS: We conducted a narrative review, underpinned by a systematic search of the literature. RESULTS: 1,711 unique entries were identified through the search process. After abstract screening, 142 articles were reviewed in full, resulting in 28 for final inclusion. A majority of papers describe uses of PREMs at the micro level, focussed on improving quality of front-line care. Meso-level uses were in quality-based financing or for performance improvement. Few macro-level uses were identified. We found limited evidence of the impact of meso‑ and macro- efforts to stimulate action to improve patient experience at the micro-level. CONCLUSIONS: PREM data are used as performance information at all levels in health systems. The use of PREM data at macro- and meso‑ levels may have an effect in stimulating action at the micro-level, but there is a lack of systematic evidence, or evaluation of these micro-level actions. Longitudinal studies would help better understand how to improve patient experience, and interfaces between PREM scores and the wider associated positive 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 | 0.029 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".