The uses of Patient Reported Experience Measures in health systems: A systematic narrative review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 it