What is measured matters: A scoping review of analysis methods used for qualitative patient reported experience measure data
Bibliographic record
Abstract
INTRODUCTION: Hospitals are increasingly turning to patients for valuable feedback regarding their care experience. A common method to collect this information is patient reported experience measures (PREMs) surveys. Health care workers report qualitative PREMs as more interesting, relevant, and informative than quantitative survey responses. However, a major barrier to utilising qualitative PREMs data to drive quality improvements is a lack of resources to analyse the data. This scoping review aimed to review the methods used to analyse qualitative PREMs survey data from routine hospital care. METHODS: We utilised the JBI scoping review methodology, and searched four databases for articles from 2013 to 2023 which analysed qualitative PREMs survey data from routine care in hospitals. Study characteristics were extracted, as well as the analysis method - specifically, whether the study used traditional manual analysis methods in which the researcher reads the text and categorise the data, or automated methods utilising computers and algorithms to read and categorise the data. RESULTS: From 960 unique articles, 123 went through full-text review and 54 were deemed eligible. 75.9 % used only manual content analysis methods to analyse the qualitative responses, 16.7 % of studies used a combination of manual and automated methods, and only 7.4 % used exclusively automated methods. Automated methods were used in 27.5 % of studies published 2019-2023, compared to 14.3 % of studies published 2013-2018. All bar one study using automated methods focused on investigating the validity of the automated methodology or used it to complement manual content analysis. CONCLUSION: The studies included in this review show a transition from traditional time-consuming manual analyses to computerised methods enabling analysis at a larger scale. As the volume of PREMs data collected grows, efficient and effective ways to analyse qualitative PREMs data at scale are required to enable health services to capture the patient voice and drive consumer-centred improvements in care.
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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 | 0.237 | 0.503 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.065 | 0.054 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".