The use of patient experience data for quality improvement in hospitals: A scoping review
Bibliographic record
Abstract
In this paper we identified what was reported in the literature on qualitative and quantitative approaches used to capture and improve patient experiences in a hospital setting. For inclusion, articles were required to describe an embedded strategy for capturing patient experiences that was used to inform quality improvement in a hospital setting. Articles also had to be published in English between January 2004 and December 2020. Six databases (MEDLINE, EMBASE, PsycINFO, CINAHL, Health and Psychosocial Instruments and Cochrane Library) and grey literature (relevant hospital and government websites) were searched. All articles were screened by two reviewers and any disagreements were resolved through consensus. Data were extracted from the included articles using a study-specific form in Microsoft Excel and synthesized using descriptive qualitative and quantitative approaches. Thirty articles were included in this scoping review. Patient experience data were captured through a variety of methods including surveys, focus groups, patient complaints and informal feedback, with the majority using formal, paper-based surveys. A wide range of quality improvement initiatives were implemented as a result of hospitals’ patient experience data, but there was limited contextual information regarding the hospital settings and population characteristics. Initiatives implemented by a dedicated and multidisciplinary quality improvement team (nurses, administrators, physicians, etc.) generally demonstrated positive outcomes. We conclude that more work is needed to better understand how best to capture and use patient experience data for quality improvement, the contexts in which initiatives are successful and how to integrate patients and families in the ongoing implementation and evaluation processes. Experience Framework This article is associated with the Policy & Measurement lens of The Beryl Institute Experience Framework. (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens.
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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 | 0.115 | 0.266 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.048 | 0.048 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| 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".