Improving patient experience in health care and oncology: A scoping review.
Bibliographic record
Abstract
209 Background: Patient-reported experience measures (PREMs) gather information directly from patients and capture their perspectives on their health care. Deficiencies identified by PREMs can lead to quality improvement (QI) interventions. The purpose of this review was to identify published and unpublished evidence on initiatives aimed to improve patient experience, to identify their areas of application and their overall impact on patient experience. Methods: We conducted a systematic literature review using MEDLINE (Ovid), EBM Reviews, HealthStar, PsycINFO, PubMed, PubMed Central, CINAHL, MEDLINE (Ebsco), Psychology & Behavioral Sciences, TRIP Database, EMBASE and Web of Science databases and several sources of grey literature. Inclusion criteria required the studies to evaluate an intervention or a systematic change aimed to improve patient experience and measured by a specific PREM. The search was limited to English language reports published between 1998 and 2018. Of the initial 780 articles, 318 were included in abstract reviews. 304 abstracts were excluded leaving 44 records for full text review. Results: 21 records were included in the final analysis (20 journal articles and 1 web report). Publication dates ranged between 2007 and 2018 in the USA, UK, Norway, Denmark, Belgium and Bangladesh. There were 8 QI initiatives, 6 randomized studies, 1 non-randomized trial, 3 mixed methods, 2 repeated cross-sectional studies and 1 national patient experience model. Areas of focus included hospital care, surgery, internal medicine, primary care and oncology. Nine studies had programmatic interventions and 12 had specific interventions. All specific interventions reported positive effects. Outcomes were variable in programmatic interventions, including 5 studies reporting positive effects, 3 neutral and 1 mixed effects. Conclusions: The effect of specific interventions aimed to improve patient experience is positive. There is limited data on the effect of programmatic initiatives and the factors that drive the improvement in patient experience. Such initiatives are needed to understand their impact on patient experience and person-centered 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 | 0.016 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".