Optimizing the design and implementation of question prompt lists to support person‐centred care: A scoping review
Bibliographic record
Abstract
INTRODUCTION: Question prompt lists (QPLs) are lists of questions that patients may want to discuss with clinicians. QPLs support person-centred care and have been associated with many beneficial outcomes including improved patient question-asking, and the amount and quality of the information provided by clinicians. The purpose of this study was to review published research on QPLs to explore how QPL design and implementation can be optimized. METHODS: We performed a scoping review by searching MEDLINE, EMBASE, Scopus, CINAHL, Cochrane Library and Joanna Briggs Database from inception to 8 May 2022, for English language studies of any design that evaluated QPLs. We used summary statistics and text to report study characteristics, and QPL design and implementation. RESULTS: We included 57 studies published from 1988 to 2022 by authors in 12 countries on a range of clinical topics. Of those, 56% provided the QPL, but few described how QPLs were developed. The number of questions varied widely (range 9-191). Most QPLs were single-page handouts (44%) but others ranged from 2 to 33 pages. Most studies implemented a QPL alone with no other accompanying strategy; most often in a print format before consultations by mail (18%) or in the waiting room (66%). Both patients and clinicians identified numerous benefits to patients of QPLs (e.g., increased patient confidence to ask questions, and patient satisfaction with communication or care received; and reduced anxiety about health status or treatment). To support use, patients desired access to QPLs in advance of clinician visits, and clinicians desired information/training on how to use the QPL and answer questions. Most (88%) studies reported at least one beneficial impact of QPLs. This was true even for single-page QPLs with few questions unaccompanied by other implementation strategies. Despite favourable views of QPLs, few studies assessed outcomes amongst clinicians. CONCLUSION: This review identified QPL characteristics and implementation strategies that may be associated with beneficial outcomes. Future research should confirm these findings via systematic review and explore the benefits of QPLs from the clinician's perspective. PATIENT/PUBLIC CONTRIBUTION: Following this review, we used the findings to develop a QPL on hypertensive disorders of pregnancy and interviewed women and clinicians about QPL design including content, format, enablers and barriers of use, and potential outcomes including beneficial impacts and possible harms (will be published elsewhere).
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".