Do patients and health care providers have discordant preferences about which aspects of treatments matter most? Evidence from a systematic review of discrete choice experiments
Bibliographic record
Abstract
OBJECTIVES: To review studies eliciting patient and healthcare provider preferences for healthcare interventions using discrete choice experiments (DCEs) to (1) review the methodology to evaluate similarities, differences, rigour of designs and whether comparisons are made at the aggregate level or account for individual heterogeneity; and (2) quantify the extent to which they demonstrate concordance of patient and healthcare provider preferences. METHODS: A systematic review searching Medline, EMBASE, Econlit, PsycINFO and Web of Science for DCEs using patient and healthcare providers. INCLUSION CRITERIA: peer-reviewed; complete empiric text in English from 1995 to 31July 2015; discussing a healthcare-related topic; DCE methodology; comparing patients and healthcare providers. DESIGN: Systematic review. RESULTS: We identified 38 papers exploring 16 interventions in 26 diseases/indications. Methods to analyse results, determine concordance between patient and physician values, and explore heterogeneity varied considerably between studies. The majority of studies we reviewed found more evidence of mixed concordance and discordance (n=28) or discordance of patient and healthcare provider preferences (n=12) than of concordant preferences (n=4). A synthesis of concordance suggested that healthcare providers rank structure and outcome attributes more highly than patients, while patients rank process attributes more highly than healthcare providers. CONCLUSIONS: Discordant patient and healthcare provider preferences for different attributes of healthcare interventions are common. Concordance varies according to whether attributes are processes, structures or outcomes, and therefore determining preference concordance should consider all aspects jointly and not a binary outcome. DCE studies provide excellent opportunities to assess value concordance between patients and providers, but assessment of concordance was limited by a lack of consistency in the approaches used and consideration of heterogeneity of preferences. Future DCEs assessing concordance should fully report the framing of the questions and investigate the heterogeneity of preferences within groups and how these compare.
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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 | 0.149 | 0.448 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.018 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".