The use of real-world evidence among healthcare payers: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Real-world evidence (RWE) is increasingly used to assess and make regulatory decisions on health technologies. However, its application in healthcare payer decision-making is less well-known. OBJECTIVES: The objectives of this study were to (i) review the recent literature on how RWE has been used by healthcare payers, (ii) highlight barriers that limit the use of RWE in payer decision making, and (iii) explore how RWE has been used in various funding arrangements between payers and manufacturers. The benefits of utilizing RWE are also discussed. METHODS: A scoping review was conducted on articles published between 2014 and 2025 in PubMed (Medline), OVID EMBASE, Cochrane Library, and ProQuest Dissertations and Theses Global. Eligible articles were those written in English that discussed the use of real-world evidence among healthcare payers/decision-makers for health technology reimbursement decisions. RESULTS: Nineteen articles were selected for full-text review based on the inclusion criteria. The review highlighted payers' interest in incorporating RWE into funding and reimbursement decisions to address uncertainty in the performance of new health technologies. However, a lack of standards for collecting, analyzing, and reporting RWE limits its use. Little is known about how RWE is used in reimbursement decisions since contractual arrangements between payers and manufacturers are confidential. CONCLUSIONS: Although payers are interested in using RWE to inform funding and reimbursement decisions, there are concerns regarding the scientific rigor used to generate such evidence. Having more insight into the contractual arrangements between payers and manufacturers would help to better understand how RWE informs these agreements.
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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 | 0.145 | 0.528 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.041 | 0.030 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".