Challenges and Solutions for Integrating and Financing Personalized Medicine in Healthcare Systems: A Systematic Literature Review
Bibliographic record
Abstract
The scope and ambitions of biomedical institutions worldwide currently working toward the integration of personalized medicine (PM) require recognizing the potential profound impact on regulatory standards and on the economic functioning and financing of healthcare. Against this background, researchers and policymakers must manage the arising challenges for the healthcare systems. In this paper we study the literature related to the consequences of PM on health insurance and care systems. Using the PRISMA research protocol, we search the existing body of literature and analyze publications dealing with insurance (419 papers) in the field of PM. After a detailed reading of the 52 studies included in our analysis, we synthesize challenges in three fields that must be addressed to avoid hindering the implantation of PM. The key issues that we highlight concern (1) a lack of clear and consistent data on the economic relevance of PM, (2) a value-oriented and cost-efficient definition of reimbursement thresholds, (3) the implementation of PM in the prevailing healthcare system. In the meantime, we provide several solutions to these concerns; we present (a) risk-sharing contracts that can deal with the emerging coverage challenges, (b) criteria that could constitute future reimbursement thresholds and (c) examples of successful implementations of PM into healthcare systems. Our findings are relevant for policymakers and health insurance companies for redefining the guidelines for the healthcare schemes of the future.
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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 | 0.027 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".