Paving the road to personalized medicine: recommendations on regulatory, intellectual property and reimbursement challenges
Bibliographic record
Abstract
Personalized medicine (PM) aims to harness a wave of 'omics' discoveries to facilitate research and discovery of targeted diagnostics and therapies and increase the efficiency of healthcare systems by predicting and treating individual predispositions to diseases or conditions. Despite significant investment, limited progress has been made bringing PM to market. We describe the major perceived regulatory, intellectual property, and reimbursement challenges to the development, translation, adoption, and implementation of PM products into clinical care. We conducted a scoping review to identify (i) primary challenges for the development and implementation of PM identified in the academic literature; (ii) solutions proposed in the academic literature to address these challenges; and (iii) gaps that exist in that literature. We identified regulatory barriers to PM development and recommendations in 344 academic papers. Regulatory uncertainty was a cross-cutting theme that appeared in conjunction with other themes including: reimbursement; clinical trial regulation; regulation of co-development; unclear evidentiary requirements; insufficient incentives for research and development; incompatible information systems; and different regulation of different diagnostics. To fully realize the benefits of PM for healthcare systems and patients, regulatory, intellectual property, and reimbursement challenges need to be addressed in lock step with scientific advances.
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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 | 0.023 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".