Barriers and facilitators to next-generation sequencing use in United States oncology settings: a systematic review
Bibliographic record
Abstract
Aim: Next-generation sequencing (NGS) of solid tumors can inform treatment decisions; however, uptake remains low. This objective of this systematic review was to identify barriers to and facilitators of NGS in US oncology settings. Materials & methods: Embase and MEDLINE were searched in March 2023 for articles published from 2012 to 2023 on barriers and facilitators of NGS adoption for solid tumors. Surveys, interviews and observational studies were eligible. Studies on genetic testing for hereditary cancers and non-US studies were excluded. The Motheral scale, Joanna Briggs Institute critical appraisal checklist and McGill Mixed Methods Appraisal Tool were used to assess study quality. Data were synthesized narratively. Results: Twenty-one studies were included. Study participants were clinicians, payers and administrators. Key barriers included complex reimbursement processes and uncertainties around clinical utility. Including recommendations for NGS in clinical practice guidelines was a key facilitator, although insurance policies were often more restrictive than guideline recommendations. Conclusion: Uptake of NGS is increasing but barriers remain. Changes to the current reimbursement frameworks are needed to increase access to NGS. The impact of implementing the 2018 National Coverage Determination, which allows access to NGS for all Medicare beneficiaries with advanced cancer, is not yet evident in the published literature. Next-generation sequencing (NGS) is used in oncology to detect gene mutations such as NTRK, ALK, MET, ROS1 and ALK in solid tumors and thereby inform treatment decisions. Actionable mutations identified by NGS can be treated with tumor-agnostic targeted therapies, such as larotrectinib and entrectinib. We conducted a systematic review to investigate the reasons underlying the documented under-use of NGS testing for predictive biomarkers in oncology in the United States. The 21 studies included in the review represent viewpoints from clinicians, lab personnel, commercial payers and patients with advanced cancer. Barriers to NGS testing included complex reimbursement processes, uncertainties around clinical utility, lack of staff training and knowledge and inadequate infrastructure. Facilitators were reported less frequently than barriers, but decision support tools for clinicians and the inclusion of NGS testing in clinical practice guidelines appear to aid the uptake of NGS testing. Despite clinical guidelines' preference for NGS testing, commercial insurers often do not offer coverage that is commensurate with the guidelines. Opportunities for increased adoption of NGS testing should be further explored by payers, clinicians and healthcare system administrators by, for example, revising the current payer framework and investing in staff resources and training. Future studies should investigate the impact of Medicare's National Coverage Determination for NGS testing, which came into effect in 2018 and covers NGS for patients with advanced cancer.
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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 | 0.036 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".