Toward bridging gaps in patient navigation: A study on the adoption of artificial intelligence technologies
Bibliographic record
Abstract
Abstract Background Patient navigators, whose value has become increasingly apparent, still face significant challenges, including a lack of support, funding, and recognition. These challenges have been exacerbated in the wake of COVID‐19 pandemic. Methods This study explored the potential use of artificial intelligence (AI) in patient navigation. Data were collected through structured surveys and individual interviews with patient navigators from a variety of institutions and professional backgrounds. The data were analyzed to understand the current state of patient navigation, identify existing gaps, and suggest best practices for the future. Results The findings showed that patient navigators (a) have diverse backgrounds and responsibilities, (b) lack technology support for their work, (c) are at risk for burnout, with the extent varying based on the level of technical support received, and (d) report significant overlap between current barriers and those that could potentially be addressed with AI‐driven technologies. Conclusion A novel intervention, that is enabled by AI and other technologies and tailored to individual needs, has the potential to reduce burnout, increase capacity, and help ensure the sustainability of patient navigation and other areas of healthcare. By addressing the specific needs of individual patients, this type of intervention could help improve the overall effectiveness of patient navigation and support the long‐term sustainability of the role.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".