Toward bridging gaps in patient navigation: A study on the adoption of artificial intelligence technologies
Open accessAbstract
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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Nurse navigators frequently lack formal recognition of their specialized skillset.
“However, navigators frequently lack institutional support, sustainable funding, and formal recognition of their specialized skillset.2”
From Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States of America (2026) · cites this paper as [2]
Section 1.5 describes patient navigators and their programs as “under supported,” “underfunded,” and “undervalued.” That supports the general concern about recognition, but it does not specifically establish that nurse navigators frequently lack formal recognition of their specialized skillset. (LLM, conf=0.85)
Experimental — accuracy not yet measuredNurse navigators frequently lack sustainable funding.
“However, navigators frequently lack institutional support, sustainable funding, and formal recognition of their specialized skillset.2”
From Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States of America (2026) · cites this paper as [2]
Section 1.5 (page unavailable) says patient navigators and their programs are underfunded, supporting the funding concern. It does not establish that nurse navigators specifically frequently lack sustainable funding; the Results section describes a broader mix of navigation professionals. (LLM, conf=0.84)
Experimental — accuracy not yet measuredNurse navigators frequently lack institutional support.
“However, navigators frequently lack institutional support, sustainable funding, and formal recognition of their specialized skillset.2”
From Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States of America (2026) · cites this paper as [2]
Section 1.5 says patient navigators and their programs are under-supported, underfunded, and undervalued. The Discussion identifies a lack of institutional support as a possible reason for limited technology use. Neither passage establishes how frequently nurse navigators specifically lack institutional support, nor does it address sustainable funding or formal recognition of their specialized skills. (LLM, conf=0.82)
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Cited by
Sorted by citations
Girdwood, T., Kheirinejad, S., Kheirkhah, P., White, B., Davis, R., Schwartz, D., & Shaban-Nejad, A. (2026) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2026.100694 · Cited by 3 · see citing works
- Authors
- Chen, F., Lan, T., Liang, J., & Zhang, R.
- Affiliations
- COVID‐19 Research Consortium The Hopkins Club for Innovation and Entrepreneurship INC Baltimore Maryland USA
- New York University (US)
- Simon Fraser University (CA)
- LINKSciences LLC Baltimore Maryland USA
- Corresponding author
- Chen, Fenghao
- Venue
- Medicine Advances
- Publisher
- Wiley
- Published
- 2024-09
- Volume
- 2
- Issue
- 3
- Pages
- 274-283
- ISSN
- 2834-4391 (ISSN-L)
- 2834-4405
- OpenAlex
- W4402457958
- Cited by
- 8
- References
- 0
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 1
- FWCI
- 0.76
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Medicine Advances (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › General Health Professions › Nursing Roles and Practices
- Health Sciences › Medicine › Oncology › Cancer survivorship and care
- Keywords
- patient navigation, artificial intelligence
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