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The Invisible Workforce: An Algorithmic Audit of Nursing, Primary Care, and Holistic Gaps in AI-Generated Survivorship Plans.

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Τίτλος: The Invisible Workforce: An Algorithmic Audit of Nursing, Primary Care, and Holistic Gaps in AI-Generated Survivorship Plans.
Συγγραφείς: İnan M; Department of Family Medicine, University of Health Sciences, Ankara Etlik City Hospital, Ankara, Türkiye. Electronic address: muhammedinan.md@gmail.com., Akar İnan S; Division of Gynecologic Oncology, University of Health Sciences, Ankara Bilkent City Hospital, Ankara, Türkiye., Bumin G; Department of Occupational Therapy, Hacettepe University, Faculty of Health Sciences, Ankara, Türkiye., Aypak C; Department of Family Medicine, University of Health Sciences, Ankara Etlik City Hospital, Ankara, Türkiye.
Πηγή: Seminars in oncology nursing [Semin Oncol Nurs] 2026 Oct; Vol. 42 (5), pp. 152304. Date of Electronic Publication: 2026 Aug 17.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Elsevier, Inc Country of Publication: United States NLM ID: 8504688 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1878-3449 (Electronic) Linking ISSN: 07492081 NLM ISO Abbreviation: Semin Oncol Nurs Subsets: MEDLINE
Imprint Name(s): Publication: Philadelphia, PA : Elsevier, Inc.
Original Publication: [Orlando, FL] : Grune & Stratton, [c1985-
Ιατρικοί όροι (MeSH): Primary Health Care*/organization & administration , Ovarian Neoplasms*/nursing , Oncology Nursing*/standards , Survivorship* , Artificial Intelligence* , Patient Care Planning*, Humans ; Female ; Middle Aged ; Large Language Models ; Algorithms ; Generative Artificial Intelligence
Περίληψη: Objectives: Advanced ovarian cancer survivorship requires multidisciplinary coordination. As patients use large language models (LLMs) as clinical navigators, essential professional roles may be inconsistently represented. This study evaluates the comprehensiveness, professional attribution, and readability of LLM-generated survivorship plans.
Methods: An algorithmic audit (N = 50) queried ChatGPT-5.1 using a standardized persona: a 58-year-old advanced ovarian cancer survivor. A dual-layer content analysis assessed nine guideline-derived domains, distinguishing explicit professional attribution from implicit functional guidance. A Nursing Function Layer quantified implicit nursing content. Readability was measured via the Flesch-Kincaid Grade Level.
Results: Medical Oncology achieved 100% explicit attribution. Conversely, Primary Care was explicitly referenced in 2% (implicit: 14%), Genetic Counseling in 0%, and Nursing in 14% (implicit: 80%), revealing a 66-point attribution gap. Despite low explicit professional attribution, nursing-relevant functional content appeared in 96% of responses (mean = 4.16 functions/response). Responses averaged an 11.57 Flesch-Kincaid Grade Level, substantially exceeding the sixth- to eighth-grade literacy benchmark.
Conclusions: LLMs demonstrate high biomedical content coverage but systematically underrepresent holistic care professionals. The functional presence of nursing content without professional visibility highlights algorithmic bias. Unsupervised LLM use risks fragmented survivorship guidance, necessitating "nurse-in-the-loop" validation workflows to correct attribution gaps and calibrate literacy levels.
Implications for Nursing Practice: AI-generated survivorship plans reflect a purely biomedical focus, systematically omitting primary care integration (2%) and explicit nursing roles (14%). This structural fragmentation necessitates "nurse-in-the-loop" workflows, positioning oncology nurses to actively review AI outputs, activate neglected primary care referrals for comorbidity management, and calibrate reading difficulty to safe patient literacy levels.
(Copyright © 2026 Elsevier Inc. All rights reserved.)
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Contributed Indexing: Keywords: Artificial intelligence; Cancer survivorship; Health literacy; Large language models; Nursing role; Ovarian neoplasms
Entry Date(s): Date Created: 20260817 Date Completed: 20260909 Latest Revision: 20260909
Update Code: 20260910
DOI: 10.1016/j.soncn.2026.152304
PMID: 42608279
Βάση Δεδομένων: MEDLINE
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  Data: <searchLink fieldCode="AU" term="%22İnan+M%22">İnan M</searchLink>; Department of Family Medicine, University of Health Sciences, Ankara Etlik City Hospital, Ankara, Türkiye. Electronic address: muhammedinan.md@gmail.com.<br /><searchLink fieldCode="AU" term="%22Akar+İnan+S%22">Akar İnan S</searchLink>; Division of Gynecologic Oncology, University of Health Sciences, Ankara Bilkent City Hospital, Ankara, Türkiye.<br /><searchLink fieldCode="AU" term="%22Bumin+G%22">Bumin G</searchLink>; Department of Occupational Therapy, Hacettepe University, Faculty of Health Sciences, Ankara, Türkiye.<br /><searchLink fieldCode="AU" term="%22Aypak+C%22">Aypak C</searchLink>; Department of Family Medicine, University of Health Sciences, Ankara Etlik City Hospital, Ankara, Türkiye.
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  Data: Objectives: Advanced ovarian cancer survivorship requires multidisciplinary coordination. As patients use large language models (LLMs) as clinical navigators, essential professional roles may be inconsistently represented. This study evaluates the comprehensiveness, professional attribution, and readability of LLM-generated survivorship plans.<br />Methods: An algorithmic audit (N = 50) queried ChatGPT-5.1 using a standardized persona: a 58-year-old advanced ovarian cancer survivor. A dual-layer content analysis assessed nine guideline-derived domains, distinguishing explicit professional attribution from implicit functional guidance. A Nursing Function Layer quantified implicit nursing content. Readability was measured via the Flesch-Kincaid Grade Level.<br />Results: Medical Oncology achieved 100% explicit attribution. Conversely, Primary Care was explicitly referenced in 2% (implicit: 14%), Genetic Counseling in 0%, and Nursing in 14% (implicit: 80%), revealing a 66-point attribution gap. Despite low explicit professional attribution, nursing-relevant functional content appeared in 96% of responses (mean = 4.16 functions/response). Responses averaged an 11.57 Flesch-Kincaid Grade Level, substantially exceeding the sixth- to eighth-grade literacy benchmark.<br />Conclusions: LLMs demonstrate high biomedical content coverage but systematically underrepresent holistic care professionals. The functional presence of nursing content without professional visibility highlights algorithmic bias. Unsupervised LLM use risks fragmented survivorship guidance, necessitating "nurse-in-the-loop" validation workflows to correct attribution gaps and calibrate literacy levels.<br />Implications for Nursing Practice: AI-generated survivorship plans reflect a purely biomedical focus, systematically omitting primary care integration (2%) and explicit nursing roles (14%). This structural fragmentation necessitates "nurse-in-the-loop" workflows, positioning oncology nurses to actively review AI outputs, activate neglected primary care referrals for comorbidity management, and calibrate reading difficulty to safe patient literacy levels.<br /> (Copyright © 2026 Elsevier Inc. All rights reserved.)
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