AI-enabled fraud detection, prevention, and perpetration in nursing credential evaluation: A scoping study

IF 6.3 4区医学 Q1 NURSING

Journal of Nursing Regulation Pub Date : 2025-10-01 DOI:10.1016/j.jnr.2025.08.008

Lauren Herckis PhD , Emily Tse MPhil

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引用次数: 0

Abstract

Background

Credential fraud among healthcare professionals is a global, significant, and ever-evolving challenge. Technological innovations, such as digital imaging and generative artificial intelligence (AI) that make it easier to fabricate documents, have changed the credential evaluation and verification landscape. A global health worker shortage compounds the critical need to maintain integrity, reliability, and rigor in credential verification of healthcare professionals.

Purpose

To identify evidence-based best practices for combatting nursing credential fraud in the context of AI.

Methods

This research effort entailed a scoping review following Arskey and O'Malley's methodological framework to identify scholarly research related to AI and nursing credential fraud. After the scoping review, an environmental scan of grey literature and professional guidance was performed. Integrated analysis of the findings was used to develop themes and recommendations to guide future work.

Results

Four articles, all published between 2020 and 2025, were subjected to full-text review. Of these four articles, none directly addressed AI in perpetrating or combatting nursing credential fraud. The environmental scan revealed practices documented by professional associations and regulatory bodies as well as emerging trends. Five areas of future research are recommended based on these findings: (1) translate existing research, (2) collaborate in cross-functional teams; (3) engage in experimental software development; (4) generate evidence-based guidance; and (5) participate in ongoing evaluation processes.

Conclusions

This study found emerging practices but no empirical research or evidence-based guidance on the use of AI in combatting or perpetuating nursing credential fraud. Literature addressing employment fraud, AI and nursing regulation, and AI in credential evaluation reveal that nursing credential fraud leveraging AI tools requires urgent attention from regulators, credential evaluators, employers, and researchers.

查看原文本刊更多论文

人工智能在护理证书评估中的欺诈检测、预防和实施：一项范围研究

医疗保健专业人员的证书欺诈是一个全球性的、重大的、不断发展的挑战。技术创新，如数字成像和生成式人工智能（AI），使伪造文件变得更容易，已经改变了证书评估和验证的格局。全球卫生工作者短缺加剧了保持卫生保健专业人员证书核查的完整性、可靠性和严谨性的迫切需要。目的确定人工智能背景下打击护理证书欺诈的循证最佳实践。方法本研究工作需要根据Arskey和O'Malley的方法框架进行范围审查，以确定与人工智能和护理证书欺诈相关的学术研究。在范围审查之后，进行灰色文献的环境扫描和专业指导。对调查结果进行了综合分析，以制定指导今后工作的主题和建议。结果4篇发表于2020 - 2025年的论文进行了全文评审。在这四篇文章中，没有一篇直接涉及人工智能在实施或打击护理证书欺诈中的作用。环境扫描揭示了专业协会和监管机构记录的做法以及新兴趋势。基于这些发现，建议未来研究的五个领域：(1)转化现有研究；(2)跨职能团队合作；（三）从事实验软件开发；(4)生成循证指导；(5)参与持续的评价过程。本研究发现了新兴的实践，但没有关于使用人工智能打击或延续护理证书欺诈的实证研究或循证指导。关于就业欺诈、人工智能与护理监管以及人工智能在证书评估中的研究表明，利用人工智能工具的护理证书欺诈需要监管机构、证书评估人员、雇主和研究人员的紧急关注。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Nursing Regulation NURSING-

CiteScore

4.60

自引率

12.50%

发文量

审稿时长

54 days

期刊介绍： Journal of Nursing Regulation (JNR), the official journal of the National Council of State Boards of Nursing (NCSBN®), is a quarterly, peer-reviewed, academic and professional journal. It publishes scholarly articles that advance the science of nursing regulation, promote the mission and vision of NCSBN, and enhance communication and collaboration among nurse regulators, educators, practitioners, and the scientific community. The journal supports evidence-based regulation, addresses issues related to patient safety, and highlights current nursing regulatory issues, programs, and projects in both the United States and the international community. In publishing JNR, NCSBN''s goal is to develop and share knowledge related to nursing and other healthcare regulation across continents and to promote a greater awareness of regulatory issues among all nurses.