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<p>Large language model (LLM) chat tools have the potential to transform healthcare workflows by improving efficiency and reducing administrative burdens. While prior research has predominantly focused on clinicians, non-clinician healthcare staff constitute the majority of the workforce, and their real-world chat tool use remains uncharacterized. This retrospective, cross-sectional study analyzed de-identified chat logs from a secure, HIPAA-compliant LLM chat tool deployed at an academic medical center over an 11-month period. Among 30,503 chat threads analyzed, 98% originated from non-clinician users across 239 roles. Usage was dominated by administrative tasks including email and document writing (53.9%), text manipulation (9.1%), and brainstorming (6.7%). A notable proportion of interactions included off-label queries unrelated to work or organizational goals, including 5.9% involving clinical decision-making. These findings highlight the need for targeted training, tailored governance policies, and refined evaluation frameworks to optimize appropriate LLM use while mitigating risks in healthcare settings.</p>

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Uses of generative AI by non-clinician staff at an academic medical center

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月2日

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  • Kameron C. Black,
  • William J. Haberkorn,
  • Stephen P. Ma,
  • Hanna Kiani,
  • Aditya Bhasin,
  • Jonathan H. Chen,
  • Nigam H. Shah,
  • Keith Morse

摘要

Large language model (LLM) chat tools have the potential to transform healthcare workflows by improving efficiency and reducing administrative burdens. While prior research has predominantly focused on clinicians, non-clinician healthcare staff constitute the majority of the workforce, and their real-world chat tool use remains uncharacterized. This retrospective, cross-sectional study analyzed de-identified chat logs from a secure, HIPAA-compliant LLM chat tool deployed at an academic medical center over an 11-month period. Among 30,503 chat threads analyzed, 98% originated from non-clinician users across 239 roles. Usage was dominated by administrative tasks including email and document writing (53.9%), text manipulation (9.1%), and brainstorming (6.7%). A notable proportion of interactions included off-label queries unrelated to work or organizational goals, including 5.9% involving clinical decision-making. These findings highlight the need for targeted training, tailored governance policies, and refined evaluation frameworks to optimize appropriate LLM use while mitigating risks in healthcare settings.