1. Identify current literature regarding the usage of generative artificial intelligence for healthcare administrative tasks.
2. Assess the current opportunities and uses of generative artificial intelligence in the assistance of providers in healthcare administrative tasks.
3. Describe the challenges and limitations of current generative artificial intelligence for healthcare administrative tasks.
Importance: Administrative burden in healthcare is a contributor to provider burnout and to an estimated $276 billion in potentially avoidable costs. Generative artificial intelligence (GenAI), which provides human-like responses to user query, has demonstrated usefulness in addressing administrative tasks in a variety of industries; however, its use in this capacity for healthcare is unknown. Objective: Identify current uses, opportunities, and challenges of the utilization of GenaI in healthcare administration Evidence Review: Reviewers examined peer reviewed studies from January 2019 to February 2024 and published in English, which identified an administrative need in healthcare and used GenAI tools to solve it. The databases searched included PubMed, Embase, Arxiv, ACM Digital Library, and IEEE Xplore. Letters, opinion pieces, case reports, and previous systematic and scoping reviews were excluded, as were studies using genAI in any fashion that was not intended to solve administrative burden. The review was conducted in accordance with PRISMA guidelines. Findings: Of 1023 identified abstracts, 9 studies met all criteria for inclusion. These included 2 studies assessing provider perception, 4 addressing document generation, 3 addressing informed consent, one for inbox management, and one for data transformation to embed and retrieve ICD-10 codes in electronic health records (EHR). Study results for speed and accuracy of GenAI vs. human-generated documentation were mixed. Perception of GenAI was largely favorable, but common concerns included data privacy and security, AI-induced fabrication, and error rates. The greatest opportunity to relieve burden likely occurs with coding, which also poses the greatest challenges. Individual studies in this analysis did not directly address savings in cost or time. Conclusions and Relevance: GenAI shows potential to improve administrative burden in healthcare; however, few studies are published, and their results are mixed. As GenAI applications evolve, further research is needed to expand the understanding of the opportunities and challenges in using GenAI for this area.