Adoption of LLM AI tools in everyday tasks: A multi-site cross-sectional qualitative study of Chinese hospital administrators (Preprint)

Journal of Medical Internet Research, Год журнала: 2025, Номер unknown

Опубликована: Янв. 2, 2025

Large Language Model (LLM) artificial intelligence (AI) tools have the potential to streamline healthcare administration by enhancing efficiency in document drafting, resource allocation, and communication tasks. Despite this potential, adoption of such among hospital administrators remains understudied, particularly at individual level. To explore factors influencing utilization LLM AI China, focusing on enablers, barriers, practical applications daily administrative A multi-center, cross-sectional, descriptive qualitative design was employed. Three tertiary hospitals located Beijing (Site 1), Shenzhen 2), Chengdu 3) were selected represent diverse geographic regions institutional profiles. Middle-level recruited using purposive sampling. Data collected from June 11 August 16, 2024 through face-to-face semi-structured interviews guided a collaboratively developed piloted interview guide. Each audio-recorded transcribed verbatim. Colaizzi's method employed for thematic analysis. saturation determined per-site basis continuously reviewing transcripts during biweekly meetings until no new themes emerged additional interviews. total 31 participants 1: 9; Site 2: 10; 3: 12) completed lasting an average 27.3 min (range: 21-39 min). Only 22.6% reported high familiarity with tools, 25.8% frequent users while 45.2% rare users. Adoption varied site. 3 had highest proportion high-familiarity who consistently used more frequently. Qualitative analysis revealed that positive early experiences prior technological expertise facilitated adoption, whereas mistrust tool accuracy, limited prompting skills, insufficient training significant barriers. Participants predominantly drafting strongly advocated structured tutorials support enhance broader utilization. Familiarity technology, experiences, openness innovation may facilitate barriers as knowledge, skills can hinder use. are now primarily basic tasks application advanced functionalities due lack confidence. Structured needed usability integration. Targeted programs, combined organizational strategies build trust improve accessibility, could rates broaden usage. Future quantitative investigations should validate rate factors.

Язык: Английский

Adoption of Large Language Model AI Tools in Everyday Tasks: Multisite Cross-Sectional Qualitative Study of Chinese Hospital Administrators (Preprint)

Опубликована: Янв. 2, 2025

Large language model (LLM) artificial intelligence (AI) tools have the potential to streamline health care administration by enhancing efficiency in document drafting, resource allocation, and communication tasks. Despite this potential, adoption of such among hospital administrators remains understudied, particularly at individual level. This study aims explore factors influencing use LLM AI China, focusing on enablers, barriers, practical applications daily administrative A multicenter, cross-sectional, descriptive qualitative design was used. Data were collected through semistructured face-to-face interviews with 31 across 3 tertiary hospitals Beijing, Shenzhen, Chengdu from June 2024 August 2024. The Colaizzi method used for thematic analysis identify patterns participants’ experiences perspectives. Adoption generally low, significant site-specific variations. Participants higher technological familiarity positive early reported more frequent use, while barriers as mistrust tool accuracy, limited prompting skills, insufficient training hindered broader adoption. Tools primarily exploration advanced functionalities. strongly emphasized need structured programs institutional support enhance usability confidence. Familiarity technology, experiences, openness innovation may facilitate adoption, knowledge, skills can hinder use. are now basic tasks application functionalities due a lack Structured tutorials needed integration. Targeted programs, combined organizational strategies build trust improve accessibility, could rates broaden Future quantitative investigations should validate rate factors.

Язык: Английский