Characterizing the Adoption and Experiences of Users of Artificial Intelligence-Generated Health Information in the United States: Cross-Sectional Questionnaire Study

被引:0
|
作者
Ayo-Ajibola, Oluwatobiloba [1 ]
Davis, Ryan J. [1 ]
Lin, Matthew E. [2 ]
Riddel, Jeffrey [3 ]
Kravitz, Richard L. [4 ]
机构
[1] Univ Southern Calif, Keck Sch Med, Los Angeles, CA USA
[2] Univ Calif Los Angeles, David Geffen Sch Med, Dept Head & Neck Surg, Los Angeles, CA USA
[3] Univ Southern Calif, Keck Sch Med, Dept Emergency Med, Los Angeles, CA USA
[4] Univ Calif Davis, Div Gen Med, 4150 V St,PSSB Suite 2400, Sacramento, CA 95817 USA
关键词
artificial intelligence; ChatGPT; health information; patient information-seeking; online health information; health literacy; ResearchMatch; users; diagnosis; decision-making; cross-sectional; survey; surveys; adoption; utilization; AI; less-educated; poor health; worse health; experience; experiences; user; non user; non users; AI-generated; implication; implications; medical practice; medical practices; public health; descriptive statistics; test; t tests; chi-square test; chi-squaretests; health-seeking behavior; health-seeking behaviors; patient-provider; interaction; interactions; patient; patients; INTERNET; ONLINE; IMPACT;
D O I
10.2196/55138
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Background OpenAI's ChatGPT is a source of advanced online health information (OHI) that may be integrated into individuals' health information-seeking routines. However, concerns have been raised about its factual accuracy and impact on health outcomes. To forecast implications for medical practice and public health, more information is needed on who uses the tool, how often, and for what. Objective This study aims to characterize the reasons for and types of ChatGPT OHI use and describe the users most likely to engage with the platform. Methods In this cross-sectional survey, patients received invitations to participate via the ResearchMatch platform, a nonprofit affiliate of the National Institutes of Health. A web-based survey measured demographic characteristics, use of ChatGPT and other sources of OHI, experience characterization, and resultant health behaviors. Descriptive statistics were used to summarize the data. Both 2-tailed t tests and Pearson chi-square tests were used to compare users of ChatGPT OHI to nonusers. Results Of 2406 respondents, 21.5% (n=517) respondents reported using ChatGPT for OHI. ChatGPT users were younger than nonusers (32.8 vs 39.1 years, P<.001) with lower advanced degree attainment (BA or higher; 49.9% vs 67%, P<.001) and greater use of transient health care (ED and urgent care; P<.001). ChatGPT users were more avid consumers of general non-ChatGPT OHI (percentage of weekly or greater OHI seeking frequency in past 6 months, 28.2% vs 22.8%, P<.001). Around 39.3% (n=206) respondents endorsed using the platform for OHI 2-3 times weekly or more, and most sought the tool to determine if a consultation was required (47.4%, n=245) or to explore alternative treatment (46.2%, n=239). Use characterization was favorable as many believed ChatGPT to be just as or more useful than other OHIs (87.7%, n=429) and their doctor (81%, n=407). About one-third of respondents requested a referral (35.6%, n=184) or changed medications (31%, n=160) based on the information received from ChatGPT. As many users reported skepticism regarding the ChatGPT output (67.9%, n=336), most turned to their physicians (67.5%, n=349). Conclusions This study underscores the significant role of AI-generated OHI in shaping health-seeking behaviors and the potential evolution of patient-provider interactions. Given the proclivity of these users to enact health behavior changes based on AI-generated content, there is an opportunity for physicians to guide ChatGPT OHI users on an informed and examined use of the technology.
引用
收藏
页数:12
相关论文
共 50 条
  • [21] How users make judgements about the quality of online health information: a cross-sectional survey study
    Pian, Wenjing
    Lin, Laibao
    Li, Baiyang
    Qin, Chunxiu
    Lin, Huizhong
    [J]. BMC PUBLIC HEALTH, 2022, 22 (01)
  • [22] How users make judgements about the quality of online health information: a cross-sectional survey study
    Wenjing Pian
    Laibao Lin
    Baiyang Li
    Chunxiu Qin
    Huizhong Lin
    [J]. BMC Public Health, 22
  • [23] A cross-sectional study of coping resources and mental health of Chinese older adults in the United States
    Guo, Man
    Steinberg, Nadia Sabbagh
    Dong, Xinqi
    Tiwari, Agnes
    [J]. AGING & MENTAL HEALTH, 2018, 22 (11) : 1448 - 1455
  • [24] Validation of the japanese version of the sarcoidosis health questionnaire: A cross-sectional study
    Kiminobu Tanizawa
    Tomohiro Handa
    Sonoko Nagai
    Toru Oga
    Takeshi Kubo
    Yutaka Ito
    Kizuku Watanabe
    Kensaku Aihara
    Kazuo Chin
    Michiaki Mishima
    Takateru Izumi
    [J]. Health and Quality of Life Outcomes, 9
  • [25] Validation of the japanese version of the sarcoidosis health questionnaire: A cross-sectional study
    Tanizawa, Kiminobu
    Handa, Tomohiro
    Nagai, Sonoko
    Oga, Toru
    Kubo, Takeshi
    Ito, Yutaka
    Watanabe, Kizuku
    Aihara, Kensaku
    Chin, Kazuo
    Mishima, Michiaki
    Izumi, Takateru
    [J]. HEALTH AND QUALITY OF LIFE OUTCOMES, 2011, 9
  • [26] Predicting and Empowering Health for Generation Z by Comparing Health Information Seeking and Digital Health Literacy: Cross-Sectional Questionnaire Study
    Jiao, Wen
    Chang, Angela
    Ho, Mary
    Lu, Qianfeng
    Liu, Matthew Tingchi
    Schulz, Peter Johannes
    [J]. JOURNAL OF MEDICAL INTERNET RESEARCH, 2023, 25
  • [27] Evaluating a Public Health Information Service According to Users' Socioeconomic Position and Health Status: Protocol for a Cross-Sectional Study
    Estevez, Megane
    Domecq, Sandrine
    Montagni, Ilaria
    Ramel, Viviane
    [J]. JMIR RESEARCH PROTOCOLS, 2023, 12
  • [28] Perceptions of Artificial Intelligence Among Otolaryngologists in Saudi Arabia: A Cross-Sectional Study
    AlSharhan, Salma S.
    AlMarzouq, Wasan F.
    Alshaikh, Hamzah K.
    Aljubran, Hussain J.
    Alghamdi, Rizam
    AlQahtani, Sarah M.
    Almarzouq, Aseel F.
    AlAmer, Naheel A.
    [J]. JOURNAL OF MULTIDISCIPLINARY HEALTHCARE, 2024, 17 : 4101 - 4111
  • [29] Radiology Residents' Perceptions of Artificial Intelligence: Nationwide Cross-Sectional Survey Study
    Chen, Yanhua
    Wu, Ziye
    Wang, Peicheng
    Xie, Linbo
    Yan, Mengsha
    Jiang, Maoqing
    Yang, Zhenghan
    Zheng, Jianjun
    Zhang, Jingfeng
    Zhu, Jiming
    [J]. JOURNAL OF MEDICAL INTERNET RESEARCH, 2023, 25
  • [30] Treatment of Alopecia Areata in the United States: A Retrospective Cross-Sectional Study
    Farhangian, Michael E.
    McMichael, Amy J.
    Huang, Karen E.
    Feldman, Steven R.
    [J]. JOURNAL OF DRUGS IN DERMATOLOGY, 2015, 14 (09) : 1012 - 1014