Adoption of AI and its Use Cases in Urban and Rural Hospitals

IF 3.2 3区医学 Q2 HEALTH CARE SCIENCES & SERVICES

Journal of Rural Health Pub Date : 2026-09-28 DOI:10.1111/jrh.70224

Nicole M. Summers-Gabr

{"title":"Adoption of AI and its Use Cases in Urban and Rural Hospitals","authors":"Nicole M. Summers-Gabr","doi":"10.1111/jrh.70224","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Purpose</h3>\n \n <p>AI can support hospitals’ business workflow as well as their patient care. This study investigates whether rural hospitals' AI adoption is influenced by their proximity to metro areas, which often offer better infrastructure, skilled labor, and other resources, with particular attention to differences across individual clinical and business operation use cases.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>Using the 2024 American Hospital Association's Annual Survey (<i>n</i> = 6173 hospitals), the likelihood of AI adoption for eight clinical and six business uses by geography was explored. Hospital ownership type and size were controlled. Geography was determined at the county level and used the Rural–Urban Continuum Code (RUCC), with metropolitan hospitals as 1–3, non-metro or rural as 4–9, and then non-metro and rural were further divided by metro adjacency.</p>\n </section>\n \n <section>\n \n <h3> Findings</h3>\n \n <p>Across the country, almost 30% of hospitals adopted AI for clinical tasks and 26% adopted AI for administrative tasks. However, the farther a hospital was from a metro area, the less frequently it adopted AI. Compared to metro hospitals, non-metro metro-adjacent hospitals were 29.6% less likely (odds ratio [OR] = 0.70, confidence interval [CI]: 0.53–0.93, <i>p</i> < 0.05) and non-metro not-adjacent hospitals were 61.7% (OR = 0.38, CI: 0.26–0.52, <i>p</i> < .001) less likely to use AI for clinical purposes. As for administrative uses, compared to metro hospitals, non-metro metro-adjacent hospitals were 48.9% (OR = 0.51, CI: 0.40–0.66, <i>p</i> < 0.001) less likely and non-metro not-adjacent hospitals were 70.4% (OR = 0.30, CI: 0.22–0.39, <i>p</i> < 0.001) less likely to use AI. This same pattern persisted for most individual-use cases.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>AI adoption remains limited in hospitals, particularly in non-metro areas. The impact on adoption rate differences is unclear, but it is an important consideration moving forward to reduce urban–rural health disparities and improve business operations.</p>\n </section>\n </div>","PeriodicalId":50060,"journal":{"name":"Journal of Rural Health","volume":"42 4","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Rural Health","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jrh.70224","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}

引用次数: 0

Abstract

Purpose

AI can support hospitals’ business workflow as well as their patient care. This study investigates whether rural hospitals' AI adoption is influenced by their proximity to metro areas, which often offer better infrastructure, skilled labor, and other resources, with particular attention to differences across individual clinical and business operation use cases.

Methods

Using the 2024 American Hospital Association's Annual Survey (n = 6173 hospitals), the likelihood of AI adoption for eight clinical and six business uses by geography was explored. Hospital ownership type and size were controlled. Geography was determined at the county level and used the Rural–Urban Continuum Code (RUCC), with metropolitan hospitals as 1–3, non-metro or rural as 4–9, and then non-metro and rural were further divided by metro adjacency.

Findings

Across the country, almost 30% of hospitals adopted AI for clinical tasks and 26% adopted AI for administrative tasks. However, the farther a hospital was from a metro area, the less frequently it adopted AI. Compared to metro hospitals, non-metro metro-adjacent hospitals were 29.6% less likely (odds ratio [OR] = 0.70, confidence interval [CI]: 0.53–0.93, p < 0.05) and non-metro not-adjacent hospitals were 61.7% (OR = 0.38, CI: 0.26–0.52, p < .001) less likely to use AI for clinical purposes. As for administrative uses, compared to metro hospitals, non-metro metro-adjacent hospitals were 48.9% (OR = 0.51, CI: 0.40–0.66, p < 0.001) less likely and non-metro not-adjacent hospitals were 70.4% (OR = 0.30, CI: 0.22–0.39, p < 0.001) less likely to use AI. This same pattern persisted for most individual-use cases.

Conclusions

AI adoption remains limited in hospitals, particularly in non-metro areas. The impact on adoption rate differences is unclear, but it is an important consideration moving forward to reduce urban–rural health disparities and improve business operations.

查看原文本刊更多论文

城乡医院采用人工智能及其用例

目的：人工智能可以支持医院的业务工作流程以及患者护理。本研究调查了农村医院的人工智能采用是否受到其靠近大都市地区的影响，这些地区通常提供更好的基础设施、熟练劳动力和其他资源，并特别关注个体临床和业务操作用例之间的差异。方法：利用2024年美国医院协会年度调查（n = 6173家医院），按地理位置探讨人工智能在8个临床和6个商业用途中采用的可能性。控制医院所有制类型和规模。在县一级确定地理位置，使用城乡连续代码（RUCC），将大都市医院分为1-3家，非地铁或农村医院为4-9家，然后根据地铁邻近程度进一步划分非地铁和农村医院。调查结果：在全国范围内，近30%的医院采用人工智能完成临床任务，26%的医院采用人工智能完成行政任务。然而，离市区越远的医院，采用人工智能的频率就越低。与地铁医院相比，非地铁邻近医院的可能性低29.6%（优势比[OR] = 0.70，置信区间[CI]: 0.53-0.93, p < 0.05），非地铁非邻近医院的可能性低61.7% (OR = 0.38, CI: 0.26-0.52， p结论：医院采用人工智能仍然有限，特别是在非地铁地区。对采用率差异的影响尚不清楚，但这是今后减少城乡健康差距和改善业务运作的一个重要考虑因素。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Rural Health 医学-公共卫生、环境卫生与职业卫生

CiteScore

7.60

自引率

6.10%

发文量

审稿时长

>12 weeks

期刊介绍： The Journal of Rural Health, a quarterly journal published by the NRHA, offers a variety of original research relevant and important to rural health. Some examples include evaluations, case studies, and analyses related to health status and behavior, as well as to health work force, policy and access issues. Quantitative, qualitative and mixed methods studies are welcome. Highest priority is given to manuscripts that reflect scholarly quality, demonstrate methodological rigor, and emphasize practical implications. The journal also publishes articles with an international rural health perspective, commentaries, book reviews and letters.