Applications of Artificial Intelligence in Health Care Delivery

Author:

Spear Joseph,Ehrenfeld Jesse M.,Miller Brian J.

Abstract

AbstractHealth care costs now comprise nearly one-fifth of the United States’ gross domestic product, with the last 25 years marked by rising administrative costs, a lack of labor productivity growth, and rising patient and physician dissatisfaction. Policy experts have responded with a series of reforms that have – ironically - increased patient and physician administrative burden with little meaningful effect on cost and quality. Artificial intelligence (AI), a topic of great consternation, can serve as the “wheat thresher” for health care delivery, empowering and freeing both patients and physicians by decreasing administrative burden and improving labor productivity. In this Viewpoint, we discuss three principal areas where AI poses an unprecedented opportunity to reduce cost, improve care, and markedly enhance the patient and physician experience: (1) automation of administrative process, (2) augmentation of clinical practice, and (3) automation of elements of clinical practice.

Publisher

Springer Science and Business Media LLC

Subject

Health Information Management,Health Informatics,Information Systems,Medicine (miscellaneous)

Reference16 articles.

1. Private Community Hospitals Labor Productivity: U.S. Bureau of Labor Statistics. Accessed June 29, 2023. https://www.bls.gov/productivity/highlights/hospitals-labor-productivity.htm

2. Kanter GP, Polsky D, Werner RM. Changes In Physician Consolidation With The Spread Of Accountable Care Organizations. Health Aff (Millwood). 2019;38(11):1936–1943. doi:https://doi.org/10.1377/hlthaff.2018.05415

3. Comprehensive Primary Care Initiative | CMS Innovation Center. Accessed July 25, 2023. https://innovation.cms.gov/innovation-models/comprehensive-primary-care-initiative

4. Almost Two-Thirds of U.S. Doctors, Nurses Feel Burnt Out at Work: Poll. US News & World Report. Accessed October 26, 2023. https://www.usnews.com/news/health-news/articles/2023-02-23/almost-two-thirds-of-u-s-doctors-nurses-feel-burnt-out-at-work-poll

5. Chaiyachati KH, Shea JA, Asch DA, et al. Assessment of Inpatient Time Allocation Among First-Year Internal Medicine Residents Using Time-Motion Observations. JAMA Intern Med. 2019;179(6):760–767. doi:https://doi.org/10.1001/jamainternmed.2019.0095

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3