Transforming healthcare delivery: next-generation medication management in smart hospitals through IoMT and ML

Author:

Rawas Soha

Abstract

AbstractThe management of medications is a crucial component of healthcare, and pharmaceutical errors can have detrimental effects on patients, healthcare professionals, and healthcare systems. By utilizing patient-specific data and cutting-edge technology like the Internet of Medical Things (IoMT) and machine learning, customized drug management systems have the potential to increase patient safety and healthcare effectiveness. In this study, we reviewed a large body of literature on the subject of medication management in healthcare and the potential advantages of personalized medication management. We then assessed how IoMT and machine learning might be used to enhance medication management in smart hospitals. Then, we created a framework for assessing how personalized medication management utilizing IoMT and machine learning affects patient safety and healthcare effectiveness. Our study's findings demonstrate that in smart hospitals, tailored medication management with IoMT and machine learning can drastically lower medication errors while also enhancing patient safety and healthcare effectiveness. Our findings have important ramifications for the future of medication administration in smart hospitals, and we advise healthcare professionals and policymakers to give priority to integrating cutting-edge technology like IoMT and machine learning for customized medication management.

Publisher

Springer Science and Business Media LLC

Reference18 articles.

1. Brown C Jr, Waheed S. Benefits of healthcare technology management input in medical product safety network reports case review. J Clin Eng. 2023;48(1):36–8.

2. Kordel P, et al. Analysis of 10 years of medical errors investigated by the Poznan University forensic medicine department. F1000Research. 2023;12:46.

3. Sandén M. Implementation of a conceptual computational model to estimate the delay time in drug delivery to reduce medication errors in pediatric emergency care. 2023.

4. Dhaini I, Rawas S, El-Zaart A. An intelligent and green e-healthcare model for an early diagnosis of medical images as an IoMT application. In: Machado JM, Chamoso P, Hernández G, Bocewicz G, Loukanova R, Jove E, del Martin Rey A, Ricca M, editors. Distributed computing and artificial intelligence, special sessions, 19th international conference. Cham: Springer International Publishing; 2023.

5. Fathallah Mostafa M, IbrahemAboseada A, Sayed S E. Medication administration errors and barriers to reporting: critical care nurses’ point of view. Int Egypt J Nurs Sci Res. 2023;3(2):103–21.

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

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