Optimization of Healthcare Process Management Using Machine Learning

被引：1

作者：

Avgoustis, Andreas ^{[1
]}

Exarchos, Themis ^{[1
]}

Vrahatis, Aristidis G. ^{[1
]}

Vlamos, Panagiotis ^{[1
]}

机构：

[1] Ionian Univ, Dept Informat, Bioinformat & Human Electrophysiol Lab, Corfu 49100, Greece

来源：

ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, PT I, AIAI 2024 | 2024年 / 711卷

关键词：

Healthcare management; Strategic planning; Resource management; Regulatory compliance; Machine learning; Patient care outcomes;

D O I：

10.1007/978-3-031-63211-2_15

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Healthcare management plays a crucial role in ensuring the efficient delivery of healthcare services. This responsibility encompasses strategic planning, resource management, and regulatory compliance to enhance patient care outcomes. In this paper, we delve into the multifaceted nature of healthcare management, highlighting the expertise required to optimize processes within dynamic healthcare environments. Furthermore, we explore the potential of machine learning in addressing operational challenges within healthcare. By examining various machine learning algorithms, we identify their advantages and limitations, proposing a structured method of application. Through this analysis, we aim to illuminate how machine learning can minimize patient waiting times and optimize overall healthcare operations. This inspection aims to elucidate how machine learning methodologies can mitigate patient wait times and refine overall healthcare logistics. By amalgamating cutting-edge technologies with strategic methodologies, healthcare entities can leverage the transformative capabilities of machine learning to enhance operational efficiency and elevate the delivery of patient care.

引用

页码：187 / 200

页数：14

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