Process mining applications in the healthcare domain: A comprehensive review

IF 6.4 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
A. Guzzo, Antonino Rullo, E. Vocaturo
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引用次数: 17

Abstract

Process mining (PM) is a well‐known research area that includes techniques, methodologies, and tools for analyzing processes in a variety of application domains. In the case of healthcare, processes are characterized by high variability in terms of activities, duration, and involved resources (e.g., physicians, nurses, administrators, machineries, etc.). Besides, the multitude of diseases that the patients housed in healthcare facilities suffer from makes medical contexts highly heterogeneous. As a result, understanding and analyzing healthcare processes are certainly not trivial tasks, and administrators and doctors look for tools and methods that can concretely support them in improving the healthcare services they are involved in. In this context, PM has been increasingly used for a wide range of applications as reported in some recent reviews. However, these reviews mainly focus on discussion on applications related to the clinical pathways, while a systematic review of all possible applications is absent. In this article, we selected 172 papers published in the last 10 years, that present applications of PM in the healthcare domain. The objective of this study is to help and guide researchers interested in the medical field to understand the main PM applications in the healthcare, but also to suggest new ways to develop promising and not yet fully investigated applications. Moreover, our study could be of interest for practitioners who are considering applications of PM, who can identify and choose PM algorithms, techniques, tools, methodologies, and approaches, toward what have been the experiences of success.
流程挖掘在医疗保健领域的应用:全面回顾
过程挖掘(PM)是一个众所周知的研究领域,包括分析各种应用领域中的过程的技术、方法和工具。在医疗保健方面,流程的特点是在活动、持续时间和涉及的资源(例如,医生、护士、管理员、机器等)方面具有高度可变性。此外,住在医疗机构的病人患有多种疾病,这使得医疗环境高度多样化。因此,理解和分析医疗保健流程当然不是一项简单的任务,管理人员和医生会寻找能够具体支持他们改善所参与的医疗保健服务的工具和方法。在这种情况下,正如最近的一些评论所报道的那样,PM已经越来越多地用于广泛的应用程序。然而,这些综述主要集中在与临床途径相关的应用讨论上,而缺乏对所有可能应用的系统综述。在本文中,我们选择了过去10年中发表的172篇论文,这些论文介绍了PM在医疗保健领域的应用。本研究的目的是帮助和指导对医学领域感兴趣的研究人员了解医疗保健中的主要PM应用,同时也提出了开发有前途但尚未完全研究的应用的新方法。此外,我们的研究可能对正在考虑项目管理应用的实践者有兴趣,他们可以识别和选择项目管理算法、技术、工具、方法论和方法,以获得成功的经验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
22.70
自引率
2.60%
发文量
39
审稿时长
>12 weeks
期刊介绍: The goals of Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (WIREs DMKD) are multifaceted. Firstly, the journal aims to provide a comprehensive overview of the current state of data mining and knowledge discovery by featuring ongoing reviews authored by leading researchers. Secondly, it seeks to highlight the interdisciplinary nature of the field by presenting articles from diverse perspectives, covering various application areas such as technology, business, healthcare, education, government, society, and culture. Thirdly, WIREs DMKD endeavors to keep pace with the rapid advancements in data mining and knowledge discovery through regular content updates. Lastly, the journal strives to promote active engagement in the field by presenting its accomplishments and challenges in an accessible manner to a broad audience. The content of WIREs DMKD is intended to benefit upper-level undergraduate and postgraduate students, teaching and research professors in academic programs, as well as scientists and research managers in industry.
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