Systematic Mapping of Process Mining Studies in Healthcare
Author(s) -
Tugba Gurgen Erdogan,
Ayca Tarhan
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2831244
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
In the last decade, as an emerging technique for business processes management, process mining (PM) has been applied in many domains, including manufacturing, supply-chain, government, healthcare, and software engineering. Particularly in healthcare, where most processes are complex, variable, dynamic, and multi-disciplinary in nature, the application of this technique is growing yet challenging. Several literature reviews, as secondary studies, reveal the state of PM applications in healthcare from different perspectives, such as clinical pathways, oncology processes, and hospital management. In this article, we present the results of a systematic mapping (SM) study which we conducted to structure the information available in the primary studies. SM is a well-accepted method to identify and categorize research literature, in which the number of primary studies is rapidly growing. We searched for studies between 2005 and 2017 in the electronic digital libraries of scientific literature, and identified 172 studies out of the 2428 initially found on the topic of PM in healthcare. We created a concept map based on the information provided by the primary studies and classified these studies according to a number of attributes including the types of research and contribution, application context, healthcare specialty, mining activity, process modeling type and notation/language, and mining algorithm. We also reported the demographics and bibliometrics trends in this domain; namely, publication volume, top-cited papers, most contributing researchers and countries, and top venues. The results of mapping showed that, despite the healthcare data and technique related challenges, the field is rapidly growing and open for further research and practice. The researchers who are interested in the field could use the results to elicit opportunities for further research. The practitioners who are considering applications of PM, on the other hand, could observe the most common aims and specialties that PM techniques are applied.
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