An approach to fuzzy process mining to reduce patient waiting time in a hospital

Ganesha, K.; Dhanush, S; Raj, Shilpa

doi:10.1109/iciiecs.2017.8275889

Cited by 8 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the context of healthcare, the work on conformance analysis includes paper focusing on comparing the actual process model with the designed and prescriptive process model (e.g., clinical guidelines, models hand‐made by medical experts, etc.) (Anggrainingsih et al, 2018; Asare et al, 2020; Caron et al, 2014; de Vries et al, 2017; Fernández‐Llatas, Meneu, et al, 2011; Ganesha et al, 2017; Jaturogpattana et al, 2017; Kelleher et al, 2014; Kim et al, 2013; Kirchner et al, 2012; Kukreja & Batra, 2017; Mannhardt & Blinde, 2017; Neira et al, 2019; Placidi et al, 2021; Rinner et al, 2018; Rovani et al, 2015; H. Xu, Pang, Yang, Jinghui, et al, 2020; H. Xu, Pang, Yang, Ma, et al, 2020; H. Xu, Yan, Pang, Nan, et al, 2020; S. Yang, Li, et al, 2017; S. Yang, Sarcevic, et al, 2018). In these works, the results of conformance analysis are the detection and fully understanding where clinical practice deviates from the specifications in a care pathway, which leads to higher levels of quality care and patient outcomes.…”

Section: Literature Discussionmentioning

confidence: 99%

Section: Conformance Analysismentioning

confidence: 99%

See 1 more Smart Citation

Process mining applications in the healthcare domain: A comprehensive review

Guzzo

Rullo

Vocaturo

2021

WIREs Data Min & Knowl

View full text Add to dashboard Cite

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.

show abstract

Section: Literature Discussionmentioning

confidence: 99%

Section: Conformance Analysismentioning

confidence: 99%

Process mining applications in the healthcare domain: A comprehensive review

Guzzo

Rullo

Vocaturo

2021

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

“…Besides process discovery, the performance perspective is commonly discussed when analyzing outpatient visit logs. In existing studies, wait times are frequently analyzed [40,41]. For conformance checking, discovered process models are evaluated by the matching rate [42] or by an adherence measurement algorithm [43], which measures the derived model's degree of conformance to the reference model [37,40,42].…”

Section: Plos Onementioning

confidence: 99%

Exploring the potential of OMOP common data model for process mining in healthcare

et al. 2023

View full text Add to dashboard Cite

Background and objective Recently, Electronic Health Records (EHR) are increasingly being converted to Common Data Models (CDMs), a database schema designed to provide standardized vocabularies to facilitate collaborative observational research. To date, however, rare attempts exist to leverage CDM data for healthcare process mining, a technique to derive process-related knowledge (e.g., process model) from event logs. This paper presents a method to extract, construct, and analyze event logs from the Observational Medical Outcomes Partnership (OMOP) CDM for process mining and demonstrates CDM-based healthcare process mining with several real-life study cases while answering frequently posed questions in process mining, in the CDM environment. Methods We propose a method to extract, construct, and analyze event logs from the OMOP CDM for process types including inpatient, outpatient, emergency room processes, and patient journey. Using the proposed method, we extract the retrospective data of several surgical procedure cases (i.e., Total Laparoscopic Hysterectomy (TLH), Total Hip Replacement (THR), Coronary Bypass (CB), Transcatheter Aortic Valve Implantation (TAVI), Pancreaticoduodenectomy (PD)) from the CDM of a Korean tertiary hospital. Patient data are extracted for each of the operations and analyzed using several process mining techniques. Results Using process mining, the clinical pathways, outpatient process models, emergency room process models, and patient journeys are demonstrated using the extracted logs. The result shows CDM’s usability as a novel and valuable data source for healthcare process analysis, yet with a few considerations. We found that CDM should be complemented by different internal and external data sources to address the administrative and operational aspects of healthcare processes, particularly for outpatient and ER process analyses. Conclusion To the best of our knowledge, we are the first to exploit CDM for healthcare process mining. Specifically, we provide a step-by-step guidance by demonstrating process analysis from locating relevant CDM tables to visualizing results using process mining tools. The proposed method can be widely applicable across different institutions. This work can contribute to bringing a process mining perspective to the existing CDM users in the changing Hospital Information Systems (HIS) environment and also to facilitating CDM-based studies in the process mining research community.

show abstract

“…They suggest that process mining tools can be used to uncover process deviations to come up with solutions which increase patient satisfactions. For example, Ganesha et al 51 . analysed the event logs for CT tests using the fuzzy miner algorithm to evaluate patient waiting times and suggest that the model can be used to decrease waiting times.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of patient transport service in hospitals using process mining methods: Patients' perspective

Kropp

Faeghi

Lennerts

2022

Health Planning & Management

View full text Add to dashboard Cite

Designing healthcare facilities and their processes is a complex task which influences the quality and efficiency of healthcare services. The ongoing demand for healthcare services and cost burdens necessitate the application of analytical methods to enhance the overall service efficiency in hospitals. However, the variability in healthcare processes makes it highly complicated to accomplish this aim. This study addresses the complexity in the patient transport service process at a German hospital, and proposes a method based on process mining to obtain a holistic approach to recognise bottlenecks and main reasons for delays and resulting high costs associated with idle resources. To this aim, the event log data from the patient transport software system is collected and processed to discover the sequences and the timeline of the activities for the different cases of the transport process. The comparison between the actual and planned processes from the data set of the year 2020 shows that, for example, around 36% of the cases were 10 or more minutes delayed. To find delay issues in the process flow and their root causes the data traces of certain routes are intensively assessed.

show abstract

An approach to fuzzy process mining to reduce patient waiting time in a hospital

Cited by 8 publications

References 10 publications

Process mining applications in the healthcare domain: A comprehensive review

Process mining applications in the healthcare domain: A comprehensive review

Exploring the potential of OMOP common data model for process mining in healthcare

Evaluation of patient transport service in hospitals using process mining methods: Patients' perspective

Contact Info

Product

Resources

About