Application Of Process Mining Techniques Research Articles

Process Mining in Sepsis Treatment Management: A Step-by-Step Approach to Discovery

This article explores the management of sepsis treatment processes, a prominent contributor to hospital morbidity and mortality on a global scale. The research delves into the application of process mining techniques during the discovery phase, utilizing anonymized data from Mannhardt and Blinde (2017). The impetus for this investigation stems from the imperative to pinpoint inefficiencies within sepsis treatment protocols, with the overarching goal of enhancing care quality while curbing expenses. A primary challenge lies in the need for a comprehensive overview of hospital processes for sepsis management. The study employs discovery algorithms such as Alpha Miner, Heuristic Mining, and DFG (Direct Flow Chart) to address this challenge. By leveraging these methodologies, the research endeavors to foster efficient allocation of costs and resources, elevate the standard of patient care, and foster operational efficacy within public health institutions.

Is it possible to automate the discovery of process maps for the time-driven activity-based costing method? A systematic review

ObjectivesThe main objective of this manuscript was to identify the methods used to create process maps for care pathways that utilized the time-driven activity-based costing method.MethodsThis is a systematic mapping review. Searches were performed in the Embase, PubMed, CINAHL, Scopus, and Web of Science electronic literature databases from 2004 to September 25, 2022. The included studies reported practical cases from healthcare institutions in all medical fields as long as the time-driven activity-based costing method was employed. We used the time-driven activity-based costing method and analyzed the created process maps and a qualitative approach to identify the main fields.ResultsA total of 412 studies were retrieved, and 70 articles were included. Most of the articles are related to the fields of orthopedics and childbirth-related to hospital surgical procedures. We also identified various studies in the field of oncology and telemedicine services. The main methods for creating the process maps were direct observational practices, complemented by the involvement of multidisciplinary teams through surveys and interviews. Only 33% of the studies used hospital documents or healthcare data records to integrate with the process maps, and in 67% of the studies, the created maps were not validated by specialists.ConclusionsThe application of process mining techniques effectively automates models generated through clinical pathways. They are applied to the time-driven activity-based costing method, making the process more agile and contributing to the visualization of high degrees of variations encountered in processes, thereby making it possible to enhance and achieve continual improvements in processes.

Transformer models for mining intents and predicting activities from emails in knowledge-intensive processes

Process mining is an interdisciplinary field that combines Artificial Intelligence and Business Process Management to extract insights from historical event data. Knowledge-intensive processes, which predominantly involve knowledge work, are often inadequately monitored by process-aware information systems. Consequently, the event data necessary for applying process mining techniques are frequently unavailable. Emails are widely used in knowledge-intensive processes for scheduling meetings, sharing documents, and reporting on the completion of outstanding tasks, which makes them suitable candidates for replacing event logs as the primary data source. In this work, we focus on the task of extracting the set of next recommended activities from incoming emails. Yet, we face two major challenges. Firstly, emails do not express process information explicitly but rather contain subtext that implies what the next best actions would be. Secondly, email data lacks domain-specific labels that would enable the use of machine learning. We overcome these limitations by utilizing an email taxonomy to represent user intents, thus bridging the gap between textual information and process semantics, as well as leveraging pre-trained transformer models applied in zero-shot and few-shot settings that require little to no labeled email data. An evaluation of our method on real-world unlabeled email communications demonstrates its effectiveness in recognizing intents and extracting activities.

Differentially private release of event logs for process mining

The applicability of process mining techniques hinges on the availability of event logs capturing the execution of a business process. In some use cases, particularly those involving customer-facing processes, these event logs may contain private information. Data protection regulations restrict the use of such event logs for analysis purposes. One way of circumventing these restrictions is to anonymize the event log to the extent that no individual can be singled out using the anonymized log. This article addresses the problem of anonymizing an event log in order to guarantee that, upon release of the anonymized log, the probability that an attacker may single out any individual represented in the original log does not increase by more than a threshold. The article proposes a differentially private release mechanism, which samples the cases in the log and adds noise to the timestamps to the extent required to achieve the above privacy guarantee. The article reports on an empirical comparison of the proposed approach against the state-of-the-art approaches using 14 real-life event logs in terms of data utility loss and computational efficiency.

Enterprise information management systems development two cases of mining for process conformance

This article investigates how Process Mining may be used to check process conformance in enterprise information system development. The concept of using Process Mining beyond pure business processes is exemplified on the bug closure processes of two open-source enterprise systems. The process models and conclusions extracted are used for drawing some generic guidelines enterprise information project managers can adopt to assess the quality of their system development processes. Process conformance checking is performed in the Bugzilla databases of two open-source enterprise information systems using Disco. The extracted models are assessed based on the documentation of the projects and are compared to the bug closure process Bugzilla team suggests being used. The findings revealed that the processes followed in the projects are very similar to the ones presented in their documentation, conforming with Bugzilla guidelines. However, several variations are revealed under specific circumstances. Potential benefits from applying Process Mining techniques for information system development management are briefly summarized. This empirical research extends previous work that has been performed in the field of Process Mining and highlights the importance and necessity of Process Mining in enterprise information system development to help standardize and improve the internal processes of an organization and to serve as a proof of compliance by external collaborators developing software for an organization. In addition, it capitalizes on the adoption of inner source development model by many companies developing enterprise software nowadays.

Identifying Possible Improvements of Software Development Life Cycle (SDLC) Process of a Bank by Using Process Mining

Software development with its unique characteristics having knowledge-intensive and human-oriented aspects and complex domains, challenges organizations. The timely outcomes with high quality and desired cost that directly affect customer satisfaction have an important place in many organizations, including banks. In the last decade, as an emerging technique for business processes management, process mining has been applied in many domains, including manufacturing, supply chain, government, healthcare, and software engineering. There are limited number of studies on process mining techniques carried out for the software process, especially in the banking sector. A lack of tool infrastructure enabling to run the entire software development process and the challenges in integrating processed data from separated varying tools and assets complicate the use of process mining for software processes. This paper aims to identify the improvement points in the software development process of the Kuveyt Turk Participation Bank in Turkey through the surfacing actions. The findings and results are gathered by the application of process mining techniques of bupaR, and evaluation is provided by experts in the bank. After that, the relevant process improvements are identified. The results of this paper show that using process mining provides the organization with beneficial results, in particular, and a comprehensive view of the end-to-end Software Development Life Cycle (SDLC) processes.

Assessment of the feasibility of developing a clinical pathway using a clinical order log

A clinical pathway (CP) is a tool for effectively managing a care process. There are several research efforts on developing clinical pathways (CPs) in the process mining domain. However, the nature of the data affects data analysis results, and patient clinical variability makes it challenging to develop CPs. Thus, it is crucial to determine candidate care processes that can be standardized as CPs before applying process mining techniques. This paper proposed a method for assessing CP feasibility regarding clinical complexity using clinical order logs from electronic health records. The proposed method consists of data preparation, activity & trace homogeneity evaluations, and process inspection using process mining. Each step consists of metrics to measure the homogeneity of processes and a visualization method to demonstrate the diversity of processes based on the log. The case study was conducted with five surgical groups of patients from a tertiary hospital in South Korea to validate the proposed method. The five groups of patients were successfully assessed. In addition, the visualization methods helped clinical experts grasp the diversity of care processes.

Online correlation for unlabeled process events: A flexible CEP-based approach

Process mining is a sub-field of data mining that focuses on analyzing timestamped and partially ordered data. This type of data is commonly called event logs. Each event is required to have at least three attributes: case ID, task ID/name, and timestamp to apply process mining techniques. Thus, any missing information need to be supplied first. Traditionally, events collected from different sources are manually correlated. While this might be acceptable in an offline setting, this is infeasible in an online setting. Recently, several use cases have emerged that call for applying process mining in an online setting. In such scenarios, a stream of high-speed and high-volume events continuously flow, e.g. IoT applications, with stringent latency requirements to have insights about the ongoing process. Thus, event correlation must be automated and occur as the data is being received.We introduce an approach that correlates unlabeled events received on a stream. Given a set of start activities, our approach correlates unlabeled events to a case identifier. Our approach is probabilistic. That implies a single uncorrelated event can be assigned to zero or more case identifiers with different probabilities. Moreover, our approach is flexible. That is, the user can supply domain knowledge in the form of constraints that reduce the correlation space. This knowledge can be supplied while the application is running. We realize our approach using complex event processing (CEP) technologies. We implemented a prototype on top of Esper, a state of the art industrial CEP engine. We compare our approach to baseline approaches. The experimental evaluation shows that our approach outperforms the throughput and latency of the baseline approaches. It also shows that using real-life logs, the accuracy of our approach can compete with the baseline approaches.

Developing a Process Mining Tool Based on HL7

In healthcare facilities, processes are not always carried out under the expected methods. The variation in practice leads to lesser quality treatments and greater costs. Within a single visit, patients are now likely to interact with multiple departments, healthcare providers, and Health Information Systems (HIS). Because of these events, information is frequently dispersed, not normalized, and incoherent, creating several barriers to overview processes and audit their quality. Process mining can be a useful tool for getting over some of these obstacles. We designed a procedure to automatically apply process mining techniques using the HL7 standard, which is used for exchanging information between HIS, as a source of event logs. Our work provides a way for pooling HL7 messages from a unified repository of a healthcare institution and provides a pipeline to apply process mining methods to create insights relative to the healthcare processes that are implemented. We show a few diagrams to demonstrate the tool’s potential as a process formalization and analysis tool. We concluded that using HL7 messages as a proxy for processes that involve several HIS is a way to easily provide process mining capabilities to an organization.

AI-Empowered Process Mining for Complex Application Scenarios: Survey and Discussion

The ever-increasing attention of process mining (PM) research to the logs of low structured processes and of non-process-aware systems (e.g., ERP, IoT systems) poses a number of challenges. Indeed, in such cases, the risk of obtaining low-quality results is rather high, and great effort is needed to carry out a PM project, most of which is usually spent in trying different ways to select and prepare the input data for PM tasks. Two general AI-based strategies are discussed in this paper, which can improve and ease the execution of PM tasks in such settings: (a) using explicit domain knowledge and (b) exploiting auxiliary AI tasks. After introducing some specific data quality issues that complicate the application of PM techniques in the above-mentioned settings, the paper illustrates these two strategies and the results of a systematic review of relevant literature on the topic. Finally, the paper presents a taxonomical scheme of the works reviewed and discusses some major trends, open issues and opportunities in this field of research.

Mapping the Patient's Journey in Healthcare through Process Mining.

Nowadays, assessing and improving customer experience has become a priority, and has emerged as a key differentiator for business and organizations worldwide. A customer journey (CJ) is a strategic tool, a map of the steps customers follow when engaging with a company or organization to obtain a product or service. The increase of the need to obtain knowledge about customers’ perceptions and feelings when interacting with participants, touchpoints, and channels through different stages of the customer life cycle. This study aims to describe the application of process mining techniques in healthcare as a tool to asses customer journeys. The appropriateness of the approach presented is illustrated through a case study of a key healthcare process. Results depict how a healthcare process can be mapped through the CJ components, and its analysis can serve to understand and improve the patient’s experience.

Process discovery on geolocation data

Fleet tracking technology collects real-time information about geolocation of vehicles as well as driving-related data. This information is typically used for location monitoring as well as for analysis of routes, vehicles and drivers. From an operational point of view, the geolocation simply identifies the state of a vehicle in terms of positioning and navigation. From a management point of view, the geolocation may be used to infer the state of a vehicle in terms of process (e.g., driving, fueling, maintenance, or lunch break). Meaningful information may be extracted from these inferred states using process mining. An innovative methodology for inferring process states from geolocation data is proposed in this paper. Also, it is presented the potential of applying process mining techniques on geolocation data for process discovery.

Getting There: Evidence-Based Decision-Making in Road Trauma Prehospital Transport and Care in Queensland

Introduction:Process mining, a branch of data science, aims at deriving an understanding of process behaviors from data collected during executions of the process. In this study, we apply process mining techniques to examine retrieval and transport of road trauma patients in Queensland. Specifically, we use multiple datasets collected from ground and air ambulance, emergency department, and hospital admissions to investigate the various patient pathways and transport modalities from accident to definitive care.Aim:The project aims to answer the question, “Are we providing the right level of care to patients?” We focus on (i) automatically discovering, from historical records, the different care and transport processes, and (ii) identifying and quantifying factors influencing deviance from standard processes, e.g. mechanisms of injury and geospatial (crash and trauma facility) considerations.Methods:We adapted the Cross-Industry Standard Process for Data Mining methodology to Queensland Ambulance Service, Retrieval Services Queensland (aero-medical), and Queensland Health (emergency department and hospital admissions) data. Data linkage and “case” definition emerged as particular challenges. We developed detailed data models, conduct a data quality assessment, and preliminary process mining analyses.Results:Preliminary results only with full results are presented at the conference. A collection of process models, which revealed multiple transport pathways, were automatically discovered from pilot data. Conformance checking showed some variations from expected processing. Systematic analysis of data quality allowed us to distinguish between systemic and occasional quality issues, and anticipate and explain certain observable features in process mining analyses. Results will be validated with domain experts to ensure insights are accurate and actionable.Discussion:Preliminary analysis unearthed challenging data quality issues that impact the use of historical retrieval data for secondary analysis. The automatically discovered process models will facilitate comparison of actual behavior with existing guidelines.

Generating time-based label refinements to discover more precise process models

Process mining is a research field focused on the analysis of event data with the aim of extracting insights related to dynamic behavior. Applying process mining techniques on data from smart home environments has the potential to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions. Finding the right event labels to enable the application of process mining techniques is however far from trivial, as simply using the triggering sensor as the label for sensor events results in uninformative models that allow for too much behavior (overgeneralizing). Refinements of sensor level event labels suggested by domain experts have been shown to enable discovery of more precise and insightful process models. However, there exists no automated approach to generate refinements of event labels in the context of process mining. In this paper we propose a framework for the automated generation of label refinements based on the time attribute of events, allowing us to distinguish behaviourally different instances of the same event type based on their time attribute. We show on a case study with real life smart home event data that using automatically generated refined labels in process discovery, we can find more specific, and therefore more insightful, process models. We observe that one label refinement could have an effect on the usefulness of other label refinements when used together. Therefore, we explore four strategies to generate useful combinations of multiple label refinements and evaluate those on three real life smart home event logs.

Improving User Experience in e-Commerce by Application of Process Mining Techniques

Improving User Experience in e-Commerce by Application of Process Mining Techniques

Detection of Bottlenecks in Hospital Processes from the XAVIA HIS System using Process Mining

Sanitary services present various challenges, one of them are bottlenecks. bottlenecks cause anomalies and eventualities in the processes flow, directly affecting the patient’s attention quality. The health organizations procure to apply a process management focusing and the usage of different technologies to improve their performance. Process mining is a technology for the process analysis, which possibilities to identify characteristics and issues associated with the execution of the process from information systems. XAVIA HIS system has the necessary characteristics to contribute to the identification of bottlenecks applying process mining techniques. This research had as objective to develop and integrates an analysis view to identify bottlenecks from the XAVIA HIS system. To fulfill the proposed objective was selected and personalized the Replay a log on Petri Net for Performance / Conformance technique of process mining on the system. The analysis view developed and incorporated to the system, will allow making an analysis of the sanitary institutions execution processes. The solution proposal benefits the professionals and patients of the hospital institutions, increasing that way the quality of the provided services.

Applying process mining techniques to improve emergency response planning for chemical spills

To evaluate an implementation process for emergency planning and to assess its effectiveness, a technical method based on process mining is utilized for the first time in hazardous chemical accidents from the perspectives of workflow and organizational structure. The proposed method investigates: (1) how the emergency-response tasks of a target emergency plan are effectively executed; and (2) how the emergency-response actors coordinate and cooperate with each other. For this purpose, a fuzzy mining algorithm was applied to reconstruct the drill process for analysing workflow. Social network mining techniques, namely density, reciprocity, node degree, centrality, and handover of work metrics, were utilized to explore the organizational structure among the actors. By means of emergency-response drills, a pre-defined emergency plan for chlorine release was analyzed, for which mining results were obtained. Regarding the workflow, four deviations were detected from the target emergency plan, which suggest ways of improving the plan rules. Regarding the organizational structure, the most influential actors A1, A5, A10, whose actions directly determined the plan implementation and outcome. The proposed method can facilitate the improvement of existing processes and organizational structures for chemical accident emergency response planning.

Practical Aspects of Event Logs Creation for Industrial Process Modelling

Abstract In the paper we address the challenge of applying process mining techniques for discovering models of underground mining operations based on a sensor data. The paper presents practical approach of creation an event log based on industrial sensors data gathered in an underground mine monitoring systems. The proposed approach enables to generate event logs at different generalization levels based on several numbers of discovered stages of devices performance. For discovering process stages data mining techniques such as exploratory data analysis, clustering and classification have been applied. Created event log has been used in one of the process mining tasks - process model discovery.

Establishment of maintenance inspection intervals: an application of process mining techniques in manufacturing

Reducing costs and increasing equipment availability (uptime) are among the main goals of industrial ventures. Well defined interval durations between maintenance inspections provide major support in achieving these targets. However, in order to establish the best interval length, process behavior, cycle times and related costs must be clearly known, and future estimates for these parameters must be established. This paper applies process mining techniques in developing a probabilistic model in Bayesian Networks integrated to predictive models. The probability of a given activity occurring in the probabilistic model output establishes the forecast boundaries for predictive models, responsible for estimating process cycle times. Availability (uptime) and cost functions are mathematically defined and an iterative process is performed in the length of intervals between maintenance inspections until the time and costs wasted are minimized and the best interval duration is found. The probabilistic model enables simulating changes in the event occurrence probability, allowing a number of different scenarios to be visualized and providing better support to managers in scheduling maintenance activities. The results show that production losses can be further reduced through optimally defined intervals between maintenance inspections.

Evaluation and Extracting Factual Software Architecture of Distributed System by Process Mining Techniques

The factual software architectures that are actually implemented of distributed systems do not conform the planned software architectures CITATION Bec10 \l 1033 (Beck 2010) . It happens due to the complexity of distributed systems. This problem begets two main challenges; First, how to extract the factual software architectures with the proper techniques and second, how to compare the planned software architecture with the extracted factual architecture. This study aims to use process mining to discover factual software architecture from codes and represents software architecture model in Petri Net to evaluate model by the linear temporal logic and process mining. In this paper, the applicability of process mining techniques, implemented in the ProM6.7 framework is shown to extract and evaluate factual software architectures. Furthermore, capabilities of Hierarchical Colored Petri Net implemented in CPN4.0 are exploited to model and simulate software architectures. The proposed approach has been conducted on a case study to indicate applicability of the approach in the distributed data base system. The final result of the case study indicates process mining is able to extract factual software architectures and also to check its conformance.