scholarly journals A Comparative Process Mining Analysis of Road Trauma Patient Pathways

2020 ◽  
Vol 17 (10) ◽  
pp. 3426
Author(s):  
Robert Andrews ◽  
Moe T. Wynn ◽  
Kirsten Vallmuur ◽  
Arthur H. M. ter Hofstede ◽  
Emma Bosley
Keyword(s):  
Hospital Care ◽  
Service Providers ◽  
Process Mining ◽  
Process Analysis ◽  
Process Modelling ◽  
Hospital System ◽  
Domain Experts ◽  
Patient Pathways ◽  
Starting Point ◽  
Automated Process

In this paper we report on key findings and lessons from a process mining case study conducted to analyse transport pathways discovered across the time-critical phase of pre-hospital care for persons involved in road traffic crashes in Queensland (Australia). In this study, a case is defined as being an individual patient’s journey from roadside to definitive care. We describe challenges in constructing an event log from source data provided by emergency services and hospitals, including record linkage (no standard patient identifier), and constructing a unified view of response, retrieval, transport and pre-hospital care from interleaving processes of the individual service providers. We analyse three separate cohorts of patients according to their degree of interaction with Queensland Health’s hospital system (C1: no transport required, C2: transported but no Queensland Health hospital, C3: transported and hospitalisation). Variant analysis and subsequent process modelling show high levels of variance in each cohort resulting from a combination of data collection, data linkage and actual differences in process execution. For Cohort 3, automated process modelling generated ’spaghetti’ models. Expert-guided editing resulted in readable models with acceptable fitness, which were used for process analysis. We also conduct a comparative performance analysis of transport segment based on hospital ‘remoteness’. With regard to the field of process mining, we reach various conclusions including (i) in a complex domain, the current crop of automated process algorithms do not generate readable models, however, (ii) such models provide a starting point for expert-guided editing of models (where the tool allows) which can yield models that have acceptable quality and are readable by domain experts, (iii) process improvement opportunities were largely suggested by domain experts (after reviewing analysis results) rather than being directly derived by process mining tools, meaning that the field needs to become more prescriptive (automated derivation of improvement opportunities).

scholarly journals Process Modelling Based on Event Logs

2018 ◽  
Vol 1 (1) ◽  
pp. 385-392
Author(s):  
Edyta Brzychczy
Keyword(s):  
Process Management ◽  
Process Mining ◽  
Process Analysis ◽  
Process Modelling ◽  
Process Models ◽  
Sources Of Information ◽  
Event Logs ◽  
It Systems ◽  
Many Sources

Abstract Process modelling is a very important stage in a Business Process Management cycle enabling process analysis and its redesign. Many sources of information for process modelling purposes exist. It may be an analysis of documentation related directly or indirectly to the process being analysed, observations or participation in the process. Nowadays, for this purpose, it is increasingly proposed to use the event logs from organization’s IT systems. Event logs could be analysed with process mining techniques to create process models expressed by various notations (i.e. Petri Nets, BPMN, EPC). Process mining enables also conformance checking and enhancement analysis of the processes. In the paper issues related to process modelling and process mining are briefly discussed. A case study, an example of delivery process modelling with process mining technique is presented.

scholarly journals No Longer Out of Sight, No Longer Out of Mind? How Organizations Engage with Process Mining-Induced Transparency to Achieve Increased Process Awareness

2021 ◽  
Author(s):  
Julia Eggers ◽  
Andreas Hein ◽  
Markus Böhm ◽  
Helmut Krcmar
Keyword(s):  
Business Process ◽  
Process Management ◽  
Business Processes ◽  
Process Mining ◽  
Process Analysis ◽  
Structured Interviews ◽  
Business Process Analysis ◽  
Starting Point ◽  
Shared Awareness ◽  
Induced Transparency

AbstractIn recent years, process mining has emerged as the leading big data technology for business process analysis. By extracting knowledge from event logs in information systems, process mining provides unprecedented transparency of business processes while being independent of the source system. However, despite its practical relevance, there is still a limited understanding of how organizations act upon the pervasive transparency created by process mining and how they leverage it to benefit from increased process awareness. Addressing this gap, this study conducts a multiple case study to explore how four organizations achieved increased process awareness by using process mining. Drawing on data from 24 semi-structured interviews and archival sources, this study reveals seven sociotechnical mechanisms based on process mining that enable organizations to create either standardized or shared awareness of sub-processes, end-to-end processes, and the firm’s process landscape. Thereby, this study contributes to research on business process management by revealing how process mining facilitates mechanisms that serve as a new, data-driven way of creating process awareness. In addition, the findings indicate that these mechanisms are influenced by the governance approach chosen to conduct process mining, i.e., a top-down or bottom-up driven implementation approach. Last, this study also points to the importance of balancing the social complications of increased process transparency and awareness. These results serve as a valuable starting point for practitioners to reflect on measures to increase organizational process awareness through process mining.

Towards Semantic Interoperability in Health Data Management Facilitating Process Mining

2020 ◽  
pp. 424-436
Author(s):  
Barbara Traxler ◽  
Emmanuel Helm ◽  
Oliver Krauss ◽  
Andreas Schuler ◽  
Josef Kueng
Keyword(s):  
Reference Model ◽  
Process Mining ◽  
Process Analysis ◽  
Healthcare Management ◽  
Delivery Of Care ◽  
Healthcare Data ◽  
Coding Systems ◽  
Business Process Analysis ◽  
Patient Pathways ◽  
Wide Range

As an evidence-based business process analysis method, process mining can be used to investigate variations in delivery of care. Existing approaches are only based on one data source. A variety of data sources means different domain languages and understanding, special processes workflows in various organizations, varying documentation with different goals and different designations and varying use of coding systems. This article describes a modular, rule-based information extraction algorithm based on CDA and compares it to a proprietary healthcare reference model approach and a resource-based extraction of healthcare data using the new standard FHIR. All three approaches can be used to derive models to extract clinical and patient pathways. Similarities and differences according to interoperability and process mining tasks are described. It is concluded that standards-based approaches allow for more interoperability and can be used for a wide range of systems to provide process insight, thus facilitating better healthcare management across institutional boundaries.

Towards Semantic Interoperability in Health Data Management Facilitating Process Mining

2018 ◽  
Vol 6 (2) ◽  
pp. 1-12
Author(s):  
Barbara Traxler ◽  
Emmanuel Helm ◽  
Oliver Krauss ◽  
Andreas Schuler ◽  
Josef Kueng
Keyword(s):  
Reference Model ◽  
Process Mining ◽  
Process Analysis ◽  
Healthcare Management ◽  
Delivery Of Care ◽  
Healthcare Data ◽  
Coding Systems ◽  
Business Process Analysis ◽  
Patient Pathways ◽  
Wide Range

As an evidence-based business process analysis method, process mining can be used to investigate variations in delivery of care. Existing approaches are only based on one data source. A variety of data sources means different domain languages and understanding, special processes workflows in various organizations, varying documentation with different goals and different designations and varying use of coding systems. This article describes a modular, rule-based information extraction algorithm based on CDA and compares it to a proprietary healthcare reference model approach and a resource-based extraction of healthcare data using the new standard FHIR. All three approaches can be used to derive models to extract clinical and patient pathways. Similarities and differences according to interoperability and process mining tasks are described. It is concluded that standards-based approaches allow for more interoperability and can be used for a wide range of systems to provide process insight, thus facilitating better healthcare management across institutional boundaries.

scholarly journals Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems

2021 ◽  
Vol 11 (12) ◽  
pp. 5476
Author(s):  
Ana Pajić Simović ◽  
Slađan Babarogić ◽  
Ognjen Pantelić ◽  
Stefan Krstović
Keyword(s):  
Process Mining ◽  
Modeling Language ◽  
Sufficient Information ◽  
Event Data ◽  
Erp Systems ◽  
Domain Experts ◽  
Domain Specific ◽  
Event Logs ◽  
Domain Specific Modeling ◽  
Specific Modeling

Enterprise resource planning (ERP) systems are often seen as viable sources of data for process mining analysis. To perform most of the existing process mining techniques, it is necessary to obtain a valid event log that is fully compliant with the eXtensible Event Stream (XES) standard. In ERP systems, such event logs are not available as the concept of business activity is missing. Extracting event data from an ERP database is not a trivial task and requires in-depth knowledge of the business processes and underlying data structure. Therefore, domain experts require proper techniques and tools for extracting event data from ERP databases. In this paper, we present the full specification of a domain-specific modeling language for facilitating the extraction of appropriate event data from transactional databases by domain experts. The modeling language has been developed to support complex ambiguous cases when using ERP systems. We demonstrate its applicability using a case study with real data and show that the language includes constructs that enable a domain expert to easily model data of interest in the log extraction step. The language provides sufficient information to extract and transform data from transactional ERP databases to the XES format.

scholarly journals How many beds? Capacity implications of hospital care demand projections in the Irish hospital system, 2015‐2030

2018 ◽  
Vol 34 (1) ◽  
Author(s):  
Conor Keegan ◽  
Aoife Brick ◽  
Brendan Walsh ◽  
Adele Bergin ◽  
James Eighan ◽  
...  
Keyword(s):  
Hospital Care ◽  
Hospital System ◽  
Irish Hospital

Process Mining–Based Method of Designing and Optimizing the Layouts of Emergency Departments in Hospitals

2016 ◽  
Vol 10 (4) ◽  
pp. 105-120 ◽  
Author(s):  
Farhood Rismanchian ◽  
Young Hoon Lee
Keyword(s):  
Operations Management ◽  
Process Mining ◽  
Clinical Pathways ◽  
Process Analysis ◽  
Healthcare Services ◽  
Service Industries ◽  
Programming Approach ◽  
Related Information ◽  
Critical Patients ◽  
Increasing Demand

Objective: This article proposes an approach to help designers analyze complex care processes and identify the optimal layout of an emergency department (ED) considering several objectives simultaneously. These objectives include minimizing the distances traveled by patients, maximizing design preferences, and minimizing the relocation costs. Background: Rising demand for healthcare services leads to increasing demand for new hospital buildings as well as renovating existing ones. Operations management techniques have been successfully applied in both manufacturing and service industries to design more efficient layouts. However, high complexity of healthcare processes makes it challenging to apply these techniques in healthcare environments. Method: Process mining techniques were applied to address the problem of complexity and to enhance healthcare process analysis. Process-related information, such as information about the clinical pathways, was extracted from the information system of an ED. A goal programming approach was then employed to find a single layout that would simultaneously satisfy several objectives. Results: The layout identified using the proposed method improved the distances traveled by noncritical and critical patients by 42.2% and 47.6%, respectively, and minimized the relocation costs. Conclusion: This study has shown that an efficient placement of the clinical units yields remarkable improvements in the distances traveled by patients.

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