scholarly journals Developing a Process Mining Tool Based on HL7

2022 ◽  
Vol 196 ◽  
pp. 501-508
Author(s):  
João Coutinho-Almeida ◽  
Ricardo João Cruz-Correia
Keyword(s):  
Author(s):  
B. F. van Dongen ◽  
A. K. A. de Medeiros ◽  
H. M. W. Verbeek ◽  
A. J. M. M. Weijters ◽  
W. M. P. van der Aalst

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
J Borges-Rosa ◽  
M Oliveira-Santos ◽  
M Simoes ◽  
C Teixeira ◽  
G Ibanez-Sanchez ◽  
...  

Abstract Introduction The expected delay of transport between patient location and percutaneous coronary intervention (PCI) centre is paramount for choosing the adequate reperfusion therapy in ST-segment elevation myocardial infarction (STEMI). The central region of Portugal has heterogeneity in PCI assess due to geographical reasons. However, this data is usually presented numerically without providing a visual distribution of patients. Purpose We aimed to analyse the impact of distance to PCI centres on mortality in patients with STEMI through visual maps of patients' flow by using an experimental process mining tool, integrated in EIT Health's project PATHWAYS. Methods Using the Portuguese Registry of Acute Coronary Syndromes (ProACS), we retrospectively assessed patients with an established diagnosis of STEMI, geographical presentation specified, reperfusion option identified (PCI, fibrinolysis or no reperfusion), short-term outcomes defined as discharge or in-hospital death. With the 2 317 patients that fulfilled the criteria, we used a process mining tool to build national and regional models that represent the flow of patients in a healthcare system, enhancing differences between groups. Results Colour gradient in nodes and arrows changes from green to red, with green representing a lower number of patients as opposed to red. In the national model, most patients from all regions had PCI. Mortality was similar between PCI and fibrinolysis groups (4%) but higher in those without reperfusion (9%). In the central region model, one third of the patients were more than 120 minutes away from a PCI centre. Despite that, almost one third of these patients had PCI instead of fibrinolysis. In this model, fibrinolytic therapy had higher in-hospital survival rate than PCI (98% vs. 94%). Overall mortality was higher in the central model compared with the national model (6.92% vs. 5%). Central region had less PCI (53% vs. 73%), more fibrinolysis (15% vs. 7%) and more patients with no reperfusion (32% vs. 20%). Conclusion In the ProACS registry, mortality was higher in the central region compared with national data. Even though global interpretation of these findings is limited by underrepresentation from certain central areas, process mining offers an easily understandable view of patients flow. With its statistical upgrade and continuous development, this tool will facilitate the analysis of big data and comparison between groups. Funding Acknowledgement Type of funding source: Public grant(s) – EU funding. Main funding source(s): EIT Health


Author(s):  
Adrien Hemmer ◽  
Remi Badonnel ◽  
Jerome Francois ◽  
Isabelle Chrisment

Author(s):  
Boudewijn F. van Dongen ◽  
Wil M. P. van der Aalst
Keyword(s):  

2013 ◽  
Vol 13 (4) ◽  
pp. 399-406 ◽  
Author(s):  
Sang Hyun Choi ◽  
Kwan Hee Han ◽  
Gun Hoon Lim

2017 ◽  
Vol 112 ◽  
pp. 306-315 ◽  
Author(s):  
Stefano Bistarelli ◽  
Tommaso Di Noia ◽  
Marina Mongiello ◽  
Francesco Nocera

2019 ◽  
Vol 13 (1) ◽  
pp. 27-36
Author(s):  
Andreas Neubert

Due to the different characteristics of the piece goods (e.g. size and weight), they are transported in general cargo warehouses by manually-operated industrial trucks such as forklifts and pallet trucks. Since manual activities are susceptible to possible human error, errors occur in logistical processes in general cargo warehouses. This leads to incorrect loading, stacking and damage to storage equipment and general cargo. It would be possible to reduce costs arising from errors in logistical processes if these errors could be remedied in advance. This paper presents a monitoring procedure for logistical processes in manually-operated general cargo warehouses. This is where predictive analysis is applied. Seven steps are introduced with a view to integrating predictive analysis into the IT infrastructure of general cargo warehouses. These steps are described in detail. The CRISP4BigData model, the SVM data mining algorithm, the data mining tool R, the programming language C++ for the scoring in general cargo warehouses represent the results of this paper. After having created the system and installed it in general cargo warehouses, initial results obtained with this method over a certain time span will be compared with results obtained without this method through manual recording over the same period.


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