Measuring Patient Flow Variations: A Cross-Organisational Process Mining Approach

Process mining to optimize palliative patient flow in a high-volume radiotherapy department

Technical Innovations & Patient Support in Radiation Oncology ◽

10.1016/j.tipsro.2021.02.005 ◽

2021 ◽

Vol 17 ◽

pp. 32-39

Author(s):

L. Placidi ◽

L. Boldrini ◽

J. Lenkowicz ◽

S. Manfrida ◽

R. Gatta ◽

...

Keyword(s):

Process Mining ◽

Patient Flow ◽

High Volume ◽

Radiotherapy Department ◽

Palliative Patient

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Cross-Organisational Process Mining in Cloud Environments

Journal of Information & Knowledge Management ◽

10.1142/s0219649218500144 ◽

2018 ◽

Vol 17 (02) ◽

pp. 1850014 ◽

Cited By ~ 1

Author(s):

Mario Luca Bernardi ◽

Marta Cimitile ◽

Francesco Mercaldo

Keyword(s):

Cloud Computing ◽

Distributed Systems ◽

Resource Management ◽

Business Processes ◽

Process Mining ◽

Process Analysis ◽

Real Life ◽

Event Logs ◽

Private And Public ◽

Organisational Process

Cloud computing market is continually growing in the last years and becoming a new opportunity for business for private and public organisations. The diffusion of multi-tenants distributed systems accessible by clouds leads to the birth of some cross-organisational environments, increasing the organisation efficiency, promoting the business dynamism and reducing the costs. In spite of these advantages, this new business model drives the interest of researchers and practitioners through new critical issues. First of all, the multi-tenant distributed systems need new techniques to improve the traditional resource management distribution along the different tenants. Secondly, new approaches to the process analysis and monitoring analysed since cross-organisational environments allow various organisations to execute the same process in different variants. Hence, information about how each process variant characterised can be collected by the system and stored as process logs. The usefulness of such logs is twofold: these logs can be analysed using some process mining techniques to understand and improve the business processes and can be used to find better resource management and scalability. This paper proposes a cloud computing multi-tenancy architecture to support cross-organisational process executions and improve resource management distribution. Moreover, the approach supports the systematic extraction/composition of distributed data from the system event logs that are assumed to carry information of each process variant. To this aim, the approach also integrates an online process mining technique for the runtime extraction of business rules from event logs. Declarative processes are used to represent process variants running on the analysed infrastructure as they are particularly suited to represent the business process in a context characterised by low predictability and high variability. In this work, we also present a case study where the proposed architecture is implemented and applied to the execution of a real-life process of online products selling.

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Performance analysis in emergency departments: a data-driven approach

Measuring Business Excellence ◽

10.1108/mbe-07-2017-0040 ◽

2018 ◽

Vol 22 (2) ◽

pp. 130-145 ◽

Cited By ~ 11

Author(s):

Alessandro Stefanini ◽

Davide Aloini ◽

Elisabetta Benevento ◽

Riccardo Dulmin ◽

Valeria Mininno

Keyword(s):

Real Time ◽

Emergency Departments ◽

Process Mining ◽

Patient Flow ◽

Waiting Times ◽

Simulation Models ◽

Data Driven ◽

Content Type ◽

Actual Patient ◽

Data Driven Approach

PurposeThis paper aims to investigate the process performances in Emergency Departments (EDs) with a novel data-driven approach, permitting to discover the entire patient-flow, deploy the performances in term of time and resources on the activities and flows and identify process deviations and critical bottlenecks. Moreover, the use of this methodology in real time might dynamically provide a picture of the current situation inside the ED in term of waiting times, crowding, resources, etc., supporting the management of patient demand and resources in real time.Design/methodology/approachThe proposed methodology exploits the process-mining techniques. Starting from the event data inside the hospital information systems, it permits automatically to extract the patient-flows, to evaluate the process performances, to detect process exceptions and to identify the deviations between the expected and the actual results.FindingsThe application of the proposed method to a real ED revealed being valuable to discover the actual patient-flow, measure the performances of each activity with respect to the predefined targets and compare different operating situations.Practical implicationsStarting from the results provided by this system, hospital managers may explore the root causes of deviations, identify areas for improvements and hypothesize improvement actions. Finally, process-mining outputs may provide useful information for creating simulation models to test and compare alternative ED operational scenarios.Originality/valueThis study responds to the need of novel approaches for monitoring and evaluating processes performances in the EDs. The novelty of this data-driven approach is the opportunity to timely connect performances, patient-flows and activities.

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