Health Analytics Using Process Mining

Process Knowledge Gaps

In many countries, healthcare organizations, such as hospitals, are under increasing pressure to improve productivity and reduce costs. Specifically, in most Western nations, the demand for hospital services is growing. Prolonged medical care for an ageing population, increasing costs associated with the management of chronic diseases, innovative but costly treatment possibilities, and the need for more healthcare personnel put pressure on care organizations. In order to deal with this situation, hospitals need to control and improve their care processes such that they are executed more efficiently and effectively.

On the other hand, today's hospital information systems contain a wealth of data. Even more, the volume of healthcare-related data is growing dramatically with the increasing use of electronic health record systems (EHR), picture archiving and communication systems (PACS), and many more systems that collect data. Contemporary analysis approaches typically only focus on presenting aggregate data about the care processes performed. For example, information is provided on performance indicators such as the number of knee operations, the length of waiting lists, and the success rate of surgery. Unfortunately, no detailed inside information is provided about the processes that are performed thereby restricting the ability to effectively understand and improve care processes!

Moreover, traditional methods of process discovery and improvement rely on lengthy interviews and group meetings in order to try to understand how things are working. Next to the huge costs associated with these methods, the results are inherently subjective and biased by idealized perceptions.

So by using factual execution data readily available within the organizations' information systems, what can healthcare organizations do to get insights into how healthcare processes are really executed?

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Within a typical hospital information system many different subsystems can be found. These systems record all steps of processes in execution. Based on this data, process mining aims at discovering process knowledge. Moreover, it allows for obtaining both qualitative and quantitative insights into these processes (e.g. performance information). As factual execution data is used, an objective view is provided on how healthcare processes are really executed. Obviously, there is a clear difference between process mining and more traditional ways of investigating business processes. For example, by conducting interviews there is always the risk that answers are subjective, idealized, and give an incomplete view on reality.

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Case Studies

We have applied process mining for various healthcare organizations. For example, in the context of the STW project Developing Tools for Understanding Healthcare Processes we analyzed processes of the following care organizations:

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Papers About Process Mining in Healthcare

On this page, we give an overview of all the scholarly publications describe a (short) real-life application of process mining in healthcare. We make a distinction between publications about Discovery and publications about Conformance.

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About Process Mining at TU/e

The Architecture of Information Systems (AIS) research group at TU/e investigates methods, techniques and tools for the design and analysis of Process-Aware Information Systems (PAIS), i.e., systems that support business processes (workflows) inside and between organizations. Hence, the focus is not limited to information systems and their architecture, but also the modeling and analysis of business processes and the organizations they support. AIS’s mission is to be one of the worldwide leading research groups in process modeling and analysis, process mining, and PAIS technology. The group is driven by the motto “Process Technology that Works” and is well-known for innovations that are highly original and applicable in real-life situations. AIS researchers are working on a wide range of topics including workflow management, process mining, simulation, Petri nets, business process management, process modeling, and process analysis. This resulted not only in landmark publications but also in software products and true impact in industry.

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Contact Information

Health Analytics Using Process Mining Team

Architecture of Information Systems
Process Mining Group
Department of Mathematics and Computer Science Eindhoven University of Technology
P.O. Box 513
5600 MB Eindhoven
The Netherlands

Phone: +31 40 247 2733

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