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Process Mining in Healthcare

Learn how process mining can be used to turn healthcare data into valuable insights to improve patient care while reducing costs.

4,762 enrolled on this course

A surgeon and a process model
  • Duration

    4 weeks
  • Weekly study

    4 hours

Learn how to get the most from healthcare data using process mining

Within healthcare there are thousands of complex and variable processes that generate data including treatment of patients, lab results and internal logistic processes. Analysing this data is vital for improving these processes and ending bottlenecks.

On this course you will explore how process mining can help turn this data into valuable insights by looking at different areas of process mining and seeing how it has been applied. You will even get the chance to apply process mining on real life healthcare data.

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Skip to 0 minutes and 0 seconds Health care has changed significantly in the past decades. Through advances in medicine and science in general, more and more diseases can be treated more effectively. However, this has also increased the complexity of the health care system. Medical staff needs more training, machines are becoming more and more complex, and multiple solutions exist to cure a particular disease. This all increased the cost of health care significantly. 10% of the gross domestic product of almost any country is being spent on health, and only growth is predicted. But in this day and age, we have the data. Data can help us analyse what’s going on, and suggest improvements.

Skip to 0 minutes and 0 seconds In this course, we will teach you how to use event data and process mining techniques to analyse the data that’s already there to get insights and suggest improvements for the processes. Consider this very simple process of a patient treatment. I hope you can see that this generates a lot of data that can be used to analyse the process flow, and provide insights and suggestions for improvement. Using process mining techniques, applied on this data, we can automatically discover a process model. A process model describes which activities are performed, and in which order. And in the top model, you see yellow dots, moving through the model indicating where patients are at a particular time and date in history.

Skip to 0 minutes and 0 seconds in the bottom model, we are projecting time performance on this process model. We automatically discover this model, and time is now being projected on the activities, showing where queues exist. This allows you to analyse the process flow in great detail. When you follow this course, you will learn how to get this data, how to apply existing techniques to get these models. But also, how you can analyse conformance and compliance issues with current rules and regulations, how users are working together, and much more. If you follow this MOOC, then you will be able to do all this on the data in your health care organisation.

Skip to 0 minutes and 0 seconds I’m Joos Buijs from Eindhoven University of Technology, and I hope to see you again in this course.

What topics will you cover?

Process mining provides a critical, process-centric perspective on data, which is not available with classical data mining or machine learning techniques. Applying process mining in the healthcare environment is extra beneficial since processes are complex, and costs are high.

This course contains 4 weeks:

Week 1 introduced the healthcare environment, discussing current problems and challenges in healthcare process execution. An overview of process mining was also provided, including hands-on experience with the free and open-source process mining tool ProM. A case study was also presented showing how process mining was applied in a healthcare setting.

Week 2 discussed what data is necessary for process mining, and how to obtain this from the health information systems. Opportunities for process mining in healthcare, and a generic project approach was also discussed. This week also discussed concretely how data can be converted to the event log format. A case study was also presented, showing real results on real health data.

Week 3 covered key process mining techniques such as event log inspection (for instance via the dotted chart), event log filtering. The key process mining technique of process discovery is also shown. This week also presented ways to do conformance checking. Another case study involving process discovery was also discussed.

Week 4 focusses more on performance and social network analysis as the final process mining techniques. This week also discusses how processes can be improved using process mining results. Current challenges for process mining in healthcare are also presented.

Learning on this course

On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

By the end of the course, you‘ll be able to...

  • Explain how process mining can help in analysing and improving healthcare processes
  • Identify opportunities for process mining in a healthcare organisation
  • Describe the data requirements in order to apply process mining
  • Interpret the results of various process mining techniques in the ProM lite tool
  • Apply ProM lite on real data to obtain process mining results

Who is the course for?

This course is for healthcare experts who want to find out more about using data to solve problems and execute ideas. It will also be of interest to process mining enthusiasts who want to know more about the application of process mining to healthcare. You don’t need any prior experience, just a keen interest in the topic.

What software or tools do you need?

We strongly advise you to carry out the process mining activities using the data provided in this course. For this we will be using our free and open source tool ProM lite. Please download the most recent ProM lite version from www.promtools.org.

Who will you learn with?

Assistant professor at Eindhoven University of Technology at the Data Science Center (DSC/e) and research group of prof. dr. ir. Wil van der Aalst on process mining.

Dr. Carlos Fernández-Llatas is research coordinator in SABIEN Group at ITACA institute at Universitat Politècnica de València.

Davide Aloini is Associate Professor of Business Process Management at the Department of Energy, Systems, Land and Constructions Engineering at the University of Pisa, Italy.

Assistant Professor in the Computer Science Department at the Pontificia Universidad Católica de Chile (UC). Author of the book "Conformance Checking and Diagnosis in Process Mining."

I am Assistant Professor at the University of Pavia, in Italy. I work at the Biomedical Informatics Lab, mainly on temporal data analysis and clinical decision support systems.

Associate Professor of the Computer Science Department at Pontificia Universidad Catolica de Chile. My research areas include business process management and process mining in healthcare and education

I'm an Electronic Engineer, with a PhD in "Oncological Sciences". I'm a passionate researcher involved in Medical Informatics from more than 15 years. Guitar player, runner and free climber.

Who developed the course?

Eindhoven University of Technology

Eindhoven University of Technology (TU/e) is a research-driven, design-oriented university of technology with a strong international focus. The university was founded in 1956 and has 8,500 students.

Endorsers and supporters

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Pontificia Universidad Católica de Chile

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Universitat Politècnica de València

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Università di Pisa

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Fondazione Policlinico Universitario Agostino Gemelli

content provided by

Università di Pavia

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