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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.


  • Week 1

    Introduction to process mining in healthcare

    • Introduction

      In this activity we explain why process mining is beneficial for the healthcare domain, we introduce ourselves, and we describe the general structure of this course.

    • The healthcare environment

      In this activity we introduce our definition of the healthcare environment, including recent developments that enable the use of process mining.

    • Process mining overview

      In this activity we introduce what process mining can do and help you to prepare to do some process mining yourself!

    • Case study: ER

      This case study on an ER process illustrates how process mining techniques can be used for the analysis of daily processes taking place in the emergency room of a university hospital.

    • Closing

      In this activity we close week 1 and test what you have learned this week.

  • Week 2

    Getting and converting event data

    • Process mining in healthcare

      In this activity we position process mining in the healthcare domain.

    • Event data extraction

      In this activity we deal with the most common issues in collecting clinical data from the real world. We will present some of the most common standards and show some pitfalls that could cause mistakes.

    • Event data conversion

      In this activity we show how the data from the system(s) can be converted to a predefined format for process mining tools to work.

    • Case study ER departement

      In this activity we present another case study, at another emergency department, with other goals and results.

    • Closing

      In this activity we close week 2 and test what you have learned this week.

  • Week 3

    Using event data: inspection, discovery and conformance checking

    • Event data exploration

      In this activity we demonstrate several ways to do a first exploration of the event data in ProM.

    • Event log filtering

      In this activity we show how an event log can be filtered to focus on particular aspects in your analysis.

    • Process discovery

      In this activity we discuss process discovery, and in particular the Inductive visual miner.

    • Conformance checking

      In this activity we discuss conformance checking, where we check how the event data conforms to the process model.

    • Case study: Careflow mining of a breastcancer treatment process

      In this activity we present a case study of careflow mining of a breastcancer treatment process

    • Closing

      In this activity we close week 3 and test what you have learned this week.

  • Week 4

    Performance analysis, process improvement and challenges for process mining

    • Performance analysis

      In this activity we show two ways to analyse the performance of a processes, e.g. how long do cases take and where are bottlenecks?

    • Social network analysis

      In this activity we discuss three different analysis methods to analyse how resources in a process work.

    • Beyond process mining

      In this article we look 'beyond' process mining: how do you make sure processes are changed, and what are current challenges in the area of process mining?

    • Case study: Central Venous Catheter training

      This is the CVC Training Case Study. In this case study we will illustrate the use of Process Mining techniques, and in particular Conformance Checking approached, for the analysis of medical procedures training.

    • Case study: Real-time indoor location systems

      In this activity we present a case study using Real-time indoor location systems' data and the PALIA analysis suite.

    • Course project and closing

      In this final activity we guide you through a process mining project, and provide some course closing remarks.

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

content provided by

Università di Pisa

content provided by

Fondazione Policlinico Universitario Agostino Gemelli

content provided by

Università di Pavia

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