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4 Challenges for Process Mining in Healthcare 4

Good process improvement can achieve both an increase of quality and lower cost at the same time. Efficient healthcare processes are very relevant, because patient treatments pose a significant burden on our aging societies.

But could hospitals be run more efficiently? Or would this necessarily mean a decrease in quality? A few weeks ago, I attended a Lean Six Sigma seminar in which a very interesting case study was presented: A process improvement team in the general hospital Reinier de Graaf groep had reduced the time patients had to stay in the hospital for a hip operation from six to only three days while improving patient satisfaction at the same time.

This example suggests that there is much more room for improvement than one might think.

Process Mining

Process mining is a revolutionary new technology for process improvement. Process mining does not start at the whiteboard to make an existing business process visible. Instead, it leverages existing log data that are collected by the many IT systems that are supporting enterprises around the world: ERP systems like SAP, but also legacy systems, CRMs, and so on, record very detailed information about the activities that have been performed, when, and by whom.

For any process improvement, determining the current ‘As-is’ process is the first necessary step. Using process mining, one can automatically and accurately visualize the actual process flows based on objective data. This transparency allows organizations to continuously monitor and improve their processes in ways that were not possible before.

But is process mining also applicable to processes in healthcare?


Healthcare processes are either diagnosis / treatment processes or of organizational nature (such as the scheduling of appointments). The biggest challenges for applying process mining to healthcare processes are their complexity, their multi-disciplinarity, that they are changing often, and the log data from the IT systems.

1. Heterogeneity

A lot of the complexity of healthcare processes comes from the heterogeneity of the patients that are treated. After all, every one of us runs through the same process when we apply for a new passport, but we are unique and complex human beings when it comes to medical conditions.

As a consequence, the individual treatment processes of different patients even with the same illness are often unique. For process mining techniques—which generalize the common process based on the individual process executions that happened in practice—this lack of similarity can be quite a challenge.

2. Multi-disciplinarity

Hospitals departments are highly specialized in their respective fields but need to work together across their disciplines. For example the doctor of a cancer patient might send the patient to the radiology department, which needs to return the results to the oncology department.

While the multi-disciplinary nature of healthcare processes adds to the complexity, it is also an indicator for improvement opportunities. Generally, process inefficiencies often emerge at the boundaries of different functional units because people oversee their part of the job quite well but lack insight into what happens before and after and why.

3. Changing fast

The medical knowledge evolves continuously. As a consequence, the corresponding medical procedures and processes change as well.

Again, this adds to the difficulty of process improvement initiatives (Try to hit a moving target!). But at the same time it increases the attractiveness of process mining because it allows to make the current process visible automatically, just based on the history logs in the IT systems.

4. Data collection

The log data is the basis for process mining. So, of course the availability and quality of data is key to be able to apply process mining techniques.

On the plus side, detailed records are kept in healthcare processes for billing purposes. And new developments such as the Electronic Patient Record will increase the availability and quality of the data.

At the same time, there are still many manual activities that are not observable. Furthermore, data entries are often made manually after an activity has actually occurred. Over time, hospital information systems will evolve and with increasing integration and automation, the data availability and quality will improve.

More to come

To give you an update on the current state of the art in process mining research in healthcare, I plan to write up a few case studies that have been performed in this area in future blog posts.

For now, join the discussion and let me know what you think about process mining in healthcare: Do you feel there is room for improvement in our hospitals? And do you think that mining (anonymized) patient history data for process improvement purposes is legitimate?

Comments (4)

I think the most challenging thing is data collection. There is a lot of manual tasks that are not registered anywhere, as you say.
Mobile technology and applications are helping doctors to register valuable data of some steps/tasks they perform, but I think it’s not going to be massive for a couple of years.
There’s still a big room there.

I agree. Sometimes, there are even more basic problems. For example, existing activity logs may only have timestamps with a date, but no time associated. This makes it difficult to reconstruct the correct process sequence. On the other hand, modern hospital information systems record very detailed information across multiple departments.

In general, manual data collection that is decoupled from the actual process is always problematic because it leads to inaccuracies. And of course analyzing inaccurate data may lead to drawing wrong conclusions.

So, in this sense mobile technology may very well help reduce the decoupling of manual data collection and the actual activities (not only enable the collection of data that has not been collected before).

Thanks for your comment!

Indeed data collection is a challenge in health. This is a huge issue in public health care systems in developing countries where health care planning and budgeting decisions are based on aggregated data from health information systems. Lower level facilities and local health management institutions cannot generate granular data to critically analyse how health interventions are performing at a population health level and cost per capita. So there is a perpetual cycle of planning, budgeting and implementation of health care programs by governments (and donor agencies) that do not leverage use of As-Is process data to validate or test out interventions before spending the scarce resources available.

Hi Nelson, yes I can see that. Thank you for adding this interesting perspective.

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