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?

Process Mining

Based on event log information available in the hospital information system, the goal of process mining is to extract process knowledge. Without any a-priori knowledge, process mining looks inside process and makes visible what is really happening! For example, it is possible to:

The above information is the key to many process improvements. For example:

In summary, process mining offers many advantages for patients, medical specialists, hospitals, and health insurers!