Process Mining provides better customer service – AOK improves its customer service with mpmX

Initial Situation‍

The AOK national companies are in the midst of a rapid change in our living environment. Companies are facing increasing demands, while their financial margins are becoming more and more limited. It is therefore essential that companies continuously develop their processes and consistently design them in a digital and efficient way. At the same time, the healthcare sector faces massive technological and structural change, from which both patients and healthcare providers can benefit. This situation offers payers, such as the AOK, a unique opportunity to proactively shape this change and thus contribute to the well-being of their insured. The focus here will be on processes from customer service.

Project Objectives‍

How can the concerns of people with statutory health insurance in Germany be dealt with more quickly, in a more targeted manner and more efficiently? This question has been on the AOK‘s mind for some time. The Allgemeine Ortskrankenkasse (AOK) has therefore introduced process mining for data-driven process optimization. In order to become more digitally oriented and to offer customers the best possible service, the following goals have been set:

• Analysis of the as-is processes
• Measurement and visualization of performance
• Identification of automation potential
• Identification of bottlenecks

Why mpmX‍

The AOK national companies made a conscious decision in favour of the process mining tool mpmX – not only because of the enormous flexibility of tool use and the design options for the end user. The option of choosing a fully functional on-premise variant or a hybrid operation in addition to a SaaS solution also convinced the AOK, since especially in the context of new technologies, very close attention must be paid to handling sensitive data from customers and partners. Another advantage of mpmX is the machine learning algorithms, which help to optimize process chains in the shortest possible time, make predictions and identify additional potential for digitalization and automation, such as Robotic Process Automation (RPA).