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Artificial Intelligence in Medicine 22, 65–80 (2001) Quaglini, S., et al.: Flexible Guideline-based Patient Careflow Systems. In: Proceedings of the 34th Annual Hawaii International Conference on System Sciences, p. Weske, M.: Formal foundation and conceptual design of dynamic adaptations in a workflow management system. Rinderle, S., Reichert, M., Dadam, P.: Correctness criteria for dynamic changes in workflow systems - a survey. Reichert, M., Dadam, P.: ADEPT-flex: Supporting dynamic changes of workflows without losing control. Proceedings 18th International Conference on Advanced Information Systems Engineering Workshops, pp. Vd Aalst, W.M.P., Guenther, C., Recker, J., Reichert, M.: Using Process Mining to Analyze and Improve Process Flexibility - Position Paper. We demonstrate the application of LPM to a vaccination process. The learning process also aims to adapt the current process model to possible technology, specific domain (e.g., clinical procedures at specific institutions), environmental requirements (e.g., regulations and policies), and process innovations.

Modifications provided by the learning process may be sufficient or may need to be complemented by non-automatic changes, when unacceptable business situations arise. LPM strives to establish a learning process by (1) identifying goal and soft-goal states of the initial process model, (2) identifying exceptional states and incomplete state definitions at runtime, and (3) adapting automatically the process model according to the discovered states.

The proposed model – the learning process model (LPM) – suggests a closed-loop-model approach applied to a generic process model (GPM), which is a formal state-based and goal-based approach to process modeling. Learning how to improve business processes is an evolutionary process that must be managed as other business processes (BPs) are managed in modern organizations.

Business process modelling case study