Declarative process mining in healthcare

Clinical guidelines aim at improving the quality of care processes based on evidence-based insights. However, there may be good reasons to deviate from such guidelines or the guidelines may provide insufficient support as they are not tailored towards a particular setting (e.g., hospital policy or patient group characteristics). Therefore, we propose an approach to mediate between event data reflecting the clinical reality and clinical guidelines describing best-practices in medicine. Concretely, we repair and enrich declarative process models based on event logs. Initial models based on clinical guidelines are improved by observing the real process. Declarative models are used as they allow for more flexibility. Process mining techniques are used to check conformance, analyze deviations, and enrich models with conformance-related diagnostics. The approach has been applied in the urology department of the Isala hospital in the Netherlands. The results demonstrate that the approach is feasible and that our toolset based on ProM and Declare is indeed able to provide valuable insights related to process conformance.