Concept Drift Detection to Assess the Diffusion of Process Innovations in Healthcare

Introduction: Clinical guidelines recommend best care pathways for many clinical conditions. When significant data from clinical trials become available, new clinical guidelines are published, formalizing the advances in the field. When we consider clinical guidelines as processes of care, we can use process mining techniques to discover how innovations diffuse into healthcare and how they modify the execution of clinical processes. Methods: We conducted a study to assess the changes in process execution patterns after a 2013 update of an acute ischemic stroke (AIS) guideline. We used MIMIC-IV as the data source, including patients from 2008 until 2019 hospitalized with an AIS and applied drift detection methods to measure changes in the therapeutic process. We performed statistical tests to determine whether the underlying distribution of events reflects the changes in the guidelines post-2013. Results: Ischemic stroke patients show few significant changes in clinical practices, despite an update in guidelines. The positive control group of aortic valve replacement patients shows a significant change in clinical practices surrounding this procedure. Conclusions: This study demonstrates the use of drift detection methods as a novel method to study the diffusion of innovations in healthcare settings from a process perspective.