title: A guide for the application of analytics on healthcare processes: A dynamic view on patient pathways.
abstract: The aim of this study is to guide healthcare instances in applying process analytics on healthcare processes. Process analytics techniques can offer new insights in patient pathways, workflow processes, adherence to medical guidelines and compliance with clinical pathways, but also bring along specific challenges which will be examined and addressed in this paper.
  
  
  
  The following methodology is proposed: log preparation, log inspection, abstraction and selection, clustering, process mining, and validation. It was applied on a case study in the type 2 diabetes mellitus domain.
  
  
  
  Several data pre-processing steps are applied and clarify the usefulness of process analytics in a healthcare setting. Healthcare utilization, such as diabetes education, is analyzed and compared with diabetes guidelines. Furthermore, we take a look at the organizational perspective and the central role of the GP. This research addresses four challenges: healthcare processes are often patient and hospital specific which leads to unique traces and unstructured processes; data is not recorded in the right format, with the right level of abstraction and time granularity; an overflow of medical activities may cloud the analysis; and analysts need to deal with data not recorded for this purpose. These challenges complicate the application of process analytics. It is explained how our methodology takes them into account.
  
  
  
  Process analytics offers new insights into the medical services patients follow, how medical resources relate to each other and whether patients and healthcare processes comply with guidelines and regulations.
authors.affiliation: KU Leuven, Dept. of Decision Sciences and Information Management, Naamsestraat 69, B-3000 Leuven, Belgium. Electronic address: Jasmien.Lismont@kuleuven.be.
authors.forename: Jasmien
authors.initials: J
authors.lastname: Lismont
authors.affiliation: KU Leuven, Dept. of Decision Sciences and Information Management, Naamsestraat 69, B-3000 Leuven, Belgium.
authors.forename: Anne-Sophie
authors.initials: AS
authors.lastname: Janssens
authors.affiliation: KU Leuven, Dept. of Public Health, Kapucijnenvoer 35, B-3000 Leuven, Belgium.
authors.forename: Irina
authors.initials: I
authors.lastname: Odnoletkova
authors.affiliation: KU Leuven, Dept. of Decision Sciences and Information Management, Naamsestraat 69, B-3000 Leuven, Belgium.
authors.forename: Seppe
authors.initials: S
authors.lastname: Vanden Broucke
authors.affiliation: KU Leuven, Dept. of Decision Sciences and Information Management, Naamsestraat 69, B-3000 Leuven, Belgium.
authors.forename: Filip
authors.initials: F
authors.lastname: Caron
authors.affiliation: KU Leuven, Dept. of Decision Sciences and Information Management, Naamsestraat 69, B-3000 Leuven, Belgium.
authors.forename: Jan
authors.initials: J
authors.lastname: Vanthienen
mesh_terms: D016000:Cluster Analysis; D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records; D006761:Hospitals; D006801:Humans; D008490:Medical Informatics / Q000379:methods*; D017064:Process Assessment, Health Care / Q000379:methods*
keywords: Case study; Electronic healthcare records; Healthcare processes; Patient pathway; Process analytics