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Title of the Thesis Author Emmanuel Helm MSc Submission Institute of Application- oriented Knowledge Processing (FAW) First Supervisor PROCESS MINING IN a.Univ.-Prof. Dr. Josef Küng STANDARDS-BASED Second Supervisor Prof. Marcos Sepúlveda PhD HEALTHCARE February 2021 INFORMATION SYSTEMS Doctoral Thesis to confer the academic degree of Doktor der technischen Wissenschaften in the Doctoral Program Technische Wissenschaften JOHANNES KEPLER UNIVERSITY LINZ Altenberger Straße 69 4040 Linz, Austria jku.at Sworn Declaration I hereby declare under oath that the submitted Doctoral Thesis has been written solely by me without any third-party assistance, information other than provided sources or aids have not been used and those used have been fully documented. Sources for literal, paraphrased, and cited quotes have been accurately credited. The submitted document here present is identical to the electronically submitted text document. Linz, 25th February, 2021 Emmanuel Helm i “Computer Science is no more about comput- ers than astronomy is about telescopes.” Edsger W. Dijkstra iii Abstract Healthcare organizations are bureaucracies where groups of trained professionals coordi- nate their work within functional units or departments. This coordination is based more on the standardization of skills and knowledge rather than on the standardization of work processes. However, by operating the user interfaces of their information systems and medical devices, healthcare personnel triggers a sequence of functions and procedures. From the point of view of the systems and their recorded event logs and databases, these actions constitute a process that is emerging over time. The research discipline of “process mining” aims to facilitate understanding and improvement of these processes. This thesis addresses challenges that emanate from the application of process mining techniques to data of healthcare information systems – especially data collection, data integration, and data quality. To this end, a review of existing work in the field process mining in healthcare is conducted and the characteristics of healthcare data are described. Based on the assumption that healthcare information systems strive for interoperability, methods are developed and tested to utilize the (process) data recorded in standardized “audit trails”. The contributions include an interface to access this data based on Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR). This thesis also presents a case study on guideline compliance checking for melanoma surveillance procedures based on process mining. The conclusion is that different initiatives and standardization efforts gradually converge towards a better, more interoperable, health IT environment. Syntactic and semantic interoperability pave the road for “process interoperability” and the standardization of data plays a key role in achieving a better understanding of the complex interactions in healthcare workflows. v Kurzfassung Gesundheitseinrichtungen sind bürokratisch organisiert und Gruppen ausgebildeter Fach- leute koordinieren ihre Arbeit innerhalb funktionaler Einheiten (Abteilungen). Die Ko- ordination basiert dabei eher auf der Standardisierung von Fähigkeiten und Wissen als auf der Standardisierung von Arbeitsprozessen. Das Gesundheitspersonal initiiert durch die Bedienung von Benutzerschnittstellen der Informationssysteme und medizinischen Geräte jedoch eine Kette von Funktionsaufrufen und Interaktionen im Hintergrund. Diese Aktionen stellen, aus der Sicht der Systeme, aufgezeichnet in ihren Ereignisprotokollen und Datenbanken, einen Prozess dar, der sich über die Zeit entwickelt. Die Forschungs- disziplin des “Process Mining” zielt darauf ab, das Verständnis und die Verbesserung dieser Prozesse zu ermöglichen. Diese Dissertation befasst sich mit den Herausforderungen, die sich aus der Anwendung von Process-Mining-Techniken auf Daten von Gesundheitsinformationssystemen ergeben - insbesondere Datenerfassung, Datenintegration und Datenqualität. Zu diesem Zweck wird ein Überblick über bestehende Arbeiten auf dem Gebiet des Process Mining im Gesund- heitswesen gegeben und die Charakteristika von Gesundheitsdaten werden beschrieben. Ausgehend von der Annahme, dass Gesundheitsinformationssysteme nach Interopera- bilität streben, werden Methoden entwickelt und getestet, um die in standardisierten “Audit Trails” aufgezeichneten (Prozess-)Daten zu nutzen. Der wissenschaftliche Beitrag umfasst zusätzlich eine Schnittstelle für den Zugriff auf diese Daten auf Basis des Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) Standards. In dieser Dissertation wird auch eine Fallstudie zur Überprüfung der Einhaltung klinischer Leitfäden nach Melanom-Behandlungen basierend auf Process Mining präsentiert. Verschiedene Initiativen und Standardisierungsvorhaben im Gesundheitswesen führen da- zu, dass die IT-Systeme allmählich interoperabler werden. Syntaktische und semantische Interoperabilität ebnen den Weg für die “Prozessinteroperabilität”, und die Standardisie- rung von Daten spielt eine Schlüsselrolle beim Erreichen eines besseren Verständnisses für die komplexen Prozesse und deren Interaktionen im Gesundheitswesen. vii Contents iii Abstract v Kurzfassung vii 1 Introduction 1 1.1 Process Mining . 2 1.1.1 Running Example . 3 1.2 Healthcare Processes . 4 1.2.1 A Distinction . 4 1.2.2 Characteristics . 5 1.3 Relevance . 6 1.3.1 Process Characteristics Challenges . 7 1.3.2 Event Log Quality . 7 1.3.3 Data Collection and Integration . 8 1.4 Research Questions . 8 1.5 Contributions . 9 1.6 Structure of the Thesis . 10 2 Process Mining 13 2.1 Event Log as Starting Point for Process Mining . 14 2.1.1 Extended Running Example . 15 2.1.2 Data Quality Issues . 16 2.1.3 Event Log Maturity Levels . 17 2.2 Types of Process Mining . 18 2.2.1 Process Discovery . 19 2.2.2 Conformance Checking . 22 2.2.3 Process Enhancement . 24 2.3 Mining Different Perspectives . 24 2.3.1 Control-Flow . 24 2.3.2 Time . 25 2.3.3 Organizational . 25 ix 2.3.4 Case . 26 2.4 Standardized Event Log Representation . 26 2.5 Recent Developments in Process Mining . 28 3 Interoperability and Healthcare Data Standards 29 3.1 Interoperability . 30 3.1.1 Technical Interoperability . 31 3.1.2 Semantic Interoperability . 31 3.1.3 Process Interoperability . 32 3.2 Standards Development Organizations . 32 3.2.1 DICOM . 32 3.2.2 HL7 . 32 3.2.3 IHE . 33 3.3 Healthcare Data Exchange Standards . 33 3.3.1 FHIR . 36 3.4 Terminology Systems . 36 3.4.1 ICD-10 . 37 3.4.2 RadLex . 37 3.4.3 SWIM Lexicon . 38 3.4.4 LOINC . 39 3.4.5 SNOMED CT . 39 3.5 Integration Profiles . 40 3.5.1 ATNA . 40 3.5.2 SOLE . 42 3.6 Discussion . 43 4 State of Process Mining in Healthcare 45 4.1 Other Reviews . 46 4.2 Literature Review Methodology . 47 4.2.1 Selection of Clinically-relevant Case Studies . 48 4.2.2 Process Mining Aspects . 50 4.2.3 Clinical Aspects and Standard Coding Schemes . 50 4.3 Results of the Review . 50 4.3.1 Selected Case Studies . 50 4.3.2 Process Mining Aspects . 50 4.3.3 Clinical Aspects Using Standard Clinical Descriptors . 52 4.4 Conclusion . 54 4.4.1 Reporting Basic Characteristics of the Event Log Data . 55 4.4.2 Adopting the Use of Standard Clinical Descriptors . 55 4.4.3 The Need for a Reporting Template . 56 4.5 Reporting Template Outline . 57 5 Mining Audit Trails 59 5.1 Standardized Audit Logging . 60 x 5.1.1 IHE Audit Message Semantics . 61 5.1.2 HL7 FHIR AuditEvent Resource . 63 5.2 Direct Mapping Approach . 63 5.2.1 Transformation Architecture . 63 5.2.2 Test Setting . 64 5.2.3 Audit Messages for the Running Example . 66 5.2.4 Transformation Result . 68 5.2.5 Visualization . 69 5.2.6 Discussion and Issues . 69 5.3 Data Warehouse Approach . 70 5.3.1 OpenSLEX Meta Model . 71 5.3.2 Mapping and Integration . 72 5.3.3 Discussion and Issues . 75 5.4 Process Mining Interface . 76 5.4.1 Simulate . 77 5.4.2 Store & Provide . 78 5.4.3 Analyze . 80 5.4.4 Test Results . 81 5.4.5 Discussion and Issues . 82 5.5 Discussion . 84 5.5.1 Data Quality in Audit Logs . 84 6 Compliance Checking 87 6.1 Melanoma Surveillance . 88 6.2 Characteristics of the Case Study . 89 6.3 Methodology . 90 6.3.1 Data Preparation . 90 6.3.2 Time Boxing . 91 6.3.3 Conformance Checking . 92 6.4 Results of the Case Study . 92 6.4.1 Data Preparation . 93 6.4.2 Conformance Checking . 94 6.4.3 Applied Process Discovery . 96 6.5 Discussion . 97 6.5.1 Reuse of Clinical Data for Process Mining . 97 6.5.2 Events with Time Constraints Spanning a Long Period of Time 99 6.5.3 Medical Implications . 99 6.5.4 Guideline Compliance Measurement . 101 7 Conclusions and Outlook 103 7.1 Research Questions Revisited . 103 7.2 Impact and Future ..
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