Extending a Data Management Maturity Model for Process Mining in Healthcare

Andreas Erhard, Klaus Arthofer, Emmanuel Helm

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

Abstract

Background: Many components must work together to continuously improve processes in healthcare organizations. Process mining has recently developed into a discipline that can make a significant contribution here. Objectives: We want to extend an existing management tool to assess and improve the capability of organizations in this area. Method: We add a dimension to the adoption readiness assessment and maturity model for sharable clinical pathways to assess and improve event data quality. Results: We present different approaches for formal and checkpoint assessments and an embedding of the improvement strategy with examples. Conclusion: The additional dimension from the process mining domain integrates with the existing model. At all levels, links can be established between the various aspects of event data quality with existing dimensions. The model has yet to be tested in a real-world use case.

Original languageEnglish
Title of host publicationdHealth 2023 - Proceedings of the 17th Health Informatics Meets Digital Health Conference
EditorsBernhard Pfeifer, Gunter Schreier, Martin Baumgartner, Dieter Hayn
PublisherIOS Press BV
Pages192-197
Number of pages6
ISBN (Electronic)9781643683867
DOIs
Publication statusPublished - 2 May 2023
Event17th Annual Sonference on Health Informatics Meets Digital Health, dHealth 2023 - Vienna, Austria
Duration: 16 May 202317 May 2023

Publication series

NameStudies in Health Technology and Informatics
Volume301
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference17th Annual Sonference on Health Informatics Meets Digital Health, dHealth 2023
Country/TerritoryAustria
CityVienna
Period16.05.202317.05.2023

Keywords

  • BPM+ Health
  • Data Management Maturity Model
  • Event Data
  • OMG
  • Process Mining
  • RAMM
  • Health Facilities
  • Delivery of Health Care
  • Data Mining/methods
  • Data Management
  • Organizations

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