Extending a Data Management Maturity Model for Process Mining in Healthcare.
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 cap...
| Publicado en: | Studies in Health Technology & Informatics Vol. 301; pp. 192 - 198 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=163652035&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 163652035 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2023 vid: 301 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 163652035 163652035 163652035 10.3233/SHTI230038 163652035 ppf: 192 ppct: 6 formats: tig: atl: Extending a Data Management Maturity Model for Process Mining in Healthcare. aug: au: ERHARD, Andreas ARTHOFER, Klaus HELM, Emmanuel affil: University of Applied Sciences Upper Austria School, of Informatics, Communications and Media, 4232 Hagenberg, Austria sug: subj: Data Management Models, Theoretical Data Mining Health Care Industry Quality Improvement Hospital Information Systems Medical Informatics Human Data Quality Critical Path Workflow Organizational Policies Privacy and Confidentiality Knowledge Skill Acquisition Organizational Change Quality of Health Care Clinical Documentation Improvement Funding Source ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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