Process Mining of Nursing Routine Data: Cool, but also Useful?...16th Annual Conference on Health Informatics meets Digital Health (dHealth 2022), May 24–25, 2022, Vienna, Austria.
Background: Process mining is a promising field of data analytics that is yet to be applied broadly in healthcare. It can streamline the care process, leading to a higher quality of care, increased patient safety and lower costs. Objectives: To get deeper insights into the emergence and detectabilit...
| Publicado en: | Studies in Health Technology & Informatics Vol. 293; pp. 137 - 145 |
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| Autores principales: | , , , , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2022
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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=157083009&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157083009 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2022 vid: 293 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 157083009 157083009 157083009 10.3233/SHTI220360 157083009 ppf: 137 ppct: 8 formats: tig: atl: Process Mining of Nursing Routine Data: Cool, but also Useful?...16th Annual Conference on Health Informatics meets Digital Health (dHealth 2022), May 24–25, 2022, Vienna, Austria. aug: au: JONK, Julian SCHALLER, Michael NETZER, Michael PFEIFER, Bernhard AMMENWERTH, Elske HACKL, Werner affil: UMIT TIROL – Private University for Health Sciences, Medical Informatics and Technology, Institute of Medical Informatics, Hall in Tirol, Austria. sug: subj: Data Mining Nursing Informatics Delirium Diagnosis Psychiatric Patients In Old Age Human Data Analytics Hospital Units Prospective Studies Quality of Nursing Care Retrospective Design Record Review Aged Congresses and Conferences Austria Austria Aged: 65+ years ab: Background: Process mining is a promising field of data analytics that is yet to be applied broadly in healthcare. It can streamline the care process, leading to a higher quality of care, increased patient safety and lower costs. Objectives: To get deeper insights into the emergence and detectability of delirium in a gerontopsychiatric setting. Methods: We use process mining to create process models from routinely collected, anonymised nursing data from two gerontopsychiatric wards. We analyse these models to get a longitudinal view of the care processes. Results: The process models comprise all activities during patients’ stays but are too extensive and challenging to interpret due to the wide variation in care paths. Although the models give insight into frequent paths and activities, they are insufficient to explain the emergence of delirium meaningfully. No apparent difference between stays with or without delirium could be detected. Conclusion: Conducting process mining on routinely collected data is easy, but the interpretation of the results was a challenge. We identified four limitations associated with using this data and gave recommendations on adapting it for further analysis. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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