Application of process mining to assess the data quality of routinely collected time-based performance data sourced from electronic health records by validating process conformance.
Effective and accurate use of routinely collected health data to produce Key Performance Indicator reporting is dependent on the underlying data quality. In this research, Process Mining methodology and tools were leveraged to assess the data quality of time-based Emergency Department data sourced f...
| Publicado en: | Health Informatics Journal Vol. 22; no. 4; pp. 1017 - 1030 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Dec2016
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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=119546248&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 119546248 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14604582 EJK jtl: Health Informatics Journal issn: 14604582 maglogo: Y pubinfo: dt: Dec2016 vid: 22 iid: 4 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 119546248 119546248 119546248 10.1177/1460458215604348 119546248 ppf: 1017 ppct: 13 formats: tig: atl: Application of process mining to assess the data quality of routinely collected time-based performance data sourced from electronic health records by validating process conformance. aug: au: Perimal-Lewis, Lua Teubner, David Hakendorf, Paul Horwood, Chris affil: Flinders University of South Australia, Australia sug: subj: Data Mining Patient Record Systems Australia Hospital Policies Clinical Indicators Emergency Service Australia Human Australia Research, Medical Software Record Review Quality Assurance ab: Effective and accurate use of routinely collected health data to produce Key Performance Indicator reporting is dependent on the underlying data quality. In this research, Process Mining methodology and tools were leveraged to assess the data quality of time-based Emergency Department data sourced from electronic health records. This research was done working closely with the domain experts to validate the process models. The hospital patient journey model was used to assess flow abnormalities which resulted from incorrect timestamp data used in time-based performance metrics. The research demonstrated process mining as a feasible methodology to assess data quality of time-based hospital performance metrics. The insight gained from this research enabled appropriate corrective actions to be put in place to address the data quality issues. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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