Integration of ISO15189 and external quality assurance data to assist the detection of poor laboratory performance in New South Wales

dc.contributor.authorLidbury, Brett
dc.contributor.authorKoerbin, G
dc.contributor.authorRichardson, Alice
dc.contributor.authorBadrick, Tony
dc.date.accessioned2019-07-24T05:55:39Z
dc.date.issued2018
dc.date.updated2019-03-31T07:21:25Z
dc.description.abstractA systematic survey of the peer-reviewed literature was conducted to identify the key international themes that govern laboratory quality management. Informed by the survey results, predictive models utilising assessment data against the ISO 15189 standard, and external quality assurance programme (EQA) data, were assessed. Via PubMed, a systematic survey (SS) of the international pathology quality literature identified more than 100 articles, which were subjected to text-mining and meta-analyses via R statistical programming. Word patterns were examined for indicators of current best practice in quality assurance. Random Forest and ANCOVA models were subsequently developed with data obtained from twenty-one anonymous pathology laboratories in NSW. The SS and text-mining did not show a consistent international consensus for laboratory quality; however, approximately 15% of articles suggested root cause analysis as a means to investigate quality problems. Using the Random Forest algorithm, an integrated ISO 15189 – EQA model was developed, with results further supported by ANCOVA. The combined Random Forest – ANCOVA method succeeded in identifying EQA markers (e.g., serum potassium) that correlated with ISO 15189 audit results, providing a robust predictive model of laboratory quality monitoring superior to that proposed for root cause analyses.en_AU
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.issn0031-3025en_AU
dc.identifier.urihttp://hdl.handle.net/1885/164686
dc.language.isoen_AUen_AU
dc.publisherTaylor & Francis Groupen_AU
dc.sourcePathologyen_AU
dc.titleIntegration of ISO15189 and external quality assurance data to assist the detection of poor laboratory performance in New South Walesen_AU
dc.typeJournal articleen_AU
local.bibliographicCitation.issue1en_AU
local.bibliographicCitation.startpageS92en_AU
local.contributor.affiliationLidbury, Brett, College of Health and Medicine, ANUen_AU
local.contributor.affiliationKoerbin, G, NSW Health Pathologyen_AU
local.contributor.affiliationRichardson, Alice, College of Health and Medicine, ANUen_AU
local.contributor.affiliationBadrick, Tony, Royal College of Pathologists of Australasia Quality Assurance Programs (RCPAQAP)en_AU
local.contributor.authoremailu3756893@anu.edu.auen_AU
local.contributor.authoruidLidbury, Brett, u3756893en_AU
local.contributor.authoruidRichardson, Alice, u3767151en_AU
local.description.embargo2037-12-31
local.description.notesImported from ARIESen_AU
local.identifier.absfor110316 - Pathologyen_AU
local.identifier.absfor111799 - Public Health and Health Services not elsewhere classifieden_AU
local.identifier.absseo920203 - Diagnostic Methodsen_AU
local.identifier.ariespublicationu4102339xPUB321en_AU
local.identifier.citationvolume50en_AU
local.identifier.doi10.1016/j.pathol.2017.12.253en_AU
local.identifier.uidSubmittedByu4102339en_AU
local.publisher.urlhttps://www.routledge.com/en_AU
local.type.statusPublished Versionen_AU

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