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dc.contributor.authorBadrick, Tony
dc.contributor.authorGay, Stephanie
dc.contributor.authorMackay, Mark
dc.contributor.authorSikaris, Ken
dc.date.accessioned2020-02-17T01:20:50Z
dc.date.available2020-02-17T01:20:50Z
dc.date.issued2018
dc.identifier.issn1434-6621
dc.identifier.doi10.1515/cclm-2017-0219
dc.identifier.urihttp://hdl.handle.net/10072/391548
dc.description.abstractBackground: The determination of reliable, practical Quality Indicators (QIs) from presentation of the patient with a pathology request form through to the clinician receiving the report (the Total Testing Process or TTP) is a key step in identifying areas where improvement is necessary in laboratories. Methods: The Australasian QIs programme Key Incident Monitoring and Management System (KIMMS) began in 2008. It records incidents (process defects) and episodes (occasions at which incidents may occur) to calculate incident rates. KIMMS also uses the Failure Mode Effects Analysis (FMEA) to assign quantified risk to each incident type. The system defines risk as incident frequency multiplied by both a harm rating (on a 1–10 scale) and detection difficulty score (also a 1–10 scale). Results: Between 2008 and 2016, laboratories participating rose from 22 to 69. Episodes rose from 13.2 to 43.4 million; incidents rose from 114,082 to 756,432. We attribute the rise in incident rate from 0.86% to 1.75% to increased monitoring. Haemolysis shows the highest incidence (22.6% of total incidents) and the highest risk (26.68% of total risk). “Sample is suspected to be from the wrong patient” has the second lowest frequency, but receives the highest harm rating (10/10) and detection difficulty score (10/10), so it is calculated to be the 8th highest risk (2.92%). Similarly, retracted (incorrect) reports QI has the 10th highest frequency (3.9%) but the harm/difficulty calculation confers the second highest risk (11.17%). Conclusions: TTP incident rates are generally low (less than 2% of observed episodes), however, incident risks, their frequencies multiplied by both ratings of harm and discovery difficulty scores, concentrate improvement attention and resources on the monitored incident types most important to manage.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherWalter de Gruyter & Co. KG Publishers
dc.relation.ispartofpagefrom264
dc.relation.ispartofpageto272
dc.relation.ispartofissue2
dc.relation.ispartofjournalClinical Chemistry and Laboratory Medicine (CCLM)
dc.relation.ispartofvolume56
dc.subject.fieldofresearchClinical sciences
dc.subject.fieldofresearchCognitive and computational psychology
dc.subject.fieldofresearchcode3202
dc.subject.fieldofresearchcode5204
dc.subject.keywordsScience & Technology
dc.subject.keywordsLife Sciences & Biomedicine
dc.subject.keywordsMedical Laboratory Technology
dc.subject.keywordsFailure Mode Effects Analysis (FMEA)
dc.subject.keywordspost-analytical error
dc.titleThe key incident monitoring and management system - history and role in quality improvement
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationBadrick, T; Gay, S; Mackay, M; Sikaris, K, The key incident monitoring and management system - history and role in quality improvement, Clinical Chemistry and Laboratory Medicine (CCLM), 2018, 56 (2), pp. 264-272
dcterms.dateAccepted2017-06-29
dc.date.updated2020-02-17T01:16:58Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2018 Walter de Gruyter & Co. KG Publishers. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
gro.hasfulltextFull Text
gro.griffith.authorBadrick, Tony C.


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