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Making sense of a haemolysis monitoring and reporting system: a nationwide longitudinal multimethod study of 68 Australian laboratory participant organisations.
Clinical Chemistry and Laboratory Medicine : CCLM 2018 March 29
BACKGROUND: The key incident monitoring and management systems (KIMMS) quality assurance program monitors incidents in the pre- and postanalytical phases of testing in medical laboratories. Haemolysed specimens have been found to be the most frequent preanalytical error and have major implications for patient care. The aims of this study were to assess the suitability of KIMMS for quality reporting of haemolysis and to devise a meaningful method for reporting and monitoring haemolysis.
METHODS: A structured survey of 68 Australian KIMMS laboratory participant organisations was undertaken. Quarterly haemolysis reports (2011-2014) were analysed.
RESULTS: Among 110 million accessions reported, haemolysis rates varied according to the reporting methods that participants used for assigning accessions (16% of participants reported haemolysis by specimen and 83% reported by episode) and counting haemolysis rejections (61% by specimen, 35% by episode and 3% by test). More than half of the participants (56%) assigned accessions by episode and counted rejections by specimen. For this group, the average haemolysis rate per 100,000 episodes was 177 rejected specimens with the average rate varying from 100 to 233 over time. The majority of participants (91%) determined rejections using the haemolysis index. Two thirds of participants (66%) recorded the haemolysis manually in laboratory information systems.
CONCLUSIONS: KIMMS maintains the largest longitudinal haemolysis database in the world. However, as a means of advancing improvements in the quality of the preanalytical laboratory process, there is a need to standardise reporting methods to enable robust comparison of haemolysis rejection rates across participant laboratories.
METHODS: A structured survey of 68 Australian KIMMS laboratory participant organisations was undertaken. Quarterly haemolysis reports (2011-2014) were analysed.
RESULTS: Among 110 million accessions reported, haemolysis rates varied according to the reporting methods that participants used for assigning accessions (16% of participants reported haemolysis by specimen and 83% reported by episode) and counting haemolysis rejections (61% by specimen, 35% by episode and 3% by test). More than half of the participants (56%) assigned accessions by episode and counted rejections by specimen. For this group, the average haemolysis rate per 100,000 episodes was 177 rejected specimens with the average rate varying from 100 to 233 over time. The majority of participants (91%) determined rejections using the haemolysis index. Two thirds of participants (66%) recorded the haemolysis manually in laboratory information systems.
CONCLUSIONS: KIMMS maintains the largest longitudinal haemolysis database in the world. However, as a means of advancing improvements in the quality of the preanalytical laboratory process, there is a need to standardise reporting methods to enable robust comparison of haemolysis rejection rates across participant laboratories.
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