Pal Bela Szecsi1, Lars Ødum. 1. Department of Clinical Biochemistry, University of Copenhagen, Roskilde Hospital, Roskilde, Denmark. palsze01@geh.regionh.dk
Abstract
BACKGROUND: We report our results for the systematic recording of all errors in a standard clinical laboratory over a 1-year period. METHODS: Recording was performed using a commercial database program. All individuals in the laboratory were allowed to report errors. The testing processes were classified according to function, and errors were classified as pre-analytical, analytical, post-analytical, or service-related, and then further divided into descriptive subgroups. Samples were taken from hospital wards (38.6%), outpatient clinics (25.7%), general practitioners (29.4%), and other hospitals. RESULTS: A total of 1189 errors were reported in 1151 reports during the first year, corresponding to an error rate of 1 error for every 142 patients, or 1 per 1223 tests. The majority of events were due to human errors (82.6%), and only a few (4.3%) were the result of technical errors. Most of the errors (81%) were pre-analytical. Of the remainder, 10% were analytical, 8% were post-analytical, and 1% was service-related. Nearly half of the errors (n=550) occurred with samples received from general practitioners or clinical hospital wards. Identification errors were relatively common when non-technicians collected blood samples. CONCLUSIONS: Each clinical laboratory should record errors in a structured manner. A relation database is a useful tool for the recording and extraction of data, as the database can be structured to reflect the workflow at each individual laboratory.
BACKGROUND: We report our results for the systematic recording of all errors in a standard clinical laboratory over a 1-year period. METHODS: Recording was performed using a commercial database program. All individuals in the laboratory were allowed to report errors. The testing processes were classified according to function, and errors were classified as pre-analytical, analytical, post-analytical, or service-related, and then further divided into descriptive subgroups. Samples were taken from hospital wards (38.6%), outpatient clinics (25.7%), general practitioners (29.4%), and other hospitals. RESULTS: A total of 1189 errors were reported in 1151 reports during the first year, corresponding to an error rate of 1 error for every 142 patients, or 1 per 1223 tests. The majority of events were due to human errors (82.6%), and only a few (4.3%) were the result of technical errors. Most of the errors (81%) were pre-analytical. Of the remainder, 10% were analytical, 8% were post-analytical, and 1% was service-related. Nearly half of the errors (n=550) occurred with samples received from general practitioners or clinical hospital wards. Identification errors were relatively common when non-technicians collected blood samples. CONCLUSIONS: Each clinical laboratory should record errors in a structured manner. A relation database is a useful tool for the recording and extraction of data, as the database can be structured to reflect the workflow at each individual laboratory.
Authors: Diana L Santos Ferreira; Hannah J Maple; Matt Goodwin; Judith S Brand; Vikki Yip; Josine L Min; Alix Groom; Debbie A Lawlor; Susan Ring Journal: Metabolites Date: 2019-04-03