Literature DB >> 22357604

Implementing a laboratory automation system: experience of a large clinical laboratory.

Choong Weng Lam1, Edward Jacob.   

Abstract

Laboratories today face increasing pressure to automate their operations as they are challenged by a continuing increase in workload, need to reduce expenditure, and difficulties in recruitment of experienced technical staff. Was the implementation of a laboratory automation system (LAS) in the Clinical Biochemistry Laboratory at Singapore General Hospital successful? There is no simple answer, so the following topics comparing and contrasting pre- and post-LAS have been explored: turnaround time (TAT), laboratory errors, and staff satisfaction. The benefits and limitations of LAS from the laboratory experience were also reviewed. The mean TAT for both stat and routine samples decreased post-LAS (30% and 13.4%, respectively). In the 90th percentile TAT chart, a 29% reduction was seen in the processing of stat samples on the LAS. However, no significant difference in the 90th percentile TAT was observed with routine samples. It was surprising to note that laboratory errors increased post-LAS. Considerable effort was needed to overcome the initial difficulties associated with adjusting to a new system, new software, and new working procedures. Although some of the known advantages and limitations of LAS have been validated, the claimed benefits such as improvements in TAT, laboratory errors, and staff morale were not evident in the initial months.

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Mesh:

Year:  2012        PMID: 22357604     DOI: 10.1177/2211068211430186

Source DB:  PubMed          Journal:  J Lab Autom        ISSN: 2211-0682


  4 in total

1.  The optimization of total laboratory automation by simulation of a pull-strategy.

Authors:  Taho Yang; Teng-Kuan Wang; Vincent C Li; Chia-Lo Su
Journal:  J Med Syst       Date:  2014-12-04       Impact factor: 4.460

2.  Key Performance Indicators to Measure Improvement After Implementation of Total Laboratory Automation Abbott Accelerator a3600.

Authors:  Marijana Miler; Nora Nikolac Gabaj; Lora Dukic; Ana-Maria Simundic
Journal:  J Med Syst       Date:  2017-12-27       Impact factor: 4.460

3.  A SNP profiling panel for sample tracking in whole-exome sequencing studies.

Authors:  Reuben J Pengelly; Jane Gibson; Gaia Andreoletti; Andrew Collins; Christopher J Mattocks; Sarah Ennis
Journal:  Genome Med       Date:  2013-09-27       Impact factor: 11.117

4.  Greater Efficiency Observed 12 Months Post-Implementation of an Automatic Tube Sorting and Registration System in a Core Laboratory.

Authors:  Fatma Ucar; Gonul Erden; Mine Yavuz Taslipinar; Gulfer Ozturk; Zeynep Ginis; Erdem Bulut; Namik Delibas
Journal:  J Med Biochem       Date:  2015-12-30       Impact factor: 3.402

  4 in total

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