Literature DB >> 19302996

The reporting, classification and grading of quality failures in the medical laboratory.

Maurice O'Kane1.   

Abstract

Laboratory errors have a reported frequency of 0.012 to 0.6% of all test results and given the central role of the laboratory in diagnosis, may have a major adverse impact on patient care. Laboratories have been at the forefront of efforts to enhance patient safety through a range of improvements such as increased automation of manual processes and analytical quality control programmes. It is important that all laboratories have active systems in place to identify and monitor quality failures. This will be facilitated by a systems based approach to error which seeks to identify and correct weaknesses in policies/procedures rather than to apportion blame. Quality failures may be classified by cause [i.e., step in the testing pathway where the problem occurred] and graded by severity on a 5 point scale. The severity grading score measures both the Actual impact ['A' score] on patient outcome and the Potential impact ['P' score] i.e., the worst case possible outcome that might have resulted. 'A' scores tend to be skewed towards low adverse patient impact while 'P' scores are skewed towards high adverse impact. This confirms the need for ongoing vigilance by laboratories. This classification/grading system is easy to implement and represents a valuable additional tool for monitoring performance.

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Year:  2009        PMID: 19302996     DOI: 10.1016/j.cca.2009.03.023

Source DB:  PubMed          Journal:  Clin Chim Acta        ISSN: 0009-8981            Impact factor:   3.786


  10 in total

1.  Role of intervention on laboratory performance: evaluation of quality indicators in a tertiary care hospital.

Authors:  Rachna Agarwal; Sujata Chaturvedi; Neelam Chhillar; Renu Goyal; Ishita Pant; Chandra B Tripathi
Journal:  Indian J Clin Biochem       Date:  2012-01-07

Review 2.  Managing the pre- and post-analytical phases of the total testing process.

Authors:  Robert Hawkins
Journal:  Ann Lab Med       Date:  2011-12-20       Impact factor: 3.464

3.  Evaluation of analytical performance of Alinity i system on 31 measurands.

Authors:  Jong Do Seo; Da Young Song; Youngwon Nam; Chihchiao Li; Seunghwan Kim; Joon Hee Lee; Kyunghoon Lee; Junghan Song; Sang Hoon Song
Journal:  Pract Lab Med       Date:  2020-10-20

Review 4.  Measurement of errors in clinical laboratories.

Authors:  Rachna Agarwal
Journal:  Indian J Clin Biochem       Date:  2013-03-26

5.  The effect of phlebotomy training on blood sample rejection and phlebotomy knowledge of primary health care providers in Cape Town: A quasi-experimental study.

Authors:  Mumtaz Abbas; Fidele K Mukinda; Mosedi Namane
Journal:  Afr J Prim Health Care Fam Med       Date:  2017-04-13

6.  Chronometric vs. Structural Hypercoagulability.

Authors:  Carmen Delianu; Mihaela Moscalu; Loredana Liliana Hurjui; Claudia Cristina Tărniceriu; Oana-Viola Bădulescu; Ludmila Lozneanu; Ion Hurjui; Ancuta Goriuc; Zinovia Surlari; Liliana Foia
Journal:  Medicina (Kaunas)       Date:  2020-12-28       Impact factor: 2.430

7.  False elevations in urinary metanephrines: under-recognised pitfall with 24-hour urinary volume collection.

Authors:  Terry Shin; Thanh Duc Hoang; Mary Thomas Plunkett; Mohamed K M Shakir
Journal:  BMJ Case Rep       Date:  2021-02-04

Review 8.  How to conduct External Quality Assessment Schemes for the pre-analytical phase?

Authors:  Gunn B B Kristensen; Kristin Moberg Aakre; Ann Helen Kristoffersen; Sverre Sandberg
Journal:  Biochem Med (Zagreb)       Date:  2014-02-15       Impact factor: 2.313

9.  Diagnostic Errors and Laboratory Medicine - Causes and Strategies.

Authors:  Mario Plebani
Journal:  EJIFCC       Date:  2015-01-27

10.  Risk analysis and assessment based on Sigma metrics and intended use.

Authors:  Yong Xia; Hao Xue; Cunliang Yan; Bowen Li; ShuQiong Zhang; Mingyang Li; Ling Ji
Journal:  Biochem Med (Zagreb)       Date:  2018-06-15       Impact factor: 2.313

  10 in total

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