Original Research

Using laboratory data to categorise CD4 laboratory turn-around-time performance across a national programme

Lindi-Marie Coetzee, Naseem Cassim, Deborah K. Glencross
African Journal of Laboratory Medicine | Vol 7, No 1 | a665 | DOI: https://doi.org/10.4102/ajlm.v7i1.665 | © 2018 Lindi-Marie Coetzee, Naseem Cassim, Deborah K. Glencross | This work is licensed under CC Attribution 4.0
Submitted: 04 August 2017 | Published: 28 June 2018

About the author(s)

Lindi-Marie Coetzee, National Health Laboratory Service, National Priority Programme, Johannesburg; Department of Molecular Medicine and Haematology, University of the Witwatersrand, Johannesburg, South Africa
Naseem Cassim, National Health Laboratory Service, National Priority Programme, Johannesburg; Department of Molecular Medicine and Haematology, University of the Witwatersrand, Johannesburg, South Africa
Deborah K. Glencross, National Health Laboratory Service, National Priority Programme, Johannesburg; Department of Molecular Medicine and Haematology, University of the Witwatersrand, Johannesburg, South Africa


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Abstract

Background and objective: The National Health Laboratory Service provides CD4 testing through an integrated tiered service delivery model with a target laboratory turn-around time (TAT) of 48 h. Mean TAT provides insight into national CD4 laboratory performance. However, it is not sensitive enough to identify inefficiencies of outlying laboratories or predict the percentage of samples meeting the TAT target. The aim of this study was to describe the use of the median, 75th percentile and percentage within target of laboratory TAT data to categorise laboratory performance.

 

Methods: Retrospective CD4 laboratory data for 2015–2016 fiscal year were extracted from the corporate data warehouse. The laboratory TAT distribution and percentage of samples within the 48 h target were assessed. A scatter plot was used to categorise laboratory performance into four quadrants using both the percentage within target and 75th percentile TAT. The laboratory performance was labelled good, satisfactory or poor.

 

Results: TAT data reported a positive skew with a mode of 13 h and a median of 17 h and 75th percentile of 25 h. Overall, 93.2% of CD4 samples had a laboratory TAT of less than 48 h. 48 out of 52 laboratories reported good TAT performance, i.e. percentage within target > 85% and 75th percentile ≤ 48 h, with two categorised as satisfactory (one parameter met), and two as poor performing laboratories (failed both parameters).

 

Conclusion: This study demonstrated the feasibility of utilising laboratory data to categorise laboratory performance. Using the quadrant approach for TAT data, laboratories that need interventions can be highlighted for root cause analysis assessment.


Keywords

CD4; HIV; turnaround time

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