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How do we use the data from pre-analytical quality indicators and how should we?

  
@article{JLPM4334,
	author = {Janne Cadamuro and Martin Gaksch and Cornelia Mrazek and Elisabeth Haschke-Becher and Mario Plebani},
	title = {How do we use the data from pre-analytical quality indicators and how should we?},
	journal = {Journal of Laboratory and Precision Medicine},
	volume = {3},
	number = {4},
	year = {2018},
	keywords = {},
	abstract = {It is impossible to imagine a modern medical laboratory without a structured quality management system in place. Nevertheless, this system usually focuses mostly on the analytical part of the total testing process (TTP), despite being aware of the fact that most errors occur in the pre- and post-analytical phases. There are many reasons possibly explaining this fact, one of them being that extra-analytical processes are hard to grasp, since they occur mostly outside of the laboratory and include many different parties. Despite these obstacles, it is mandatory to keep all steps of TTP, potentially influencing the analytical test result or their interpretation, under meticulous quality control and to improve them as much as possible, involving all health care workers contributing to sample quality. Additionally, it is necessary to collect and evaluate all data in a standardized manner, not only within one laboratory, but nationwide and internationally. This is a perquisite for valid data comparison over time (trend analysis) and benchmarking, the basis for threshold definition and continuous improvement. A widely accepted method to meet all these requirements is the concept of quality indicators (QIs). In this review, we provide an overview over this topic and we discuss an easy to use and freely available tool, aiming to aid laboratories in data collection and comparison: The QIs project of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Working Group on “Laboratory errors and patient safety” (WG-LEPS). Additionally, we provide practical suggestions on how to successfully implement QIs within any laboratory.},
	issn = {2519-9005},	url = {https://jlpm.amegroups.org/article/view/4334}
}