In the clinical laboratory, the term “Quality” refers to the standard of one lab’s results compared to others. Generally, every lab or test system can be made more efficient, and labs seeking a good standard of quality will need to engage in ongoing process evaluation and improvement.
Good quality is like a reputation; it takes a long time to build, but it can be ruined in an instant.
What is the difference between good quality and poor quality? In simple terms, a good quality (GQ) test system will produce very few adverse events, errors, regulatory failures, and so on. Conversely, poor quality (PQ) will result in more adverse events and failures, which will ultimately need to be remedied.
Both GQ and PQ have associated costs, and the overall cost of quality in the lab is calculated by adding the cost of good quality and the cost of poor quality.
Costs of good quality
Maintaining a GQ test system incurs costs associated with prevention of adverse effects and test system review. Some examples include:
- Staff training: Staff must be trained to carry out their roles effectively, as well as implement cost-saving practices, such as precision in work, reduction of waste, etc.
- Equipment maintenance: Equipment and instruments should have preventative maintenance carried out, so as to avoid any potential downtime.
- Quality Management System (QMS): The QMS should be continually reviewed and optimized. Labs should keep abreast of the latest developments in QMS and adopt new process improvement strategies (e.g., Six Sigma).
- Regulatory body accreditation: Labs can pay for a test system audit by a recognized regulatory body. Achieving accreditation is proof of the lab’s commitment to quality.
Costs of poor quality
While the costs associated with GQ are incurred with a view to prevent potential issues, costs of PQ are incurred as a result of test system failure. Where GQ costs are relatively easy to anticipate and account for, PQ costs can be more complex. For instance, PQ costs can come from internal or external sources:
- Repeated testing: The most obvious issue with PQ is the need to re-test both QC and patient samples when errors are detected. This can be extremely difficult to do in the case of patient samples, and has the added effects of increased cost and reduced patient/physician confidence in the lab’s results.
- Failure analysis: This involves troubleshooting and root cause analysis. Labs may spend a significant amount of resources in determining the root cause of an adverse incident and implementing systems to prevent reoccurrence.
- Misdiagnoses and inappropriate treatment: The most severe outcome from erroneous lab results is the potential for misdiagnosis and inappropriate patient treatment, which can have severe medical repercussions for the patient and significant cost/reputation implications for the lab due to malpractice litigation.
When we compare GQ and PQ, it is plain to see that costs associated with GQ are much easier to forecast and plan for, whereas PQ costs are much more scenario-dependent. Both GQ and PQ have costs associated, but the main difference is that labs will spend money on GQ practices and waste money correcting issues arising from PQ practices—a key distinction.
Examples of poor quality practices
Case Study 1. A lab was using QC material with non-human source material for its entire Immunoassay panel, despite ISO 15189 and CLIA recommendations to use QC material with a matrix as close to the patient sample as possible. Due to matrix effects associated with the non-human material in the QC, the lab had to reassign QC values upon every reagent batch change due to significant shifts in QC performance, resulting in unnecessary time and QC wastage.
Case Study 2. In an effort to consolidate and save money, a lab decided to use a three-point calibrator, provided by the instrument manufacturer, to carry out its protein linearity verification. As three levels is the minimum regulatory requirement, the lab believed its linearity verification was sufficient. Upon proficiency testing (PT), lab leaders realized that they were misreporting results at the high end of the assay range, as their three-point linearity verifier did not adequately measure this range. The lab then had to acquire a new six-point linearity verifier, then re-run all patient samples reported at the high end of the assay range.
Making quality improvements
There are many methods of improving quality in the lab. The most obvious methods are to ensure staff are adequately trained, and that all materials and instruments are properly maintained and of sufficiently high quality. Some key areas to improve quality include using high quality QC and external quality assessment (EQA), utilizing peer group reporting software, and using comprehensive calibration linearity verification.
- High quality QC and EQA: As per ISO 15189 and CLIA recommendations to use a control matrix as close as possible to the patient sample, labs should endeavour to use QC and EQA material which is manufactured with 100 percent human source material.
- Peer group reporting software: Peer group reporting (PGR) software can be used to further optimize QC performance. QC performance can be compared to a global peer group of labs using the same reagent method and lot of QC, giving a firm indication of result quality. Some PGR software also automatically calculates statistics such as Six Sigma, which can provide an indication of the efficiency of each individual assay, and efforts can be made to improve tests showing poor performance.
- Linearity verification: It is important to recognize that meeting regulatory requirements isn’t just an exercise in checking off boxes. Linearity verification should adequately test the entire assay range of a specific assay/instrument. For this reason, labs should aim to use a linearity verifier which uses a 5/6 point calibration and tests the entire assay range.
“Prevention is better than cure” is a statement frequently used in medicine and healthcare, and it also applies to quality in the laboratory. Prevention of adverse incidents is preferable to implementing corrective actions once an issue occurs.
The cost of PQ is unpredictable and often substantial. It stands to reason that the more logical approach would be to invest heavily in GQ practices, ultimately decreasing the risk of suffering the significant implications of PQ.
Edward Hill, BSc, serves as a QC Product Specialist for Randox Laboratories, provider of Laboratory Quality Controls, Peer Group Reporting Software, and other diagnostic solutions for clinical, food/wine testing, veterinary, forensic and molecular laboratories.
