True analytical errors occur within the laboratory and are usually the result of operator or instrument error. Analytical errors are often ≤10% of all errors in diagnostic testing, and the frequency of these types of errors have decreased in the last decade.
Analytical errors and increased data variability may result from instrument malfunctions, inability to follow up proper procedures, undetected failures in quality control, sample misidentification, and/or test interference. Errors in the analytical phase are very important because they lead to inaccurate test results that may harm patients as well as increase the cost of business.
The analytical phase has fewer challenges as compared to pre-analytical, but they can be very damaging and cost a life too. It is important to standardize the analytical phase of lab operations and very necessary to understand the challenges that we often encounter or neglect due to which the process can be error-prone.
Data can be very useful to troubleshoot these challenges. Therefore, we should focus on what these challenges are and how to tackle them, to keep the workflow smooth and running.
Incorrect results due to inability to follow proper laboratory procedures can be due to -
- The unexpected delay in sample processing - The analytical phase of studies begins upon the receipt of the sample within the clinical pathology laboratory. Timely processing of the submitted sample is an important factor for correct results. Delays in centrifugation and/or removal of the serum from the cells can result in alterations in the concentration of several analytes.
- Incorrectly printed barcode on the sample tube - can lead to missing out tests or wrong test selection or sample mixups.
- Instrument interfacing issues also play a very important role. The value transferred from one end to another will be erroneous.
- A very little volume of sample in vacutainer issues or reagent tube - If the volume is way below the required limit, the pipette may not pick up sample properly which can lead to erroneous report/value.
- Test systems not properly calibrated.
- Undetected failure of quality control, frequency of running quality control needs to be defined based on sample workload and working.
- Quality control data and machine maintenance - Before samples arrive in the lab, it is important to evaluate machine preparation and maintenance to keep challenges at bay. Quality control data can help to know if there is any problem with machines or are ready for testing.
- Reporting of results when controls are out of range.
- Reagent stored inappropriately.
- Linearity and dilution errors can give you invalid or misleading test results affecting the patient treatment. A proper understanding of dilution is important to not only get correct results but save cost too.
- Analytical phase optimization- We should also look into making the system lean - which sample to be run early. For example, a vitamin D test takes keeps the machine occupied for a longer time. By organizing the tests based on their processing TAT (analytical TAT) we can optimise the analytical process. The biggest challenge in any outpatient laboratory is that the majority of the samples come around 1.30 pm -2 pm; especially those which are picked from a doctor’s clinic or healthcare setting. It takes time for them to reach the lab. These clinics re-open at 6 pm in the evening and require reports at that time. So the laboratory has only 3-6 hours of processing time for such samples. Thus it’s important to ensure that processes are well documented tracked and monitored well or else turnaround time gets badly affected. When laboratories know how to operate with the analytical flow systematically, there is a positive impact on the post-analytical phase as well.
In an automated lab, a good laboratory information management software should be able to red flag these errors and create alerts for the technician. There are four basic strategies that work to prevent errors: education, standardization, mistake-proofing, and streamlining. Lab technicians must be properly trained to do the jobs.
Well-written laboratory testing procedures, validation of laboratory instruments and assays, strong quality control programs, and proper education and training of laboratory professionals are practices that will decrease analytical errors and reduce data variability. All good quality LIMS should be able to handle all of the above and help the lab to deliver quality results and services.
Get solutions for post-analytical workflow - by Dr Gaur.