True analytical errors in clinical laboratory occur and are usually the result of operator or instrument errors. Types of analytical errors in laboratory are often ≤10% but frequency has decreased in the last decade in diagnostic testing.
Analytical errors in laboratory 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. The analytical phase errors are very important because they lead to inaccurate test results that may harm patients as well as increase the cost of business.
Type of analytical Errors
The analytical phase errors have fewer challenges as compared to the pre-analytical, but they can be very damaging and cost a life too. It is important to standardize the analytical phase in laboratory operations and very necessary to understand the challenges that we often encounter or neglect due to which the process can be error-prone.
Clinical laboratory data analytics can be very useful to troubleshoot these challenges. Therefore, we should focus on these challenges and how to tackle them, to keep the workflow smooth and running.
Incorrect results due to the 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 or removal of the cell serum 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 the sample properly which can lead to an erroneous report/value.
- Test systems are not properly calibrated.
- Undetected failure of quality control and frequency of running quality control needs to define 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 in clinical laboratory data analytics 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’s treatment. A proper understanding of dilution is important to not only get correct results but save costs too.
- Analytical phase in laboratory 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 optimize the common analytical laboratory system process. The biggest challenge in any outpatient laboratory is that the majority of the samples come around 1.30 pm -2 pm. Most of those 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 and monitored or else turnaround time gets badly affected. There is a positive impact in the post-analytical phase when laboratories know how to operate with the analytical flow
In an automated lab, 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.
Conclusion
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 common analytical laboratory system errors and reduce data variability. All good quality LIMS help the lab to deliver quality results and services by handling errors.
Get solutions for post-analytical workflow – by Dr Gaur.
