Common Pre-analytical Mistakes and Ways to Solve Them by Dr. Gaur

Dr Ravi Gaur, Principal Advisor Spice Health & Ex-Director & Chair Medical Advisory Committee, Oncquest laboratories Ltd, shares his expertise about the unnoticed mistakes that often take place in the pre-analytical phase of testing.

Dr. Ravi Gaur

Dr. Ravi Gaur

CrelioHealth for Diagnostics (formerly LiveHealth)
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Data is vital to resolve different problems in a laboratory management process. For the pre-analytical process, there are several steps involved. If we have enough knowledge on how to refine these steps to avoid errors with the help of the right data at the right time, we can reduce the rate of error in the pre-analytical phase of operations itself.

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Evidence-based medicine has now become the main principle guiding clinical practice and playing an important role in laboratory medicine. This results in helping the clinicians to make crucial decisions based on test results leading to better health outcomes.

Dr. Ravi Gaur (Director & Chair Medical Advisory Committee, Oncquest laboratories Ltd) shares his expertise and suggests-

Quality is never an accident. It is always a result of intelligent efforts. The total testing process of a patient’s blood from ordering – collection - reporting and finally communicating to the treating physician has three broad components-

  • Pre-analytical
  • Analytical
  • Post-analytical

Errors can arise at any step and lead to a faulty report generation that can affect patient care like misdiagnosis, and improper treatment. The pre-analytical phase which consists of specimen collection, transport and sample preparation for processing is the most important step and involves variables that are not under the control of the laboratory.

Most errors affecting laboratory test results occur in the pre-analytical phase (50 –75% of total errors). Thus it is important to keep a strong eye on this step of the analysis. One of the most important factors in this step is capturing correct data.

Labs must be aware of the data that involves a few of the crucial information like -


  • Time of sample collection and the time it reaches the processing lab, also known as the pre-analytical TAT for a specific test - The analytical results can vary based on the time taken from collection to processing. The more the delay in sample reaching the lab, the bigger can be the variation and which can lead to erroneous results. This can happen especially in coagulation assays, urine cultures, electrolytes etc.
  • Patient preparation - Information of fasting /non-fasting state is important to interpret correct values, mainly in the assay of lipids, vitamins and many enzymes. At times phlebos collect samples without asking the patient’s fasting status and this is not even mentioned in the patient requisition form. On analysis, we can find elevated values and thus interpretation becomes challenging. If this info is captured correctly, then such issues can be addressed well.
  • History of the patient- The patient’s health history especially for medication, including the dose of the drug taken is equally essential. For example, if a patient is under thyroid medication, the values for the test might come out to be either low or high. Then, we have to repeat the sample, because the report is not correlating. If you have the history of the patient, it might help you interpret the report better; and prevent reruns. Similar is for anticoagulants, vitamins, and any other drug therapies.
  • Sample transportation method - Here, apart from just transportation, the temperature can affect testing quality as well. For example, sometimes for semen analysis, samples are carried in a plastic bag - which is not supposed to be done. Semen is supposed to be transported at room temperature and not in ice and should reach the lab within an hour of collection. Ideally, it should be collected in the lab facility itself.
  • Sample preparation before analysis - An equally important step. Accessioning, barcoding application of acceptance /rejection criteria, centrifugation speed etc. are few very important factors that should be captured as data in the record to detect errors in time.
  • Patient demography - Key aspects such as age and sex should be captured correctly as this helps in proper result interpretation and clinical correlation.
  • Patient identification - Identification of the patient is equally important right from sample collection to processing and communication. A wrong identification can lead to fatal consequences.

There are many such finer things, and which are overlooked several times which further can lead to testing errors. If we capture data at every step with a proper checklist and analyse them in time, most of these pre-analytical errors can be easily avoided.

If you have the data, you can always say okay we found so and so variation and based on this variation we will be able to help our team improvise to prevent errors. If such things are not available for the team to learn, the only option we are left with is to repeat the sample which can lead to critical delays, in addition, to an increase in lab operating cost.

Know more about data analysis for repeat samples or redraws to improve the preanalytical process, in the coming blogs.

To conclude, every step howsoever small, it may be must be looked at in the pre-analytical phase to prevent redraws and avoid errors of interpretation. I believe, if we have relevant data for it, we can have better graphics or charts then better will be our understanding of errors.

The maximum errors happen in the pre-analytical phase itself; thus, needless to say, that data capturing plays an important role to prevent these errors. Having access to the right and relevant data can help each lab team member grow, prevent errors, save rerun efforts & costs and benefit patients a lot.

We will talk about the analytical and post-analytical phases in the next article.