Are we there yet?—why is the adoption of patient-based real-time quality control taking so long?

The advantages of using patient-based real-time quality control (PBRTQC) are well documented and flow from the commutability of the samples, the early detection of error and the lack of false positive flags inherent in conventional quality control (QC) (1,2). One of the principal reasons laboratories want to implement PBRTQC is because of an error in a method not detected by conventional QC.

The use of PBRTQC in clinical chemistry has become more viable because of the increased volume of patient samples being routinely run in laboratories, the development of middleware and instrument-based software capable of supporting it, and a better understanding of the theory of setting its parameters (3,4).

However, adoption into routine laboratory practice has been slow (5). Several reasons could be suggested for this:

• Most laboratorians who have heard of PBRTQC do not understand the algorithms and how to optimise them for their patient populations. Many different algorithms are available, and each monitored assay may be more suited to a different calculation (3). There is freely available simulation software that can provide this support (6,7).
• PBRTQC algorithms and parameters must be optimised using simulation. Applying a generic model or parameters will produce a suboptimal model with high false positives and poor error detection. The simulation must be used with the patient population to identify the optimal algorithm and PBRTQC parameters (block size, exclusion, truncation and error detection limits) (6). Some algorithms can be used with semi-qualitative assays (8,9).
• There is a lack of knowledge of how fluctuations in the patient population can impact PBRTQC. These include sex-related measurands such as creatinine, pre-analytical problems such as hemolysis, and laboratory arrival patterns for specific patient groups such as inpatients, clinics, and critical care patients (10).
• There is a lack of understanding of the problems associated with conventional QC or the work required to introduce PBRTQC (11). The laboratory must understand its patient population(s), analytical systems, and laboratory software limitations (12).

Some laboratories have experienced ineffective implementations because of a lack of flexibility in the software. For example, in many cases, only an average of normal algorithm is available, which can produce too many false positive flags or take too long to identify a true positive. This leads to frustration and reticence in determining the true problem behind the failure. Algorithms that are more robust to outliers, such as a moving median or trimmed mean, should be selectable for different analytes based on their distributions. Similarly, transformation (e.g., log or Box-Cox) of raw data before the application of the algorithm should be available. In addition to flexible algorithms, tools within existing manufacturer software to test and validate PBRTQC strategies on historical data and compare them with internal QC performance would also be beneficial. Laboratories successfully implementing PBRTQC have primarily had the resources to develop customised software. Improvement of existing manufacturer middleware as described would likely improve the utilisation of PBRTQC.

The following are key steps to setting up PBRTQC effectively:

• Understand which assays are best to start investigating the implementation of PBRTQC
  • It is best to start with a few measurands with tight biological control, such as potassium, calcium or sodium. These measurands are often clinically the most important to monitor.
• Understand the patient population at each facility for those assays
  • Selecting measurands with less biological variation due to age, sex, and seasonal variation.
• Determine the appropriate algorithm to use for the assay
  • Use available simulation packages to assist.

Unreal expectations

Many laboratories want to be able to turn on PBRTQC with little effort and immediately see gains (13).

There is a lot of fine-tuning that needs to be undertaken to get the correct QC limits for PBRTQC (13). If the limits are too wide, it can result in false negatives and miss issues. Having limits that are too narrow can result in false positives, which can be very disruptive to workflow, especially if PBRTQC is tied to auto-validation workflow.

Unnecessary concerns

Most regulatory bodies do not have an official stance on using PBRTQC. Many potential users worry about whether their local accreditation agent will accept using PBRTQC to reduce the amount of QC needed. Laboratories are hesitant to implement it because of this. PBRTQC is an acceptable approach to QC for ISO 15189 clause 7.2.7.2c (9) and College of American Pathologists accreditation, which requires two levels of QC daily.

According to Everett’s diffusion theory, the five stages of adopting new ideas and technology are knowledge/awareness, persuasion, decision, implementation and confirmation/continuation (14). The concepts of PBRTQC are not new and have been used in hematology for decades (15). However, the idea is new in clinical chemistry. PBRTQC is also an active area of research and development, with newer algorithms incorporating Artificial intelligence being described (15,16); however, these models have yet to be widely validated (16,17). So perhaps the profession is at the decision phase of this technology adoption (5). Thus, the requirement is to understand the advantages and disadvantages of PBRTQC over conventional QC.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was a standard submission to the journal. The article has undergone external peer review.

Peer Review File: Available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-24-54/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jlpm.amegroups.com/article/view/10.21037/jlpm-24-54/coif). T.B. serves as an unpaid editorial board member of Journal of Laboratory and Precision Medicine from February 2023 to January 2027. P.D. is the owner and director of PRD Consulting Inc which provides consulting to medical laboratories on workflow and middleware implementation. The other author has no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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