Prevention of extra-analytical phase errors by non-analytical automation in clinical laboratory

Introduction

This review article aims to introduce the importance of laboratory error prevention based on non-analytical automation. First, the error sources/errors in the extra-analytical phase of the clinical laboratory will be detailed. Secondly, some possible improvements in these errors by non-analytical automation will be focused on. Finally, the conclusion will be included. Table 1 shows some definitions of clinical laboratory automation and the issue, which the reader can consult for clearance.

Automation comprises extra-analytical and analytical processes, with the latter being developed earlier than the former [1]. Clinical laboratory automation has improved such laboratory processes as specimen labeling, sorting, transport, processing, loading on the analyzers, analysis, storage, and archiving [2]. Although the analytical and non-analytical automation has developed asynchronously, clinical laboratory performances have dramatically changed to a satisfactory level today [3].

Previously, the term automation was used for clinical chemistry analyzers to describe the processes with only minimal involvement of an analyst. The automation, for the past two decades, has also covered extra-analytical as well as analytical procedures. It is well known that extra-analytical error sources dominate those of analytical systems and have become substantial for the efficiency of clinical laboratories [1], [4], [5]. Since a high-quality test result can be defined as the fact that it must be accurate and be reported faster, i.e., short turnaround time (TAT), the value of extra-analytical technologies besides the automated analyzers may be appreciated well. Consequently, the implementation of automatable processes to the extra-analytical phase will become crucial, considering the improvement of clinical laboratory performance and patient’s sample safety [6].

Extra-analytical and analytical components have recently been integrated or interfaced with each other to construct fully integrated modular laboratory automation. The current developments in non-analytical automation provide error elimination, improve the quality, and reduce the labor, costs, and TAT [3]. On the one hand, a wide range of variables is included in the extra-analytical phase, such as test order and sample collection, delivery to the laboratory, handling, and processing before analysis. On the other hand, the pre-analytical part of the extra-analytical-phase covers all processes outside and inside the clinical laboratory or begins with test order and ends with analyzer entrance of the processed samples. Similarly, the post-analytical part of the extra-analytical phase comprises auto-verification, recapping, automated specimens archiving and storage, decapping in the case of a rerun, and secondary specimen sorting for off-line analyzers [1], [3], [7].

In short, extra-analytical processes summarized in Table 2 can be automated by non-analytical instrumentation in the clinical laboratory together with some limitations depending on the facilities. Either of the following automation can automate some of these processes: (a) stand-alone pre-analytical systems (single-function or multifunction) [24], [25], [26], [27], [28] and (b) integrated pre- and post-analytical systems [1], [3], [27], [29], [30], [31], [32]. Consequently, we think that many extra-analytical processes, within and outside the clinical laboratory, have nowadays been or may in the future, be automated. Resultantly, the extra-analytical phase management may prevent some errors, resulting in the total quality of laboratory diagnostics and high-quality test results, as considered in the following sections. For this reason, the review has been intended especially for laboratory professionals, managers, and directors for the sake of patient safety and will help improve the TTP in the laboratory.

1. Error sources/errors in the extra-analytical phase of clinical laboratory.

In the clinical laboratory, each step in total testing processes (TTP) may be affected by laboratory errors. However, a large body of evidence has shown that the extra-analytical phase (both intra- and extra-laboratory) are more prone to errors than the analytical phase, as mentioned earlier [7], [25], [26], [27], [28], [29], [30], [31]. Fortunately, those kinds of errors have been able to be prevented partly by the introduction of some technological and computer-aided informatic solutions to that phase. Several studies have shown that after the implementation of stand-alone pre-analytical systems to clinical laboratories, the errors in the conventional pre-analytical steps have been considerably reduced [32], [33], [34].

Similarly, some of the technological and computer-aided bioinformatic solutions have been able to be implemented to the extra-laboratory part of the non-analytical phase, resulting in partial automation in that phase. On the other hand, the extra-laboratory part of the extra-analytical phase is not under the control of laboratory staff since other non-laboratory health care stuff execute the processes at this phase, covering several processes, including test order and specimen collection, identification, labeling, handling, tracing, and transporting. Consequently, laboratory professionals and staff cannot directly control and are not responsible for that phase’s errors. However, it is of critical importance for the laboratory professionals to partly standardize and control the extra-laboratory steps [29], [34], [35], [36]. By using bioinformatic technology and robotics, thousands of clinical laboratories worldwide could have the opportunity to reduce errors caused by pre-analytical processes, resulting in improvement in accuracy and TTP. Unfortunately, there are limited numbers of studies focusing on the error improvements by implemented tools. Before the implementation of such automation, each laboratory does need justification for demonstrable improvements. However, it is clear that non-analytical automation effectively reduces the number of laboratory errors occurring during TTP [31], [32].

Lippi et al. [7] summarized major sources of pre-analytical variability, associated with patient preparation, sample collection, sample transportation, sample preparation for analysis, and sample storage. Under these main categories, Lippi et al. detailed 20 different sub-variables. The clinicians and the patients are generally interested in total testing error and timeliness (i.e., TAT), which are the basis of test quality or the measure of performance of the clinical laboratory. Automation of analytical and extra-analytical phases of laboratory processes enhanced clinician and patient satisfaction, which is because prompt, electronic reporting of test results and critical value alerting by bioinformatics as well as test quality are of critical importance [28], [37]. Thus, TAT improvement may be considered as a quality indicator (QI), a measure of laboratory performance. The implementation of performance measurements must evaluate TTP.

For this reason, the guidelines and regulations on the clinical laboratory test performance encourage the laboratories to improve all steps of the TTP as well as the analytical phase [38]. International Standard for Accreditation of Clinical Laboratories (ISO 15189:2012) prompts the laboratorians to establish QIs to assess clinical laboratory performance and to reduce the errors caused by the extra-analytical phase. The standard similarly states that the laboratory shall establish QIs to monitor and evaluate performance throughout critical aspects of pre-examination, examination, and post-examination processes. It also suggests the process of monitoring QIs be planned, which includes establishing the objectives, methodology, interpretation, limits, action plan, and duration of measurement [39], [40], [41], [42], [43].

In the application of QIs to all clinical laboratories thoroughly, one can have difficulties or discrepancies since a laboratory’s QIs assessment belongs to its establishment and not applicable to other laboratories. Resultantly, a harmonization of the usage of IQs is needed. For this purpose, the Working Group on Laboratory Errors and Patient Safety (WG-LEPS) of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) planned a project in 2008 for establishing the Model of QI (MQI) [44], [45]. The project was targeted to define a list of MQIs and to determine the data collection procedures and quality specifications for laboratory result evaluation. In succeeding years, the MQIs were discussed and approved in the 2013 Padova Consensus Conference and were tested on different occasions. Thus, the resultant MQI covers 53 QIs, which are generally key processes. Of them, 39 are related to the extra-analytical (pre- and post-analytical) phase, which is of critical importance. The MQIs have been listed by classification based on TTP phases. Besides, the significance of a given QI and data collection difficulty determine its order of priority, grading from 1 to 4 (one the highest, four the lowest priority). The QIs, for example, with priority 1 is mandatory and the first to be put into practice [46]. MQIs of critical processes associated with the pre-analytical phase include misidentification errors, test transcription errors, incorrect sample type, incorrect fill level, unsuitable samples for transportation and storage problems, sample hemolysed, and inappropriate time in sample collection, and almost all of which have priority 1. On the other hand, MQIs of critical processes associated with post-analytical phase processes encompass inappropriate TATs, incorrect laboratory reports, notification of critical values, and results notification (TAT), with the majority having priority 1 as well.

After the implementation of automation to the extra-analytical phase, the MQI ratios will become low when compared with those of non-automated processes, since MQIs have rather good match with extra-analytical phase errors. A study describing the comparison of the pre- and post-automation QI evaluation of the same clinical laboratory would be valuable and show the importance of non-analytical automation.

1. Improvements in extra-analytical phase errors by non-analytical automation.

The non-analytical automation, within and outside the clinical laboratory, will substantially lessen the effect of the error sources in TTP and enhance the quality of the test results, resulting in an accurate result(s) and faster test result reporting. For the implementation of an MQI, a QI form is prepared first, which includes many characteristics such as identification code, method of data collection, a method for data processing, the goal for improvement activities, and so on. The classification of the QI in that form is also marked as Efficiency, Structure, Effectiveness, Activity/process, Timeliness (TAT), Results, Pre-analytical phase, Intra-analytical phase, Post-analytical phase, Safety of the staff, Outcome, Support processes, and competence. There may be a relationship between some of these classification items (e.g., Timeliness, Preanalytical phase, Post-analytical phase, and Safety of the staff) and the improvements executed by non-analytical automation tools. On the other hand, there is a good match between pre-analytical error types and their amelioration by non-analytical automation. Similarly, there is a considerable overlap of pre-analytical error types with MQIs. The majority of the non-analytical automation steps are closely associated with increased patient and healthcare staff safety.

Lippi, G. et al. reported the pre-analytical error types [41]. We divided these error types into two categories: intra- and extra-laboratory extra-analytical errors. Two tables were instructed (Tables 3 and 4), describing the errors and their prevention. The extra-laboratory errors occur in the pre-pre-analytical phase, and intra-laboratory errors in the intra-aboratory pre- and post-analytical phases. Each error type mentioned in Tables 3 and 4 were illuminated by a detailed comment or explanation about the probable improvements provided by non-analytical automation at the respective steps for lessening the error ratio. We can summarize this situation as follows.

Ten of 12 extra-laboratory extra-analytical errors in Table 3 and all intra-laboratory non-analytical errors in Table 4 may be prevented by automation. İmprovements can be summarized as follows, which are detailed in two tables. Missing sample and/or test request is prevented by computerized physician order entry and query-host communication. Positive patient identification by bioinformatics prevents wrong/missing identification of the patients. In vitro hemolysis may partly be averted by adaptor usage, using automated venous sampling systems, and using devices to find the veins or real-time digital imagers for sampling. All are closely associated with both patient and healthcare staff safety and high-quality testing. Similarly, the tube labeler and preparer tool hampers the wrong container issue, related to correct patient ID and tube labeling for phlebotomy and meaning patient safety. Adaptor usage and using the containers with a pre-determined vacuum volume, which is possible, hampers insufficient sample. During sampling, an adaptor is used for sampling, and complete filling of the tube vacuum lasted, precluding inappropriate blood to anticoagulant ratio. Inappropriate transport and storage conditions may be put an end to by PTS, which is closely associated with improved TAT and high-quality testing. The tube labeler and preparer restrains poor label alignment on the specimen container, especially in outpatients, reducing TAT. Automated centrifugation prevents inappropriate centrifugation conditions, shortening TAT, and providing staff safety.

The automated specimen storage and retrieval system anticipates the inappropriate storage condition of the samples in the posttest phase, meaning sample safety and improved TAT. An automatic tube loader and sorter usage can avert the delay in sorting and submission of the specimens, reducing TAT, achieving staff safety, and providing efficient specimen traceability. A delay in sample processing steps in the laboratory may be restricted by primary tube usage, which shortens TAT and provides direct storage within the automated cabinet. Volume/clotting/bubble sensors contained in pre-analytical automation systems counteract the errors originated from inadequate sample integrity, improving the test results, and TAT. Some errors coming from different manual processes are prevented by the module of decapper, aliquoter, bar-code labeler, and recapper contained in pre-analytical automation systems, which is associated with staff safety and TAT reduction. Impairment in secondary sample traceability is avoided by secondary tube sorter module of intra-laboratory non-analytical automation, relating to patient safety and reduced TAT. Delayed test reporting is counteracted by automatic verification, meaning improved TAT. Process–controlling software managing the system precludes the deceleration in total laboratory automation, resulting in high-quality test reporting.

Table 5 summarizes extra-analytical errors, improvements by automation, and improved parameters, including TAT, test result quality, patient safety, and staff safety. These four parameters are principal factors determining the performance of a clinical laboratory, proving the importance of non-analytical automation for high-quality laboratory performance.

The management and standardization of non-analytical laboratory processes, especially of pre-analytical ones, have been primarily achieved thanks recently to automation, either by stand-alone devices or those integrated with analytical automation, as well as by informatics and computer sciences. Non-analytical automated systems should be implemented in the laboratory processes. For this purpose, a risk management strategy should first be developed for the systematic analysis of the present laboratory’s workflow and bottlenecks. Then, the measurements of improvements can be executed by the pathways of QIs and outcome measures. Non-analytical automation within and outside the clinical laboratory will standardize as possible as the processes in that phase. The standardization of a process within TTP will necessarily make it possible to control, to trace, to measure, to evaluate, and to improve it, providing the integration of TTP with maximal effectiveness, and all factors improved result in a high-quality test result reporting.

Conclusion

The considerations mentioned above suggest that the clinical laboratory automation and computer sciences have rendered non-analytical laboratory processes mostly manageable and standardized the automation technologies have added a serious impact on the proficiency of laboratory medicine. The laboratory processes need the implementation of a non-analytical automated system. A risk management strategy, for this reason, should first be developed for the systematic analysis of the present laboratory’s workflow and bottlenecks. Then, the application of QIs for different steps allows measuring the improvements in TTP. Several manual processes have been automated by instrumentations, resulting in improvements in standardization, organization, efficiency, and quality of TTP. Consequently, non-analytical automation, within and outside the clinical laboratory, will permanently reduce the effect of the error sources in TTP and enhance the test result quality. More studies, or QI-oriented measurements, should be executed on the improvements in extra-analytical steps by non-analytical automation improvements, achieving accurate and faster test result reporting.