Automation In Clinical Laboratory Research Articles

The next wave of innovation in laboratory automation: systems for auto-verification, quality control and specimen quality assurance.

Laboratory automation in clinical laboratories has made enormous differences in patient outcomes, with a wide range of tests now available that are accurate and have a rapid turnaround. Total laboratory automation (TLA) has mechanised tube handling, sample preparation and storage in general chemistry, immunoassay, haematology, and microbiology and removed most of the tedious tasks involved in those processes. However, there are still many tasks that must be performed by humans who monitor the automation lines. We are seeing an increase in the complexity of the automated laboratory through further platform consolidation and expansion of the reach of molecular genetics into the core laboratory space. This will likely require rapid implementation of enhanced real time quality control measures and these solutions will generate a significantly greater number of failure flags. To capitalise on the benefits that an improved quality control process can deliver, it will be important to ensure that an automation process is implemented simultaneously with enhanced, real time quality control measures and auto-verification of patient samples in middleware. Therefore, it appears that the best solution may be toautomate those critical decisions that still require human intervention and therefore include quality control as an integral part of total laboratory automation.

An algorithmic approach to the use of rapid molecular diagnostics for SARS-CoV-2, influenza viruses, and other respiratory pathogens during an unprecedented respiratory season

Abstract Introduction/Objective The COVID-19 pandemic exacerbated deficiencies of testing personnel, reagents, supplies and disposables, instruments, and automation in many clinical laboratories. Upon entering respiratory season, a strategy was warranted to optimize laboratory resources when supplies were already limited and expected respiratory season test volume was unknown. An algorithm was devised to prioritize test ordering and TAT based on patient clinical scenario. Methods/Case Report The institutional respiratory season SARS-CoV-2 algorithm was constructed by a multidisciplinary team including infectious disease, infection prevention, laboratory, and IT/LIS leadership. CDC guidance on influenza testing was incorporated. Antigen-based testing was discontinued; only molecular amplification- based platforms with FDA EUA were utilized. Platforms had a range of TAT (20 minutes to 8 hours) and included fully- automated high throughput, rapid random access, point-of-care, and CDC SARS-CoV-2 assays. Test bundles included SARS-CoV-2 (monoplex), or SARS-CoV-2 + fluA&B (triplex), or SARS-CoV-2 + respiratory pathogen panel (multiplex RPP; includes 22 targets, including flu A&B). Results (if a Case Study enter NA) Key factors in the algorithm included whether the patient was outpatient or inpatient, hospital employee or not, symptomatic or not, immunocompetent or immunocompromised, and whether a concurrent order for other respiratory pathogens was included or not. Clinician responses for these factors determined the type of swab collected (wet swab in VTM or dry swab) and how quickly the TAT was indicated for a given patient using a colored-dot sticker system. Priority TAT in decreasing order was symptomatic inpatients, asymptomatic pre- procedure patients, asymptomatic admissions, symptomatic employees, and symptomatic outpatients. Conclusion An algorithm for respiratory pathogen testing during an unprecedented respiratory season prioritizes result TAT to an individual patient’s clinical situation while maximizing laboratory stewardship by eliminating redundant influenza testing and requiring ‘all upfront’ orders to avoid add-on orders that require ‘dumpster diving’ for samples. Limitations include inherent differences in sensitivity, LOD, and specificity when multiple different platforms are utilized to detect the same analytes.

Using machine learning to identify clotted specimens in coagulation testing.

A sample with a blood clot may produce an inaccurate outcome in coagulation testing, which may mislead clinicians into making improper clinical decisions. Currently, there is no efficient method to automatically detect clots. This study demonstrates the feasibility of utilizing machine learning (ML) to identify clotted specimens. The results of coagulation testing with 192 clotted samples and 2,889 no-clot-detected (NCD) samples were retrospectively retrieved from a laboratory information system to form the training dataset and testing dataset. Standard and momentum backpropagation neural networks (BPNNs) were trained and validated using the training dataset with a five-fold cross-validation method. The predictive performances of the models were then assessed based on the testing dataset. Our results demonstrated that there were intrinsic distinctions between the clotted and NCD specimens regarding differences in the testing results and the separation of the groups (clotted and NCD) in the t-SNE analysis. The standard and momentum BPNNs could identify the sample status (clotted and NCD) with areas under the ROC curves of 0.966 (95% CI, 0.958-0.974) and 0.971 (95% CI, 0.9641-0.9784), respectively. Here, we have described the application of ML algorithms in identifying the sample status based on the results of coagulation testing. This approach provides a proof-of-concept application of ML algorithms to evaluate the sample quality, and it has the potential to facilitate clinical laboratory automation.

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

Abstract During previous decades, significant improvements in laboratory errors have become a substantial part of reducing preventable diagnostic errors. In clinical laboratory practice, the errors in the testing process are primarily associated with extra-analytical phase error sources, influencing the test result quality profoundly. Thus, the management of these critical error sources makes their effects preventable thanks to automation and computer sciences. The implementation of non-analytical automated systems requires a risk management strategy based on laboratory’s workflow and bottlenecks. Then, the improvements can be measured and evaluated by the usage of quality indicators (QI). Consequently, the total quality of laboratory diagnostics and higher patient safety is closely dependent on this type of automation. This review will help laboratory professionals, managers, and directors improve the total testing processes (TTP). The automation technologies have added a serious impact on the proficiency of laboratory medicine. Several instrumentations have now partially or entirely automated many manual tasks to improve standardization, organization, efficiency, and TTP quality. The implementation of non-analytical automation has made them manageable. As a result, non-analytical automation within and outside the clinical laboratory will necessarily lessen the error sources’ effect on the total test process, enhancing the quality of the test results.

Efficacy of Pooled Serum Internal Quality Control in Comparison with Commercial Internal Quality Control in Clinical Biochemistry Laboratory.

Introduction With increasing automation in clinical laboratories, the requirements for quality control (QC) material have greatly increased in order to monitor performance. The constant use of commercial control material is not economically feasible for many countries because of nonavailability or the high-cost of those materials. Therefore, preparation and use of in-house QC serum will be a very cost-effective measure with respect to laboratory needs. Materials and Methods In-house internal quality control from leftover serum samples of master health checkup subjects, which have been screened negative for HIV, HCV and HBsAg antibodies was pooled in a glass jar with ethanediol as preservative and kept in deep freezer at − 20°C. From the pooled serum, 100 microliter thirty aliquots were prepared. Every day along with commercial internal QC (IQC), one aliquot of pooled serum was analyzed for 30 days for the following parameters: plasma glucose, blood urea, serum creatinine, total cholesterol, triglycerides (TGL), high-density lipoprotein, calcium, total protein, albumin, total bilirubin, AST, ALT, ALP, amylase. After getting 30 values for each parameter, mean, standard deviation (SD) and CV% were calculated for both IQC commercial sample and pooled serum sample. Results The mean, SD, and CV% of glucose, cholesterol, TGL, calcium, alanine aminotransaminase (ALT), aspartate aminotransferase (AST), amylase, and alkaline phosphatase (ALP) were statistically significant between pooled serum and commercial QC. Conclusion In-house QC prepared from pooled serum is better than commercial internal QC. The biochemical parameters were stable in pooled serum due to less matrix effect; also, variation was less in pooled serum IQC.

MENGENAL SISTEM PENERANGAN LABORATORIUM/LIS

Clinical laboratory as a supporting tool to establish diagnostic as well as the efficiency of laboratory results will need on time report,accurate result, and satisfaction of the customer should necessary supported by suitability equipments. Most laboratories using automaticmachine need the assistance of LIS (Laboratory Information System) to enhance good results. To prepare its ready use of these laboratoryinstruments, request orders of the physicians' should be explicit and content satisfaction of the clinically symptoms as well. The laboratorypersonnel and the supervisor of LIS software should know well how to operate it to match with the other laboratory equipment used.The result of laboratory's orders should be recorded by LIS and send back to the physicians. In this computerisation world, automationof clinical laboratory is necessary if efficient results are the main need.

Evaluation on the performance of clinical laboratory automation system

Objective By analysis of the key performance indexes of the clinical laboratory automation system, to clarify the advantage and optimize the comprehensive performance of the laboratory automation system. Methods Key performance indexes were Collected from January 2017 to April 2017 in biochemistry and immunoassay group of Clinical Laboratory of Shanghai Tong Ren Hospital.(1)The data were collected and compared by the before-and-after method, the starting time of the automation system and initial sample test were analyzed.(2) Key performance indexes were analyzed for the time of specimen registration, inspection, and reporting. (3) The specimen turnaround time (TAT) was analyzed based on two months operation of the laboratory automation system. In view of disadvantage of infectious assays, setting up priority sample absorption, then TAT performance was re-evaluated. (4) By the assessment of total serum dosage required in the automation system, the number of blood vacuum tubes were reduced reasonably. The pros and cons of laboratory automation system were analyzed and the potential improvement were proposed. Results (1)According to the sample peak shift forward, the system start time could move forward 30 minutes earlier.(2)With the adopting of railway logistics, the specimens were sent to the lab and the registration time was at 7∶25 am, and the time required for specimen delivery was greatly reduced which made specimen test, report and audit time all moved forward accordingly. (3)Data has shown that specimen TAT declined dramatically based on the performance of the first two month operation of the automation system, biochemical items were shortened 2 h, and the immunoassay shortened 4 h, respectively. Moreover the trend keeps better gradually. With setting up priority absorption infectious tests, the TAT was improved greatly, TAT reduced the average by 40 min. (4)500 μl (including the sample in dead space of vacuum tube) were needed for all the 65 biochemical items included in the system, and 1 495 μl serum were used for the 28 immunoassay. As a result, a total of 2 000 μl serum will be enough for sample analysis by the system, which provided the feasibility to reduce 3 vacuum tubes averagely . Considering the current automation system does not include all the analysis items in our lab directory, a few tests remain to be performed on offline instruments respectively. The methodology for some infectious agents are different from previous method, therefore some test results may need a period of time for comprehensive clinical appreciation. Furthermore, due to the parallel connection of multiple instruments included in the system, more rigorous and frequent quality control becomes a necessity, which may rely on more strict quality control procedure to guarantee the quality. Conclusions The application of the automation system significantly enhanced the efficiency of clinical laboratory all round. In addition, by the quantitative indicators, it is possible to monitor the system operation performance real time, which may feedback and facilitate the improvement constantly, and result in auto confirmation the majority results, eventually. (Chin J Lab Med, 2017, 40: 810-815) Key words: Automation, laboratory; Clinical laboratory information systems; Data interpretation, statistical; Evaluation studies

Nonanalytic Laboratory Automation: A Quarter Century of Progress.

Clinical laboratory automation has blossomed since the 1989 AACC meeting, at which Dr. Masahide Sasaki first showed a western audience what his laboratory had implemented. Many diagnostics and other vendors are now offering a variety of automated options for laboratories of all sizes. Replacing manual processing and handling procedures with automation was embraced by the laboratory community because of the obvious benefits of labor savings and improvement in turnaround time and quality. Automation was also embraced by the diagnostics vendors who saw automation as a means of incorporating the analyzers purchased by their customers into larger systems in which the benefits of automation were integrated to the analyzers.This report reviews the options that are available to laboratory customers. These options include so called task-targeted automation-modules that range from single function devices that automate single tasks (e.g., decapping or aliquoting) to multifunction workstations that incorporate several of the functions of a laboratory sample processing department. The options also include total laboratory automation systems that use conveyors to link sample processing functions to analyzers and often include postanalytical features such as refrigerated storage and sample retrieval.Most importantly, this report reviews a recommended process for evaluating the need for new automation and for identifying the specific requirements of a laboratory and developing solutions that can meet those requirements. The report also discusses some of the practical considerations facing a laboratory in a new implementation and reviews the concept of machine vision to replace human inspections.

Clinical Laboratory Automation: A Case Study.

BackgroundThis paper presents a case study of an automated clinical laboratory in a large urban academic teaching hospital in the North of Italy, the Spedali Civili in Brescia, where four laboratories were merged in a unique laboratory through the introduction of laboratory automation.Materials and MethodsThe analysis compares the preautomation situation and the new setting from a cost perspective, by considering direct and indirect costs. It also presents an analysis of the turnaround time (TAT). The study considers equipment, staff and indirect costs.ResultsThe introduction of automation led to a slight increase in equipment costs which is highly compensated by a remarkable decrease in staff costs. Consequently, total costs decreased by 12.55%. The analysis of the TAT shows an improvement of nonemergency exams while emergency exams are still validated within the maximum time imposed by the hospital.ConclusionsThe strategy adopted by the management, which was based on re-using the available equipment and staff when merging the pre-existing laboratories, has reached its goal: introducing automation while minimizing the costs.Significance for public healthAutomation is an emerging trend in modern clinical laboratories with a positive impact on service level to patients and on staff safety as shown by different studies. In fact, it allows process standardization which, in turn, decreases the frequency of outliers and errors. In addition, it induces faster processing times, thus improving the service level. On the other side, automation decreases the staff exposition to accidents strongly improving staff safety. In this study, we analyse a further potential benefit of automation, that is economic convenience. We study the case of the automated laboratory of one of the biggest hospital in Italy and compare the cost related to the pre and post automation situation. Introducing automation lead to a cost decrease without affecting the service level to patients. This was a key goal of the hospital which, as public health entities in general, is constantly struggling with budget constraints.

One hundred years of clinical laboratory automation: 1967–2067

One hundred years of clinical laboratory automation: 1967–2067

Automation in the clinical laboratory: integration of several analytical and intralaboratory pre- and post-analytical systems

Abstract Clinical laboratory automation is very important to obtain reliable test results and to provide patient safety. There are some difficulties in implementing total automation to the clinical laboratories because they need a continuous, high quality customer service to keep their high quality serving, a questionable cost-affecting situation. It may be very difficult to keep the balance between the cost and the quality goals, patient safety, and demands. However, clinical laboratory automation may solve the dilemma and be implemented in clinical laboratories provided that it does not result in new bottlenecks in laboratory workflow. It is beyond the dispute that the minimal operator intervention benefited by total lab automation results in increased productivity, intra laboratory traceability of specimens, the decreased turnaround times, improvements in specimen handling, improved laboratory safety, and minimized errors. It has become very difficult, time-consuming, challenging task for the laboratories to decide to automate and which tests must be included in the analytical automation, to decide which one is more appropriate. First of all, a workflow and a workload analysis must be made for the present semi-automated laboratory. It would be focused in the present review that some strategies can be developed for this purpose.

On the application of optical forward-scattering to bacterial identification in an automated clinical analysis perspective

The Optical Forward Scattering (OFS) technique can be used to identify pathogens by direct observation of bacteria colonies growing on a culture plate. The identification is based on the acquisition of scattering images from isolated colonies and their subsequent comparison with reference images acquired from known bacteria. The technique has been mainly studied for the identification of pathogens in the food-safety field. This paper focuses on the possibility of extending the applicability of the technique to the field of clinical laboratory automation. This scenario requires that the paradigm of image acquisition at fixed colony-dimension, well established in the food-safety applications, should be substituted by an acquisition at fixed incubation time. As a consequence, the scatterometer must be adjustable in real-time for adapting to the actual features of the bacterial colony. The paper describes an OFS system prototype qualified by the possibility to tune both the laser beam diameter and the acquisition camera field of view. Preliminary experiments on bacteria cultures from pathogens causing infections of the urinary tract show that the proposed approach is promising for the development of an automated bacteria identification station. The new OFS approach also involves an alternative method for building a reference image database for subsequent image analysis.

Evaluation of the latex particle-enhanced turbidimetric immunoassay for the measurement of plasma and urinary neutrophil gelatinase-associated lipocalin

Objective To evaluate the performance of the latex particle-enhanced turbidimetric immunoassay for the determination of plasma and urinary neutrophil gelatinase-associated lipocalin. Methods Latex particle-enhanced turbidimetric immunoassay was used to measure the concentration of NGAL in plasma and urinary samples for confirmatory study.The assay imprecision,limit of blank (LOB),limit of detection (LOD),linearity,accuracy and interference factors were assessed according to CLSI protocols.Twenty samples of patients with acute kidney injury (AKI),166 samples of patients with chronic kidney disease (CKD) and 100 healthy adult samples were tested. Results Repeatability estimate of the NGAL was 1.90% (Level 1) & 1.22% (Level 2),and the within-Laboratory imprecision was 4.06% (Level 1) & 2.57% (Level 2) respectively.The LOB was 3.1μg/L and the LOD of plasma NGAL (sNGAL) and urinary NGAL (u NGAL) were 12.5μg/L, 21μg/L respectively.The linear coverage was from 25 to 4250μg/L.The accuracy of sNGAL and uNGAL was 98.89%,98.28% (Level 1) & 102.61%,95.44% (Level 2).The results were basically identical with similar foreign kit measurement results.No interference in plasma was observed with hemoglobin≤7 g/L,bilirubin≤0.6 g/L,vitamin C≤0.6 g/L,sodium heparin≤5 g/L and introlipid≤1%.The concentration of u NGAL was in the acceptable range with the hemoglobin≤10 g/L,bilirubin≤0.6 g/L and vitamin C≤0.6 g/L.In general,the measured NGAL levels in AKI group and CKD group were higher than control group; AKI group was significantly higher than CKD group.The evaluated glomerular filter rate (eGFR) was significantly inverse correlated with sNGAL and u NGAL (r=-0.758 & -0.692,P<0.01). Conclusions The data show that excellent imprecision and linearity are achieved using the particle-enhanced turbidimetric immunoassay for testing plasma and urinary NGAL concentrations.The assay of NGAL has advantages including high automation and fast speed detection which are suited for clinical application and laboratory automation.(Chin J Lab Med,2013,36:1027-1032) Key words: Acute-phase proteins; Lipocalins; Latex particle enhanced immunoassay

Automation and Expert Systems in a Core Clinical Chemistry Laboratory

Clinical pathology has a major influence on clinical decisions and the past 60 years have seen an evolution brought about by advances in information technology and automation. The impact of the eve...

Selecting Automation for the Clinical Chemistry Laboratory

Laboratory automation proposes to improve the quality and efficiency of laboratory operations, and may provide a solution to the quality demands and staff shortages faced by today's clinical laboratories. Several vendors offer automation systems in the United States, with both subtle and obvious differences. Arriving at a decision to automate, and the ensuing evaluation of available products, can be time-consuming and challenging. Although considerable discussion concerning the decision to automate has been published, relatively little attention has been paid to the process of evaluating and selecting automation systems. To outline a process for evaluating and selecting automation systems as a reference for laboratories contemplating laboratory automation. Our Clinical Chemistry Laboratory staff recently evaluated all major laboratory automation systems in the United States, with their respective chemistry and immunochemistry analyzers. Our experience is described and organized according to the selection process, the important considerations in clinical chemistry automation, decisions and implementation, and we give conclusions pertaining to this experience. Including the formation of a committee, workflow analysis, submitting a request for proposal, site visits, and making a final decision, the process of selecting chemistry automation took approximately 14 months. We outline important considerations in automation design, preanalytical processing, analyzer selection, postanalytical storage, and data management. Selecting clinical chemistry laboratory automation is a complex, time-consuming process. Laboratories considering laboratory automation may benefit from the concise overview and narrative and tabular suggestions provided.

EDITORIAL PREAMBLE: Molecular biology tools in clinical diagnosis

The discovery of Polymerase Chain Reaction (PCR) by Kary Mullis (US Patent Number 4,683,202) triggered an unprecedented race for more specific, rapid and highly sensitive diagnosis methods in the clinical laboratory. A wide spectrum of infectious diseases and genetic disorders are now routinely tested using in-house designed or commercially available nucleic acid amplification tests and nucleic acid detection systems. Molecular biology testing can now aid in infection control interventions by accurately and rapidly determining antimicrobial resistance. Implementation of molecular diagnosis in clinics is not without the pains of labor. Experimental set-up, finding the ‘gold standard’ for results confirmation, dealing with standardization of DNA and RNA extraction and cross-contamination are among the problematic issues and logistic challenges often difficult to overcome in this process. High-throughput analytical platforms have been implemented in certain more sophisticated laboratories and lead to cost-effective testing and rapid turnaround time. The development of molecular techniques for diagnosis and also typing of potential agents of bioterrorism or emerging pathogens is also part of a larger strategy to develop rapid and sensitive testing while circumventing safety concerns related to growing these agents in culture. Acknowledging the rapid pace of application of molecular biology tools in clinical diagnosis, the Journal of Cellular and Molecular Biology is introducing a new section to highlight the present state of the art of molecular diagnostics in the clinical area. Manuscripts to be considered for this section should address the clinical application of molecular diagnosis of infectious diseases and genetic disorders including nucleic acid amplification tests, proteomics, microarray analysis, Human Genome Project, as well as clinical laboratory automation and validation methods. Kary Mullis (inventor of PCR and 1993 Nobel prize winner) and Dana Perkins (Assistant Editor, JCMM), 2003, National Inventors Hall of Fame, Akron, OH. Two papers in the field of molecular diagnosis are published in this issue [1, 2].

The Clinical Chemist: Masahide Sasaki, MD, PhD (August 27, 1933–September 23, 2005)

In the 1980s, long before clinical laboratories in the United States were faced with the challenges of consolidation, Masahide Sasaki, MD, PhD, and his associates were inventing what was to become the greatest revolution in clinical laboratory technology since the autoanalyzer. Masahide Sasaki is credited with being the father of modern clinical laboratory automation, a technology that has been installed in laboratories throughout the world. Sadly, Masahide Sasaki passed away on September 23, 2005, after a valiant fight against cancer. He is survived by his devoted wife Toyoko and his children Mika, Kyoko, and Masanori. Masahide was born in 1933 in Yamaguchi Prefecture, Japan. He graduated from Yamaguchi Medical School in 1961. During 1965, he served as an internist for the Hiroshima Atomic …

Cost Justifying Clinical Laboratory Automation a Task Targeted Automation™ (TTA) Approach to Justifying Laboratory Automation

Cost Justifying Clinical Laboratory Automation a Task Targeted Automation™ (TTA) Approach to Justifying Laboratory Automation

Stand-Alone Automated Solutions Can Enhance Laboratory Operations

Clinical laboratory automation has developed over the past decade as one means of consolidating testing, reducing costs, and improving the effectiveness of laboratory testing. Most of the developments have been aimed at core clinical laboratory operations, and have primarily addressed preanalytical and analytical processing of traditional specimens arriving in blood collection or similar aliquot tubes. Much less attention has been given to specialized applications such as processing specimens for urine toxicology, and only recently have vendors attacked the problems associated with sorting and maintaining the laboratory's inventory of specimens. This report highlights selected developments in these areas, describes one approach to cost-effective custom platform development, and discusses the advantages and pitfalls to solving problems with laboratory automation.

Total laboratory automation in Japan: Past, present and the future

The history of systematized automation in clinical laboratories in Japan started in 1981. At that time, about 12 laboratory technicians worked in a typical private University hospital laboratory (average size 1000 beds), whereas in national university hospitals (typical size 600 beds), the number of technicians was as low as 18–25. In 1981, the Kochi Medical School was founded as a new national school, and laboratory staffing was limited by the Ministry of Education to only 19 technicians for the first 3 years. Therefore, we started to develop a fully automated laboratory system by ourselves rather than accepting an intolerable shortage of technicians. The system was based on conveyor and robotic technology, and we called this approach systematization. Ten years later, systematized automation was introduced into the Japanese market. As a result, 72% of the national university hospitals in Japan installed commercial systems for systematization. There is a trend now in hospitals with sufficient numbers of technicians, to introduce fully automated systems in their laboratories as well, and even small hospitals with less than 100 beds are planning to introduce such systems. However, current technology is too expensive and not sufficiently standardized to meet the needs of these market segments in Japan. We recommend that companies agree on common shapes and sizes of racks and include more flexible robotic technology in their sample handling systems, to allow for plug and play systems and to make systematization affordable for every laboratory in the world.