Benefit analysis of the auto-verification system of intelligent inspection for microorganisms.

In recent years, the automatic machine for microbial identification and antibiotic susceptibility tests has been introduced into the microbiology laboratory of our hospital, but there are still many steps that need manual operation. The purpose of this study was to establish an auto-verification system for bacterial naming to improve the turnaround time (TAT) and reduce the burden on clinical laboratory technologists. After the basic interpretation of the gram staining results of microorganisms, the appearance of strain growth, etc., the 9 rules were formulated by the laboratory technologists specialized in microbiology for auto-verification of bacterial naming. The results showed that among 70,044 reports, the average pass rate of auto-verification was 68.2%, and the reason for the failure of auto-verification was further evaluated. It was found that the main causes reason the inconsistency between identification results and strain appearance rationality, the normal flora in the respiratory tract and urine that was identified, the identification limitation of the mass spectrometer, and so on. The average TAT for the preliminary report of bacterial naming was 35.2 h before, which was reduced to 31.9 h after auto-verification. In summary, after auto-verification, the laboratory could replace nearly 2/3 of manual verification and issuance of reports, reducing the daily workload of medical laboratory technologists by about 2 h. Moreover, the TAT on the preliminary identification report was reduced by 3.3 h on average, which could provide treatment evidence for clinicians in advance.

Optimization and Validation of Limit Check Error-Detection Performance Using a Laboratory-Specific Data-Simulation Approach: A Prerequisite for an Evidence-Based Practice.

BACKGROUND: Autoverification procedures based on limit checks (LCs) provide important support to preanalytical, analytical, and postanalytical quality assurance in medical laboratories. A recently described method, based on laboratory-specific error-detection performances, was used to determine LCs for all chemistry analytes performed on random-access chemistry analyzers prior to application. METHODS: Using data sets of historical test results, error-detection simulations of limit checks were performed using the online MA Generator system (www.huvaros.com). Errors were introduced at various positions in the data set, and the number of tests required for an LC alarm to occur was plotted in bias detection curves. Random error detection was defined as an LC alarm occurring in 1 test result, whereas systematic error detection was defined as an LC alarm occurring within an analytical run, both with ≥97.5% probability. To enable the lower limit check (LLC) and the upper limit check (ULC) to be optimized, the simulation results and the LC alarm rates for specific LLCs and ULCs were presented in LC performance tables. RESULTS: Optimal LLCs and ULCs were obtained for 31 analytes based on their random and systematic error-detection performances and the alarm rate. Reliable detection of random errors greater than 60% was only possible for analytes known to have a rather small variation of results. Furthermore, differences for negative and positive errors were observed. CONCLUSIONS: The used method brings objectivity to the error-detection performance of LCs, thereby enabling laboratory-specific LCs to be optimized and validated prior to application.

Evidence-based approach to setting delta check rules.

Delta checks are a post-analytical verification tool that compare the difference in sequential laboratory results belonging to the same patient against a predefined limit. This unique quality tool highlights a potential error at the individual patient level. A difference in sequential laboratory results that exceeds the predefined limit is considered likely to contain an error that requires further investigation that can be time and resource intensive. This may cause a delay in the provision of the result to the healthcare provider or entail recollection of the patient sample. Delta checks have been used primarily to detect sample misidentification (sample mix-up, wrong blood in tube), and recent advancements in laboratory medicine, including the adoption of protocolized procedures, information technology and automation in the total testing process, have significantly reduced the prevalence of such errors. As such, delta check rules need to be selected carefully to balance the clinical risk of these errors and the need to maintain operational efficiency. Historically, delta check rules have been set by professional opinion based on reference change values (biological variation) or the published literature. Delta check rules implemented in this manner may not inform laboratory practitioners of their real-world performance. This review discusses several evidence-based approaches to the optimal setting of delta check rules that directly inform the laboratory practitioner of the error detection capabilities of the selected rules. Subsequent verification of workflow for the selected delta check rules is also discussed. This review is intended to provide practical assistance to laboratories in setting evidence-based delta check rules that best suits their local operational and clinical needs.

Establishing and evaluating an auto-verification system of thalassemia gene detection results.

The manual verification of gene tests is time-consuming and error prone. In this study, we try to explore a high-efficiency, clinically useful auto-verification system for gene detection of thalassemia. A series of verification elements were rooted in the auto-verification system. Consistency check was applied initially as one of the essential elements in our study. One hundred twenty-four archived cases were used to choose the consistency-check rules' indices from routine blood examination and hemoglobin electrophoresis by the receiver operating characteristic curves. Rule 1 and rule 2 established by the chosen indices were compared by their passing rate, consistency with manual validation, and error rate. Finally, 748 cases were used for verifying the system's feasibility by evaluating the passing rate, turn-around time (TAT), and error rate. The rule 2 had a higher passing rate (67.7% vs. 50.8%) and consistency (0.623 vs. 0.364) than the rule 1 with an error rate of zero. In a "live" valuation, the auto-verification system can reduce the TAT and error rate of verification by 51.5% and 0.13%, respectively, with a high passing rate of 82.8%. The auto-verification system for gene detection of thalassemia in this study can shorten the validation time, reduce errors, and enhance efficiency.

Establishing and validating of an laboratory information system-based auto-verification system for biochemical test results in cancer patients.

BACKGROUND: To establish and validate an laboratory information system (LIS)-based auto-verification (AV) system by using large amounts of biochemical test results in cancer patients. METHODS: An algorithm of the AV process was designed for pre-analysis, analysis, and post-analysis. The limit range check was adjusted three times, while the delta check criteria were first replaced by the same patients' historical extremum results. AV rules of 51 biochemical test items were tested by using data of 121 123 samples (6 177 273 tests) in 2016 that were manually reviewed through the simulative i-Vertification software of Roche. The improved and optimal AV rules were programed into our LIS and validated by using 140 113 clinical specimens in 2018. RESULTS: The AV passing rate for samples tested in our laboratory increased from 15.57% to the current overall passing rate of 49.70%. The passing rate of each item for rule 3 was between 71.16% and 99.91%. Different cancer groups had different passing rate, while the disease group of liver, gallbladder, and pancreas always had the lowest passing rate. A total of 9420 reports (6.72%) were not verified by AV but could be verified by MV in 2018, while there were no reports that were verified by AV but not by MV. The TAT of March 2018 decreased with increase in sample size compared with the same time in 2017. CONCLUSION: We have firstly established an LIS-based AV system and implemented it in actual clinical care for cancer patients.

Multiple pre- and post-analytical lean approaches to the improvement of the laboratory turnaround time in a large core laboratory.

BACKGROUND: Core laboratory (CL), as a new business model, facilitates consolidation and integration of laboratory services to enhance efficiency and reduce costs. This study evaluates the impact of total laboratory automation system (TLA), electric track vehicle (ETV) system and auto-verification (AV) of results on overall turnaround time (TAT) (phlebotomy to reporting TAT: PR-TAT) within a CL setting. METHODS: Mean, median and percentage of outlier (OP) for PR-TAT were compared for pre- and post-CL eras using five representative tests based on different request priorities. Comparison studies were also carried out on the intra-laboratory TAT (in-lab to reporting TAT: IR-TAT) and the delivery TAT (phlebotomy to in-lab TAT: PI-TAT) to reflect the efficiency of the TLA (both before and after introducing result AV) and ETV systems respectively. RESULTS: Median PR-TATs for the urgent samples were reduced on average by 16% across all representative analytes. Median PR-TATs for the routine samples were curtailed by 51%, 50%, 49%, 34% and 22% for urea, potassium, thyroid stimulating hormone (TSH), complete blood count (CBC) and prothrombin time (PT) respectively. The shorter PR-TAT was attributed to a significant reduction of IR-TAT through the TLA. However, the median PI-TAT was delayed when the ETV was used. Application of various AV rules shortened the median IR-TATs for potassium and urea. However, the OP of PR-TAT for the STAT requests exceeding 60min were all higher than those from the pre-CL era. CONCLUSIONS: TLA and auto-verification rules help to efficiently manage substantial volumes of urgent and routine samples. However, the ETV application as it stands shows a negative impact on the PR-TAT.