Multivariate anomaly detection models enhance identification of errors in routine clinical chemistry testing.

OBJECTIVES: Conventional autoverification rules evaluate analytes independently, potentially missing unusual patterns of results indicative of errors such as serum contamination by collection tube additives. This study assessed whether multivariate anomaly detection algorithms could enhance the detection of such errors. METHODS: Multivariate Gaussian, k-nearest neighbours (KNN) distance, and one-class support vector machine (SVM) anomaly detection models, along with conventional limit checks, were developed using a training dataset of 127,451 electrolyte, urea, and creatinine (EUC) results, with a 5 % flagging rate targeted for all approaches. The models were compared with limit checks for their ability to detect atypical EUC results from samples spiked with additives from collection tubes: EDTA, fluoride, sodium citrate, or acid citrate dextrose (n=200 per contaminant). The study additionally assessed the ability of the models to identify 127,449 single-analyte errors, a potential weakness of multivariate models. RESULTS: The KNN distance and SVM models outperformed limit checks for detecting all contaminants (p-values <0.05). The multivariate Gaussian model did not surpass limit checks for detecting EDTA contamination but was superior for detecting the other additives. All models surpassed limit checks for identifying single-analyte errors, with the KNN distance model demonstrating the highest overall sensitivity. CONCLUSIONS: Multivariate anomaly detection models, particularly the KNN distance model, were superior to the conventional approach for detecting serum contamination and single-analyte errors. Developing multivariate approaches to autoverification is warranted to optimise error detection and improve patient safety.

Establishment and discussion of autoverification rules for transfusion compatibility testing.

OBJECTIVES: To develop an automated verification workflow for transfusion compatibility testing (TCT) based on the AUTO10-A guidelines and blood group serology characteristics and to conduct a simulated validation of the test and subtest results by assessing the appropriateness of the autoverification rules. BACKGROUND: The accuracy of TCT results is a fundamental prerequisite for ensuring the safety of blood transfusions. However, the verification of these results still requires manual intervention. MATERIALS AND METHODS: Five autoverification rules and their standards were determined: agglutination intensity, normal results, logical relationships, delta checks and interlaboratory test comparisons. The established categories and standards for the five rules were retrospectively validated using 13 506 samples (requests) that had been manually verified in our laboratory from January 2020 to June 2023. RESULTS: A total of 66 638 test items were involved in the autoverification, with 3844 items violating the verification rules, resulting in a pass rate of 96.10%. Considering individual test items, four tests had a pass rate of more than 90% in both the test item result table and the test subitem result table. However, there were significant differences in the pass rates between different tests. The same conclusion can be drawn when the unit is requests. The different standards set for the agglutination intensity and the delta check in the ABO typing testing subitems showed significant differences in pass rates. DISCUSSION: The incorporation of manually verified results into the automated verification simulation indicated that the five rules established in this study have good applicability, and appropriate standards can lead to reasonable pass rates.

Machine learning-based delta check method for detecting misidentification errors in tumor marker tests.

OBJECTIVES: Misidentification errors in tumor marker tests can lead to serious diagnostic and treatment errors. This study aims to develop a method for detecting these errors using a machine learning (ML)-based delta check approach, overcoming limitations of conventional methods. METHODS: We analyzed five tumor marker test results: alpha-fetoprotein (AFP), cancer antigen 19-9 (CA19-9), cancer antigen 125 (CA125), carcinoembryonic antigen (CEA), and prostate-specific antigen (PSA). A total of 246,261 records were used in the analysis. Of these, 179,929 records were used for model training and 66,332 records for performance evaluation. We developed a misidentification error detection model based on the random forest (RF) and deep neural network (DNN) methods. We performed an in silico simulation with 1 % random sample shuffling. The performance of the developed models was evaluated and compared to conventional delta check methods such as delta percent change (DPC), absolute DPC (absDPC), and reference change values (RCV). RESULTS: The DNN model outperformed the RF, DPC, absDPC, and RCV methods in detecting sample misidentification errors. It achieved balanced accuracies of 0.828, 0.842, 0.792, 0.818, and 0.833 for AFP, CA19-9, CA125, CEA, and PSA, respectively. Although the RF method performed better than DPC and absDPC, it showed similar or lower performance compared to RCV. CONCLUSIONS: Our research results demonstrate that an ML-based delta check method can more effectively detect sample misidentification errors compared to conventional delta check methods. In particular, the DNN model demonstrated superior and stable detection performance compared to the RF, DPC, absDPC, and RCV methods.

Technical quality assurance and quality control for medical laboratories: a review and proposal of a new concept to obtain integrated and validated QA/QC plans.

Technical quality assurance (QA) and quality control (QA/QC) are important activities within medical laboratories to ensure the adequate quality of obtained test results. QA/QC tools available at medical laboratories include external QC and internal QC, patient-based real-time quality control (PBRTQC) tools such as moving average quality control (MAQC), limit checks, delta checks, and multivariate checks, and finally, analyzer flagging. Recently, for PBRTQC tools, new optimization and validation methods based on error detection simulation have been developed to obtain laboratory-specific insights into PBRTQC error detection. These developments have enabled implementation and application of these individual tools in routine clinical practice. As a next step, they also enable performance comparison of the individual QA/QC tools and integration of all the individual QA/QC tools in order to obtain the most powerful and efficient QA/QC plans. In this review, a brief overview of the individual QA/QC tools and their characteristics is provided and the error detection simulation approaches are explained. Finally, a new concept entitled integrated quality assurance and control (IQAC) is presented. To enable IQAC, a conceptual framework is suggested and demonstrated for sodium, based on available published data. The proposed IQAC framework provides ways and tools by which the performance of different QA/QC tools can be compared in a so-called QA/QC error detection table to enable optimization and validation of the overall QA/QC plan in terms of alarm rate as well as pre-analytical, analytical, and post-analytical error detection performance.

The development of autoverification system of lymphocyte subset assays on the flow cytometry platform.

OBJECTIVES: Peripheral blood lymphocyte subsets are important parameters for monitoring immune status; however, lymphocyte subset detection is time-consuming and error-prone. This study aimed to explore a highly efficient and clinically useful autoverification system for lymphocyte subset assays performed on the flow cytometry platform. METHODS: A total of 94,402 lymphocyte subset test results were collected. To establish the limited-range rules, 80,427 results were first used (69,135 T lymphocyte subset tests and 11,292 NK, B, T lymphocyte tests), of which 15,000 T lymphocyte subset tests from human immunodeficiency virus (HIV) infected patients were used to set customized limited-range rules for HIV infected patients. Subsequently, 13,975 results were used for historical data validation and online test validation. RESULTS: Three key autoverification rules were established, including limited-range, delta-check, and logical rules. Guidelines for addressing the issues that trigger these rules were summarized. The historical data during the validation phase showed that the total autoverification passing rate of lymphocyte subset assays was 69.65% (6,941/9,966), with a 67.93% (5,268/7,755) passing rate for T lymphocyte subset tests and 75.67% (1,673/2,211) for NK, B, T lymphocyte tests. For online test validation, the total autoverification passing rate was 75.26% (3,017/4,009), with 73.23% (2,191/2,992) for the T lymphocyte subset test and 81.22% (826/1,017) for the NK, B, T lymphocyte test. The turnaround time (TAT) was reduced from 228 to 167 min using the autoverification system. CONCLUSIONS: The autoverification system based on the laboratory information system for lymphocyte subset assays reduced TAT and the number of error reports and helped in the identification of abnormal cell populations that may offer clues for clinical interventions.

Combined strategy of knowledge-based rule selection and historical data percentile-based range determination to improve an autoverification system for clinical chemistry test results.

BACKGROUND: Current autoverification, which is only knowledge-based, has low efficiency. Regular historical data analysis may improve autoverification range determination. We attempted to enhance autoverification by selecting autoverification rules by knowledge and ranges from historical data. This new system was compared with the original knowledge-based system. METHODS: New types of rules, extreme values, and consistency checks were added and the autoverification workflow was rearranged to construct a framework. Criteria for creating rules for extreme value ranges, limit checks, consistency checks, and delta checks were determined by analyzing historical Zhongshan laboratory data. The new system's effectiveness was evaluated using pooled data from 20 centers. Efficiency improvement was assessed by a multicenter process. RESULTS: Effectiveness was evaluated by the true positive rate, true negative rate, and overall consistency rate, as compared to manual verification, which were 77.55%, 78.53%, and 78.3%, respectively for the new system. The original overall consistency rate was 56.2%. The new pass rates, indicating efficiency, were increased by 19%-51% among hospitals. Further customization using individualized data increased this rate. CONCLUSIONS: The improved system showed a comparable effectiveness and markedly increased efficiency. This transferable system could be further improved and popularized by utilizing historical data from each hospital.

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.

Using machine learning to develop an autoverification system in a clinical biochemistry laboratory.

OBJECTIVES: Autoverification systems have greatly improved laboratory efficiency. However, the long-developed rule-based autoverfication models have limitations. The machine learning (ML) algorithm possesses unique advantages in the evaluation of large datasets. We investigated the utility of ML algorithms for developing an artificial intelligence (AI) autoverification system to support laboratory testing. The accuracy and efficiency of the algorithm model were also validated. METHODS: Testing data, including 52 testing items with demographic information, were extracted from the laboratory information system and Roche Cobas® IT 3000 from June 1, 2018 to August 30, 2019. Two rounds of modeling were conducted to train different ML algorithms and test their abilities to distinguish invalid reports. Algorithms with the top three best performances were selected to form the finalized ensemble model. Double-blind testing between experienced laboratory personnel and the AI autoverification system was conducted, and the passing rate and false-negative rate (FNR) were documented. The working efficiency and workload reduction were also analyzed. RESULTS: The final AI system showed a 89.60% passing rate and 0.95 per mille FNR, in double-blind testing. The AI system lowered the number of invalid reports by approximately 80% compared to those evaluated by a rule-based engine, and therefore enhanced the working efficiency and reduced the workload in the biochemistry laboratory. CONCLUSIONS: We confirmed the feasibility of the ML algorithm for autoverification with high accuracy and efficiency.

Development and implementation of an LIS-based validation system for autoverification toward zero defects in the automated reporting of laboratory test results.

BACKGROUND: Validation of the autoverification function is one of the critical steps to confirm its effectiveness before use. It is crucial to verify whether the programmed algorithm follows the expected logic and produces the expected results. This process has always relied on the assessment of human-machine consistency and is mostly a manually recorded and time-consuming activity with inherent subjectivity and arbitrariness that cannot guarantee a comprehensive, timely and continuous effectiveness evaluation of the autoverification function. To overcome these inherent limitations, we independently developed and implemented a laboratory information system (LIS)-based validation system for autoverification. METHODS: We developed a correctness verification and integrity validation method (hereinafter referred to as the "new method") in the form of a human-machine dialog. The system records personnel review steps and determines whether the human-machine review results are consistent. Laboratory personnel then analyze the reasons for any inconsistency according to system prompts, add to or modify rules, reverify, and finally improve the accuracy of autoverification. RESULTS: The validation system was successfully established and implemented. For a dataset consisting of 833 rules for 30 assays, 782 rules (93.87%) were successfully verified in the correctness verification phase, and 51 rules were deleted due to execution errors. In the integrity validation phase, 24 projects were easily verified, while the other 6 projects still required the additional rules or changes to the rule settings. Taking the Hepatitis B virus test as an example, from the setting of 65 rules to the automated releasing of 3000 reports, the validation time was reduced from 452 (manual verification) to 275 h (new method), a reduction in validation time of 177 h. Furthermore, 94.6% (168/182) of laboratory users believed the new method greatly reduced the workload, effectively controlled the report risk and felt satisfied. Since 2019, over 3.5 million reports have been automatically reviewed and issued without a single clinical complaint. CONCLUSION: To the best of our knowledge, this is the first report to realize autoverification validation as a human-machine interaction. The new method effectively controls the risks of autoverification, shortens time consumption, and improves the efficiency of laboratory verification.

Increased effectiveness of urinalysis testing via the integration of automated instrumentation, the lean management approach, and autoverification.

BACKGROUND: In 2014, the Department of Clinical Pathology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand developed and implemented a new process that uses fully automated instrumentation, the lean management approach, and autoverification to improve the productivity and efficiency of the urinalysis workflow process. The aim of this study was to evaluate analytical turnaround time compared with our old urinalysis workflow process and our new urinalysis workflow process that was launched in 2014. METHODS: This study was performed at the Central Laboratory of our center during June 2017 using data collected from the July 2012 (old process) and July 2014 (new process) study periods. We used our laboratory information system to compute and analyze turnaround time of urinalysis tests, and those results were compared between processes. RESULTS: The 90th percentile turnaround time in overall data was dramatically decreased from approximately 60 minutes in 2012 to <50 minutes in 2014. The mean during both 6:00 am to 9:00 am and 9:00 am to 12:00 pm was approximately 42 minutes in 2012; however, that duration was reduced to approximately 30 minutes for both of those time periods in 2014. Specimens within 60 minutes in both intervals increase from approximately 80% to more than 90%. CONCLUSION: The results of this study revealed our new urinalysis workflow process that incorporates fully automated instrumentation, the lean management approach, and autoverification to be effective for significantly increasing productivity as measured by analytical turnaround time and removing 1 staff to another section.

Application and optimization of reference change values for Delta Checks in clinical laboratory.

BACKGROUND: Delta check is a patient-based QC tool for detecting errors by comparing current and previous test results of patient. Reference change value (RCV) is adopted in guidelines as method for delta check, but the performance is not verified. We applied RCV-based delta check method to patients' data and modified for application. MATERIALS AND METHODS: Reference change value were calculated using results of internal QC materials and biological variation data. Test results of 17 analytes in inpatients, outpatients, and health examination recipients were collected. The detection rates of currently used delta check method and those of RCV-based method were compared, and the methods were modified. RESULTS: Reference change value-based method had higher detection rates compared to conventional method. Applied modifications reduced detection rates. Removing the pairs of results within reference interval reduced detection rates (0.42% ~ 10.92%). When RCV was divided by time interval, the detection rates were similar to prior rates in outpatients (0.19% ~ 1.34%). Using RCV multiplied by twice the upper limit of reference value as cutoff reduced the detection rate (0.07% ~ 1.58%). CONCLUSIONS: Reference change value is a robust criterion for delta check and included in clinical laboratory practice guideline. However, RCV-based method generates high detection rates which increase workload. It needs modification for use in clinical laboratories.

Optimization of Turnaround Time for Group A Streptococcus PCR.

The use of some nucleic acid amplification tests (NAATs) for the diagnosis of group A Streptococcus (GAS) pharyngitis allows laboratories to adopt single-tiered testing without reflex culture. However, centralization may delay the delivery of actionable information to the bedside, particularly in the outpatient setting. We describe two novel workflows at our institution and their effect on in-lab turnaround time (TAT) at a tertiary care microbiology lab. Laboratory records were extracted, and relevant data were analyzed after the implementation of qualitative in vitro diagnostic testing for GAS with the Xpert Xpress Strep A assay, performed using the GeneXpert Infinity-48s. Workflow optimization steps studied included: (i) direct specimen submission to the microbiology laboratory via the pneumatic tube system and (ii) autoverification of GAS NAAT results in the laboratory information system. Between April 2018 and October 2018, 2,595 unique specimens were tested for GAS by PCR. Of these, 2,523 were included in the final analysis. Linear regression established that the total in-lab TAT was significantly reduced by direct specimen submission to the microbiology laboratory, autoverification, and processing during the night shift. We describe two workflow optimization methods that reduced the in-lab TAT for GAS NAAT. Although microbiology labs historically use manual processes, the advent of total laboratory automation and the adoption of on-demand NAATs will allow for more streamlined processing of microbiology specimens. It may be beneficial to consider instrument interfacing and specimen processing optimization during the early phases of implementation planning for NAATs in the microbiology laboratory.

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.

Design and evaluation of a LIS-based autoverification system for coagulation assays in a core clinical laboratory.

BACKGROUND: The autoverification system for coagulation consists of a series of rules that allow normal data to be released without manual verification. With new advances in medical informatics, the laboratory information system (LIS) has growing potential for the autoverification, allowing rapid and accurate verification of clinical laboratory tests. The purpose of the study is to develop and evaluate a LIS-based autoverification system for validation and efficiency. METHODS: Autoverification decision rules, including quality control, analytical error flag, critical value, limited range check, delta check and logical check, as well as patient's historical information, were integrated into the LIS. Autoverification limited range was constructed based on 5 and 95% percentiles. The four most commonly used coagulation assays, prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), and fibrinogen (FBG), were followed by the autoverification protocols. The validation was assessed by the autoverification passing rate, the true-positive cases, the true-negative cases, the false-positive cases, the false-negative cases, the sensitivity and the specificity; the efficiency was evaluated in the turnaround time (TAT). RESULTS: A total of 157,079 historical test results of coagulation profiles from January 2016 to December 2016 were collected to determine the distribution intervals. The autoverification passing rate was 77.11% (29,165/37,821) based on historical patient data. In the initial test of the autoverification version in June 2017, the overall autoverification passing rate for the whole sample was 78.75% (11,257/14,295), with 892 true-positive cases, 11,257 true-negative cases, 2146 false-positive cases, no false-negative cases, sensitivity of 100% and specificity of 83.99%. After formal implementation of the autoverification system for 6 months, 83,699 samples were assessed. The average overall autoverification passing rate for the whole sample was 78.86% and the 95% confidence interval (CI) of the passing rate was [78.25, 79.59%]. TAT was reduced from 126 min to 101 min, which was statistically significant (P < 0.001, Mann-Whitney U test). CONCLUSIONS: The autoverification system for coagulation assays based on LIS can halt the samples with abnormal values for manual verification, guarantee medical safety, minimize the requirements for manual work, shorten TAT and raise working efficiency.

Establishment and application of autoverification system for HbA1c testing.

Introduction: This study aimed to determine autoverification rules for routine glycated hemoglobin (HbA1c) analysis based on high-performance liquid chromatography (HPLC) principle. Laboratory information system (LIS) and Bio-Rad D-100 Advisor software (Bio-Rad, Hercules, USA) with graphics recognition function were carriers for the autoverification system. Materials and methods: A total of 105,126 HbA1c results, including 98,249 HbA1c matching fast plasma glucose (FPG) results of real-world data from May 2019 to June 2020, were collected to determine autoverification rules including flags, delta checks, reporting limits, and logical rules. The validation database was composed of 48,045 HbA1c results and 41,083 matching FPG results. Autoverification passing rate and the reduction of turnaround time (TAT) were evaluated. Results: Four autoverification systems (A, B, C, D) were established by two types of delta check rules, 28 flags, one reporting limits, and two kinds of logical rules. The autoverification passing rates were 80.6%, 78.8%, 83.7%, and 81.3%, and the average time saved in TAT were 117.5 min, 116.7 min, 121.1 min, and 121.7 min, respectively. Conclusions: Autoverification system C was the optimal one. Application of distribution of FPG corresponding to HbA1c groups had better performance as logical rules. Established HbA1c autoverifcation system shortened the auditing report time and improved work efficiency.

Development and implementation of autoverification standardization in the adult and pediatric emergency department.

DISCLAIMER: In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. PURPOSE: This project utilized the guidance of the American Society of Health-System Pharmacists (ASHP) autoverification toolkit to refine our health system's approach to autoverification and contribute to the literature regarding appropriate use of autoverification technology in a pediatric and adult emergency department (ED). SUMMARY: This single-center quality improvement study was conducted in an academic medical center ED that has 33 pediatric beds and 77 adult beds. A team consisting of clinical pharmacy specialists in emergency medicine, medication safety and informatics personnel, operational managers, and pharmacy leadership was identified to develop and implement autoverification best practices in the ED utilizing practices outlined within the ASHP autoverification toolkit. Before implementation of best practices, defined as the "preoptimization" state, autoverification took place for most medications available in the automated dispensing cabinets (ADCs). By anchoring the autoverification rule on ADC inventory, it was challenging to optimize both inventory practices and autoverification best practices. This project focused on redesigning the autoverification rules in the electronic health record, defined as the "postoptimization" state. In the postoptimization state, autoverification in the ED was updated to better align with regulatory standards. Autoverification metrics and the percentage of orders that autoverified vs required pharmacist verification were analyzed in the preoptimization and postoptimization states. CONCLUSION: This project utilized the guidance from ASHP's autoverification toolkit to refine our health system's approach to autoverification. High-alert medications (eg, insulin, extended-release opioids, digoxin) were taken off autoverification following implementation. Optimization of autoverification rules allows more orders for high-alert medications to be reviewed by a pharmacist.

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.

Autoverification-based algorithms to detect preanalytical errors: Two examples.

The preanalytical phase of testing accounts for the majority of the errors. Software-based quality rules, such as autoverification, can assist in preanalytical error detection; therefore, preventing erroneous results from being reported. Two autoverification rules, turbidity/lipemia, and pseudohypoglycemia/pseudohyperkalemia alarms, are highlighted. Increased sample turbidity may arise from several causes outside of lipemia. Typically, this can be resolved by clarifying the sample with standard centrifugation. Truly lipemic specimens typically require higher centrifugation speeds and greater centrifugation time. At our facility, 96% of direct bilirubin (DBIL), 95% of aspartate transaminase (AST), and 98% of alanine transaminase (ALT) turbidity/lipemia alarms were found to be from sample turbidity versus lipemia. Secondly, a pseudohypoglycemia/pseudohyperkalemia rule was employed for specimens with delayed separation from cellular material. Of the total potassium results >6.0 mmol/L or glucose results <40 mg/dL (2.2 mmol/L), 30% and 50% respectively were noted to have delayed sample separation.

Short-Term (6 Weeks) Experience of a Modular Workcell for Hemostasis Testing Including an Intelligent Data Manager at a Tertiary Care Hospital.

OBJECTIVE: Modular workcells could be a better solution than total laboratory automation (TLA) in hemostasis laboratories. Here, we evaluated the impact of implementing a modular workcell (HemoCell) with an intelligent data management facility (HemoHub). METHODS: We compared the turnaround times (TATs), numbers of rerun samples, and rerun times pre- and postimplementation of the HemoCell at Gil Medical Center. Prothrombin time (PT), activated partial thromboplastin time (aPTT), D-dimer, and fibrinogen were evaluated. RESULTS: The TAT standard deviations (SDs) and maximum TAT values decreased after HemoCell implementation, although the mean TATs for PT, aPTT, and D-dimer were increased. Numbers of rerun samples were increased (18.1/day vs 44.7/day). However, rerun times were reduced, and SDs were decreased during the post-HemoCell period compared with pre-HemoCell. Additionally, technologists needed smaller working space and less labor. CONCLUSION: The modular workcell could improve quality and efficiency by providing more consistent TATs and shorter rerun times in the hemostasis laboratory.