Total Automation for the Core Laboratory: Improving the Turnaround Time Helps to Reduce the Volume of Ordered STAT Tests

Characterization of add-on testing before and after automation at a core laboratory

Objectives

Add-on testing refers to the process that occurs in clinical laboratories when clinicians request that additional tests be performed on a previously analysed specimen. This is a common but inefficient procedure, highly time-consuming, especially at core laboratories and could be optimised by automating these procedures. The aims of this study are: 1) To describe patterns of add-on testing at a core laboratory at a tertiary hospital, 2) To evaluate turnaround time (TAT) before and after automation of the pre-, post- and analytical phases.

Methods

Retrospective, observational study conducted at the biochemistry area of a core laboratory of all add-on orders received in two different months (pre-automation and post-automation).

Results

A total of 2464 add-on orders were analysed, representing around 5 % of total requests. Most orders were for either one (>50 %) or two (≈20 %) tests. Most orders were received during the week (from Monday to Friday), particularly during the morning shift (>50 %). More than 50 % of requests were made by the Emergency Department. The two most common add-on parameters were C-reactive protein and N-terminal pro-brain natriuretic peptide. After automation, the median TAT decreased by 42.3 % (from 52 to 22 min). The largest decreases in TAT were observed for routine samples (58.89 %) and fully automated analyses (56.86 %).

Conclusions

Automation of our core laboratory substantially reduced turnaround time for add-on testing, indicating an increase in efficiency. Automation eliminated several manual steps in the process, leading to a mean reduction of 15 work hours per day (more than 2 full-time equivalents).

Shorter laboratory turnaround time is associated with shorter emergency department length of stay: a retrospective cohort study

BACKGROUND

A longer emergency department length of stay (EDLOS) is associated with poor outcomes. Shortening EDLOS is difficult, due to its multifactorial nature. A potential way to improve EDLOS is through shorter turnaround times for diagnostic testing. This study aimed to investigate whether a shorter laboratory turnaround time (TAT) and time to testing (TTT) were associated with a shorter EDLOS.

METHODS

A retrospective cohort study was performed, including all visits to the emergency department (ED) of an academic teaching hospital from 2017 to 2020 during which a standardized panel of laboratory tests had been ordered. TTT was calculated as the time from arrival in the ED to the ordering of laboratory testing. TAT was calculated as the time from test ordering to the reporting of the results, and was divided into a clinical and a laboratory stage. The outcome was EDLOS in minutes. The effect of TTT and TAT on EDLOS was estimated through a linear regression model.

RESULTS

In total, 23,718 ED visits were included in the analysis. Median EDLOS was 199.0 minutes (interquartile range [IQR] 146.0-268.0). Median TTT was 7.0 minutes (IQR 2.0-12.0) and median TAT was 51.1 minutes (IQR 41.1-65.0). Both TTT and TAT were positively associated with EDLOS. The laboratory stage comprised a median of 69% (IQR 59-78%) of total TAT.

CONCLUSION

Longer TTT and TAT are independently associated with longer EDLOS. As the laboratory stage predominantly determines TAT, it provides a promising target for interventions to reduce EDLOS and ED crowding.

Turnaround Time: An Efficacy Measure for Medical Laboratories

Turnaround time (TAT), which doctors frequently use as the benchmark for laboratory performance, is a typical way to communicate timeliness. It also acts as a quality indicator to evaluate the effectiveness and efficiency of the testing process and the satisfaction of clinicians and patients. TAT is the time from receipt of the sample in the laboratory to final delivery or dispatch of the report of said test. The TAT procedure can be broadly divided into three stages pre-analytical, analytical and post-analytical. There is variability in TAT according to different conditions like the volume of sample size, staff expertise, availability of adequate resources, distances of the hospital from the lab, and various sub-departments. To remove obstacles to optimizing TAT, we must take a practical approach. A workload reduction plan, proper stock management, specialized work assignments, and skilled staff retention are crucial strategies to reduce the setting's delayed TAT.

The Impact of Total Automaton on the Clinical Laboratory Workforce: A Case Study

Background

There has been a significant concern that total automation can decrease the need for laboratory personnel at all levels. The objective of this study was to investigate the impact of total laboratory automation on the clinical laboratory workforce.

Methods

A one-year data including the demographical features of laboratory workforce and technical productivity of laboratory tests were provided by two medical laboratory departments of similar profile and different equipment setup; one adopting a total automation system and the other utilizing discrete analysis system. The technical productivities of the two laboratories were compared and statistically tested.

Results

A similar technical productivity per single laboratory worker was noted in the hematology section in each of the two sites with no significant difference (average odd radio = 0.9, p = 0.79). However, with total automation, the number of tests performed per single worker has increased to an average of 1.4 and 3.7 times with total automation in the clinical chemistry and serology sections, respectively (p ≤ 0.001).

Conclusion

Total laboratory automation improves the productivity of the laboratory, leading to a decreased laboratory workforce. The laboratory workload has increased steadily therefore, the existing laboratory workforce, in the absence of automation, could not have been able to maintain the current volume of service. Adoption of automation reduces repetitive manual labor, thereby allowing the redefinition of the job roles of the laboratory workforce. TLA is ideal for laboratories that suffer from workforce shortages or managing high volume testing with less staff.

Effects of Automation on Sustainability of Immunohistochemistry Laboratory

The COVID-19 pandemic that hit the world recently caused numerous changes affecting the health system in every department. Reduced staff numbers, mostly due to illness, led to an increase in automation at every stage of laboratory work. The immunohistochemistry (IHC) laboratory conducts a high volume of slide staining every day. Therefore, we analyzed time and total costs required to obtain IHC slides in both the manual and automated way, comparing their efficiency by processing the same sample volume (48 microscope slides—the maximum capacity that an automated immunostainer—DAKO, Autostainer Link 48, Part No AS48030—can process over a single cycle). The total IHC procedure time to run 48 slides manually by one technician was 460 min, while the automated process finished a cycle within 390 min (15.22% less time). The final cost of a single manual IHC slide was 12.26 EUR and 7.69 EUR for slides labeled in the automated immunostainer, which reduced final costs by 37.27%. Thus, automation of the IHC procedure reduces the time and costs of the IHC process, contributing significantly to the sustainability of the healthcare system during the COVID-19 pandemic, overcoming insufficient human resources.

Performance characteristics of the Abbott Alinity m SARS-CoV-2 assay

Mass molecular diagnostic testing for the SARS-CoV-2 pandemic has drawn on laboratory developed tests, commercial assays, and fully-automated platforms to accommodate widespread demand. The Alinity m instrument by Abbott is capable of detecting several clinically relevant pathogens and has recently received FDA emergency use authorization for SARS-CoV-2 molecular testing. The Alinity m performs automatic sample preparation, RT-PCR assembly, amplification, detection, and result calculation in under two hours. Here, we validate the performance characteristics of the Alinity m SARS-CoV-2 assay in comparison with the Roche cobas 6800 and Hologic Panther Fusion platforms. Across 178 positive and 195 negative nasopharyngeal swab specimens (C_T range 14.30–38.84), the Alinity m detected one additional positive specimen that was found to be negative on the Roche cobas 6800 (PPA 100%, NPA 99.5%). Across a separate set of 30 positive and 174 negative nasopharyngeal swab specimens (C_T range 14.1–38.5), the Alinity m had 100% positive and negative agreement with the Hologic Panther Fusion. Using SeraCare SARS-CoV-2 RNA standards, the assay limit of detection was verified to be two-fold more sensitive than the parameters stated by the SARS-CoV-2 AMP kit package insert, at 50 virus copies/mL. Assay specificity was 100% over 20 specimens positive for other respiratory viruses and intraday precision was 100% concordant with <2% CV. These data illst u illustrate the Abbott Alinity m system's high concordance with reference assays and analyti high analytical for SARS-CoV-2 molecular detection.

Workflow Mapping-A Q-Probes Study of Preanalytic Testing Processes: A College of American Pathologists Q-Probes Study of 35 Clinical Laboratories

CONTEXT.—

Workflow mapping is a tool used to characterize operational processes throughout most industries and to identify non-value-added activities.

OBJECTIVE.—

To develop a set of workflow mapping tools to compare the sequence and timing of activities, including waiting steps, used by clinical laboratories to process specimens during the preanalytic testing phase.

DESIGN.—

Laboratories enrolled in this College of American Pathologists Q-Probes study created workflow maps detailing the steps they used to process specimens from the time of sample arrival in the laboratory to the time of sample delivery to chemistry analyzers. Enrollees recorded the sequence and types of steps involved in specimen processing and the time needed to complete each step.

RESULTS.—

Institution average total specimen processing times (SPTs) and the number of steps required to prepare samples varied widely among institutions. Waiting steps, that is, steps requiring specimens to wait before advancing to the next process step, and specimen centrifugation consumed the greatest amount of processing times for both routine and STAT testing. Routine and STAT testing SPTs were shorter at institutions that used rapid centrifuges to prepare samples. Specimen processes requiring more sample waiting steps and computer entry steps had longer aggregate total process times than those with fewer such steps.

CONCLUSIONS.—

Aggregate specimen processing times may be shortened by reducing the number of steps involving sample waiting and computer entry activities. Rapid centrifugation is likely to reduce overall average institutional SPTs.

Optimization on reagent-loading manner for modular clinical chemistry analyzer series: simulations and verifications

Objectives

The pre- and post-analytical processes have been discussed both in total laboratory system (TLA) and modular automation (MA). The analytical process, especially reagent-related factors influences on the integrated clinical chemistry analyzer, demonstrates a significant effect on clinical chemistry analyzer. Modular analyzer reagent-loading mode influences two mainly factors, testing turnaround time (tTAT) and the cost. Furthermore, how to definite the different reagent loading manners and verify the best reagent loading manner is big challenge.

Methods

We focus on tTAT, and study how the reagent-related factors effect TAT by simulations and verifications. Parameters were simulated by cobas 8000 workflow simulator for reagent-loading manner with at least three positions (Pattern 1), the module-parallel reagent-loading manner (Pattern 2) and the single-position loading mode (Pattern 3).

Results

tTAT, reagent on-line time, quality control (QC) cost and performance verification times all declined by 43%. Tuesday effect solved the repetitive problem for verification. Pattern 2 shows optimal performance in Tuesday effect-based verification.

Conclusions

The optimization of reagent-loading manner saved much workforce, and reduced the QC cost.

Six Sigma revisited: We need evidence to include a 1.5 SD shift in the extraanalytical phase of the total testing process

The Six Sigma methodology has been widely implemented in industry, healthcare, and laboratory medicine since the mid-1980s. The performance of a process is evaluated by the sigma metric (SM), and 6 sigma represents world class performance, which implies that only 3.4 or less defects (or errors) per million opportunities (DPMO) are expected to occur. However, statistically, 6 sigma corresponds to 0.002 DPMO rather than 3.4 DPMO. The reason for this difference is the introduction of a 1.5 standard deviation (SD) shift to account for the random variation of the process around its target. In contrast, a 1.5 SD shift should be taken into account for normally distributed data, such as the analytical phase of the total testing process; in practice, this shift has been included in all type of calculations related to SM including non-normally distributed data. This causes great deviation of the SM from the actual level. To ensure that the SM value accurately reflects process performance, we concluded that a 1.5 SD shift should be used where it is necessary and formally appropriate. Additionally, 1.5 SD shift should not be considered as a constant parameter automatically included in all calculations related to SM.

Confidence interval of percentiles in skewed distribution: The importance of the actual coverage probability in practical quality applications for laboratory medicine

Introduction

Quality indicators (QI) based on percentiles are widely used for managing quality in laboratory medicine nowadays. Due to their statistical nature, their estimation is affected by sampling so they should be always presented together with the confidence interval (CI). Since no methodological recommendation has been issued to date, our aim was investigating the suitability of the parametric method (LP-CI), the non-parametric binomial (NP-CI) and bootstrap (BCa-CI) procedures for the CI estimation of 2.5^th, 25^th, 50^th, 75^th and 97.5^th percentile in skewed sets of data.

Materials and methods

Skewness was reproduced by numeric simulation of a lognormal distribution in order to have samples with different right-tailing (moderate, heavy and very heavy) and size (20, 60 and 120). Performance was assessed with respect to the actual coverage probability (ACP, accuracy) against the confidence level of 1-α with α = 0.5, and the median interval length (MIL, precision).

Results

The parametric method was accurate for sample size N ≥ 20 whereas both NP-CI and BCa-CI required N ≥ 60. However, for extreme percentiles of heavily right-tailed data, the required sample size increased to 60 and 120 units respectively. A case study also demonstrated the possibility to estimate the ACP from a single sample of real-life laboratory data.

Conclusions

No method should be applied blindly to the estimation of CI, especially in small-sized and skewed samples. To this end, the accuracy of the method should be investigated through a numeric simulation that reproduces the same conditions of the real-life sample.

Impact of total automation consolidating first-line laboratory tests on diagnostic blood loss

Background

Blood loss for laboratory testing may contribute to hospital-acquired anemia. When implementing the core laboratory (core-lab) section, we consolidated first-line tests decreasing the number of tubes previously dispatched to different sites. Here, hypothesized benefits of the amount of blood volume drawn were explored.

Methods

We retrieved, using a laboratory information system (LIS), the number of tubes received by laboratories interested in the change from all clinical wards in a year-based period, i.e. 2013 for pre-core-lab and 2015 for core-lab system, respectively. Data were expressed as the overall number of tubes sent to laboratories, the corresponding blood volume, and the number of laboratory tests performed, normalized for the number of inpatients.

Results

After consolidation, the average number of blood tubes per inpatient significantly decreased (12.6 vs. 10.7, p < 0.001). However, intensive care units (ICUs) did not reduce the number of tubes per patient, according to the needs of daily monitoring of their clinical status. The average blood volume sent to laboratories did not vary significantly because serum tubes for core-lab required higher volumes for testing up to 55 analytes in the same transaction. Finally, the number of requested tests per patient during the new osystem slightly decreased (−2.6%).

Conclusions

Total laboratory automation does not automatically mean reducing iatrogenic blood loss. The new system affected the procedure of blood drawing in clinical wards by significantly reducing the number of handled tubes, producing a benefit in terms of costs, labor and time consumption. Except in ICUs, this also slightly promoted some blood saving. ICUs which engage in phlebotomizing patients daily, did not take advantage from the test consolidation.

Magnitude of delayed turnaround time of laboratory results in Amhara Public Health Institute, Bahir Dar, Ethiopia

BACKGROUND

Clinical decisions depend on timely laboratory result reporting. The timeliness is commonly expressed in turnaround time and serves as a quality improvement tool to assess the effectiveness and efficiency of the laboratory. According to the International Organization for Standardization (ISO) guidelines, each laboratory shall establish turnaround times for each of its examinations that reflect clinical needs, and shall periodically evaluate whether or not it is meeting the established turnaround times. Therefore, this study aimed to assess the TAT of laboratory results done in the reference laboratories of the Amhara Public Health Institute, Bahir Dar, Ethiopia.

METHODS

A retrospective cross sectional study was carried out from 01 January to 31 September 2018. Each patient sample was considered as a study unit. Nine months data were extracted from the sample tracking log and from the Laboratory Information System (LIS) database. Descriptive and summary statistics were calculated using SPSS version 20.0 statistical software.

RESULTS

A total of 34,233 patients samples were tested during the study period. Monthly average TAT ranged from 38.6 to 51.3 days for tuberculosis (TB) culture, 5.3 to 42.4 days for exposed infant diagnosis (EID) for HIV, 8.4 to 26 days for HIV 1 viral load, and 1.9 to 3.5 days for TB genexpert tests. Compared with the standard, 76.5% of the viral load, 68.1% of the EID for HIV and 53.8% of the TB genexpert tests had delayed TAT. Repeated reagent stock out, high workload, activities overlapping, and staff turnover were major reasons for the result delays.

CONCLUSIONS

There was a delayed turnaround time of laboratory results in APHI. HIV viral load, EID and TB genexpert results were the most affected tests. Workload reduction plan, proper stock management, specific work assignment and trained staff retention are important approaches to minimize the delayed TAT in the setting.

Automation and artificial intelligence in the clinical laboratory

The daily operation of clinical laboratories will be drastically impacted by two disruptive technologies: automation and artificial intelligence (the development and use of computer systems able to perform tasks that normally require human intelligence). These technologies will also expand the scope of laboratory medicine. Automation will result in increased efficiency but will require changes to laboratory infrastructure and a shift in workforce training requirements. The application of artificial intelligence to large clinical datasets generated through increased automation will lead to the development of new diagnostic and prognostic models. Together, automation and artificial intelligence will support the move to personalized medicine. Changes in pathology and clinical doctoral scientist training will be necessary to fully participate in these changes. KEYWORDS: Automation; artificial intelligence; deep learning; laboratory medicine.

Advantages and limitations of total laboratory automation: a personal overview

Automation is considered one of the most important breakthroughs in the recent history of laboratory diagnostics. In a model of total laboratory automation (TLA), many analyzers performing different types of tests on different sample matrices are physically integrated as modular systems or physically connected by assembly lines. The opportunity to integrate multiple diagnostic specialties to one single track seems effective to improve efficiency, organization, standardization, quality and safety of laboratory testing, whilst also providing a significant return of investment on the long-term and enabling staff requalification. On the other hand, developing a model of TLA also presents some potential problems, mainly represented by higher initial costs, enhanced expenditure for supplies, space requirements and infrastructure constraints, staff overcrowding, increased generation of noise and heat, higher risk of downtime, psychological dependence, critical issues for biospecimen management, disruption of staff trained in specific technologies, along with the risk of transition toward a manufacturer’s-driven laboratory. As many ongoing technological innovations coupled with the current scenario, profoundly driven by cost-containment policies, will promote further diffusion of laboratory automation in the foreseeable future, here we provide a personal overview on some potential advantages and limitations of TLA.

Validation of the Six Sigma Z-score for the quality assessment of clinical laboratory timeliness

BACKGROUND

The International Federation of Clinical Chemistry and Laboratory Medicine has introduced in recent times the turnaround time (TAT) as mandatory quality indicator for the postanalytical phase. Classic TAT indicators, namely, average, median, 90th percentile and proportion of acceptable test (PAT), are in use since almost 40 years and to date represent the mainstay for gauging the laboratory timeliness. In this study, we investigated the performance of the Six Sigma Z-score, which was previously introduced as a device for the quantitative assessment of timeliness.

METHODS

A numerical simulation was obtained modeling the actual TAT data set using the log-logistic probability density function. Five thousand replicates for each size of the artificial TAT random sample (n=20, 50, 250 and 1000) were generated, and different laboratory conditions were simulated manipulating the PDF in order to generate more or less variable data. The Z-score and the classic TAT indicators were assessed for precision (%CV), robustness toward right-tailing (precision at different sample variability), sensitivity and specificity.

RESULTS

Z-score showed sensitivity and specificity comparable to PAT (≈80% with n≥250), but superior precision that ranged within 20% by moderately small sized samples (n≥50); furthermore, Z-score was less affected by the value of the cutoff used for setting the acceptable TAT, as well as by the sample variability that reflected into the magnitude of right-tailing.

CONCLUSIONS

The Z-score was a valid indicator of laboratory timeliness and a suitable device to improve as well as to maintain the achieved quality level.

Experimental fusion of different versions of the total laboratory automation system and improvement of laboratory turnaround time

BACKGROUND

Use of total laboratory automation (TLA) system has expanded to microbiology and hemostasis and upgraded to second and third generations. We herein report the first successful upgrades and fusion of different versions of the TLA system, thus improving laboratory turnaround time (TAT).

METHODS

A 21-day schedule was planned from the time of pre-meeting to installation and clinical sample application. We analyzed the monthly TAT in each menu, distribution of the "out of range for acceptable TAT" samples, and "prolonged time out of acceptable TAT," before and after the upgrade and fusion.

RESULTS

We installed and customized hardware, middleware, and software. The one-way CliniLog 2.0 version track, 50.0-m long, was changed to a 23.2-m long one-way 2.0 version and an 18.7-m long two-way 4.0 version. The monthly TAT in the outpatient samples, before and after upgrading the TLA system, were uniformly satisfactory in the chemistry and viral marker menus. However, in the tumor marker menu, the target TAT (98.0% of samples ≤60 minutes) was not satisfied during the familiarization period. There was no significant difference in the proportion of "out of acceptable TAT" samples, before and after the TLA system upgrades (7.4‰ and 8.5‰). However, the mean "prolonged time out of acceptable TAT" in the chemistry samples was significantly shortened to 17.4 (±24.0) minutes after the fusion, from 34.5 (±43.4) minutes.

CONCLUSIONS

Despite experimental challenges, a fusion of the TLA system shortened the "prolonged time out of acceptable TAT," indicating a distribution change in overall TAT.

Clinical Laboratory Automation: A Case Study

Background

This 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 Methods

The 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.

Results

The 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.

Conclusions

The 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.

Total laboratory automation: Do stat tests still matter?

During the past decades the healthcare systems have rapidly changed and today hospital care is primarily advocated for critical patients and acute treatments, for which laboratory test results are crucial and need to be always reported in predictably short turnaround time (TAT). Laboratories in the hospital setting can face this challenge by changing their organization from a compartmentalized laboratory department toward a decision making-based laboratory department. This requires the implementation of a core laboratory, that exploits total laboratory automation (TLA) using technological innovation in analytical platforms, track systems and information technology, including middleware, and a number of satellite specialized laboratory sections cooperating with care teams for specific medical conditions. In this laboratory department model, the short TAT for all first-line tests performed by TLA in the core laboratory represents the key paradigm, where no more stat testing is required because all samples are handled in real-time and (auto)validated results dispatched in a time that fulfills clinical needs. To optimally reach this goal, laboratories should be actively involved in managing all the steps covering the total examination process, speeding up also extra-laboratory phases, such sample delivery. Furthermore, to warrant effectiveness and not only efficiency, all the processes, e.g. specimen integrity check, should be managed by middleware through a predefined set of rules defined in light of the clinical governance.

Timeliness “at a glance”: assessing the turnaround time through the six sigma metrics

Almost thirty years of systematic analysis have proven the turnaround time to be a fundamental dimension for the clinical laboratory. Several indicators are to date available to assess and report quality with respect to timeliness, but they sometimes lack the communicative immediacy and accuracy. The six sigma is a paradigm developed within the industrial domain for assessing quality and addressing goal and issues. The sigma level computed through the Z-score method is a simple and straightforward tool which delivers quality by a universal dimensionless scale and allows to handle non-normal data. Herein we report our preliminary experience in using the sigma level to assess the change in urgent (STAT) test turnaround time due to the implementation of total automation. We found that the Z-score method is a valuable and easy to use method for assessing and communicating the quality level of laboratory timeliness, providing a good correspondence with the actual change in efficiency which was retrospectively observed.