Key Performance Indicators to Measure Improvement After Implementation of Total Laboratory Automation Abbott Accelerator a3600

Laboratory testing consolidation and total laboratory automation improves service efficiency and effectiveness: a study of a medical center in Taiwan

BACKGROUND

Test consolidation and total laboratory automation (TLA) were implemented in a core laboratory with a high volume of specimens in a medical center in Taiwan to reduce the costs of laboratory services and improve laboratory workflow and performance.

METHODS

Using a retrospective research approach, 5 stat and 7 routine tests were used to analyze the in-laboratory to report turnaround time (IR-TAT). Mean, SD, medium, 90th percentile, outlier percentage of IR-TAT, full-time equivalents, productivity, tube touch moment (TTM), and financial impact were determined and compared pre- and post-TLA.

RESULTS

The mean IR-TAT of overall stat chemical tests for inpatient and outpatient were 32.8% and 11.9% reductions, respectively. The productivity of each medical technologist increased by 32.4% per month, and there was a reduction of 5 medical technologists compared with the number required to complete the same tests before consolidation. The TTM of staff per year post-TLA decreased by 74.1% tube touches.

CONCLUSION

The efficiency of laboratory services was improved by consolidation to the core laboratory along with TLA implementation coupled with logic rules such as delta-check and autoverification. Effectiveness was improved as measured by an increase in productivity, labor reduction, staff safety, and cost reduction.

Impacts of the advancement in artificial intelligence on laboratory medicine in low- and middle-income countries: Challenges and recommendations-A literature review

Background and Aims

Artificial intelligence (AI) has emerged as a transformative force in laboratory medicine, promising significant advancements in healthcare delivery. This study explores the potential impact of AI on diagnostics and patient management within the context of laboratory medicine, with a particular focus on low- and middle-income countries (LMICs).

Methods

In writing this article, we conducted a thorough search of databases such as PubMed, ResearchGate, Web of Science, Scopus, and Google Scholar within 20 years. The study examines AI's capabilities, including learning, reasoning, and decision-making, mirroring human cognitive processes. It highlights AI's adeptness at processing vast data sets, identifying patterns, and expediting the extraction of actionable insights, particularly in medical imaging interpretation and laboratory test data analysis. The research emphasizes the potential benefits of AI in early disease detection, therapeutic interventions, and personalized treatment strategies.

Results

In the realm of laboratory medicine, AI demonstrates remarkable precision in interpreting medical images such as radiography, computed tomography, and magnetic resonance imaging. Its predictive analytical capabilities extend to forecasting patient trajectories and informing personalized treatment strategies using comprehensive data sets comprising clinical outcomes, patient records, and laboratory results. The study underscores the significance of AI in addressing healthcare challenges, especially in resource-constrained LMICs.

Conclusion

While acknowledging the profound impact of AI on laboratory medicine in LMICs, the study recognizes challenges such as inadequate data availability, digital infrastructure deficiencies, and ethical considerations. Successful implementation necessitates substantial investments in digital infrastructure, the establishment of data-sharing networks, and the formulation of regulatory frameworks. The study concludes that collaborative efforts among stakeholders, including international organizations, governments, and nongovernmental entities, are crucial for overcoming obstacles and responsibly integrating AI into laboratory medicine in LMICs. A comprehensive, coordinated approach is essential for realizing AI's transformative potential and advancing health care in LMICs.

Implementation of an interactive mobile application to pilot a rapid assay to detect HIV drug resistance mutations in Kenya

Usability is an overlooked aspect of implementing lab-based assays, particularly novel assays in low-resource-settings. Esoteric instructions can lead to irreproducible test results and patient harm. To address these issues, we developed a software application based on "Aquarium", a laboratory-operating system run on a computer tablet that provides step-by-step digital interactive instructions, protocol management, and sample tracking. Aquarium was paired with a near point-of-care HIV drug resistance test, "OLA-Simple", that detects mutations associated with virologic failure. In this observational study we evaluated the performance of Aquarium in guiding untrained users through the multi-step laboratory protocol with little supervision. To evaluate the training by Aquarium software we conducted a feasibility study in a laboratory at Coptic Hope Center in Nairobi, Kenya. Twelve volunteers who were unfamiliar with the kit performed the test on blinded samples (2 blood specimens; 5 codons/sample). Steps guided by Aquarium included: CD4+ T-Cell separation, PCR, ligation, detection, and interpretation of test results. Participants filled out a short survey regarding their demographics and experience with the software and kit. None of the laboratory technicians had prior experience performing CD4+ separation and 7/12 had no experience performing laboratory-based molecular assays. 12/12 isolated CD4+ T cells from whole blood with yields comparable to isolations performed by trained personnel. The OLA-Simple workflow was completed by all, with genotyping results interpreted correctly by unaided-eye in 108/120 (90%) and by software in 116/120 (97%) of codons analyzed. In the surveys, participants favorably assessed the use of software guidance. The Aquarium digital instructions enabled first-time users in Kenya to complete the OLA-simple kit workflow with minimal training. Aquarium could increase the accessibility of laboratory assays in low-resource-settings and potentially standardize implementation of clinical laboratory tests.

Implementation of an interactive mobile application to pilot a rapid assay to detect HIV drug resistance mutations in Kenya

Usability is an overlooked aspect of implementing lab-based assays, particularly novel assays in low-resource-settings. Esoteric instructions can lead to irreproducible test results and patient harm. To address these issues, we developed a software application based on "Aquarium", a laboratory-operating system run on a computer tablet that provides step-by-step digital interactive instructions, protocol management, and sample tracking. Aquarium was paired with a near point-of-care HIV drug resistance test, "OLA-Simple", that detects mutations associated with virologic failure. In this observational study we evaluated the performance of Aquarium in guiding untrained users through the multi-step laboratory protocol with little supervision. To evaluate the training by Aquarium software we conducted a feasibility study in a laboratory at Coptic Hope Center in Nairobi, Kenya. Twelve volunteers who were unfamiliar with the kit performed the test on blinded samples (2 blood specimens; 5 codons/sample). Steps guided by Aquarium included: CD4+ T-Cell separation, PCR, ligation, detection, and interpretation of test results. Participants filled out a short survey regarding their demographics and experience with the software and kit. None of the laboratory technicians had prior experience performing CD4+ separation and 7/12 had no experience performing laboratory-based molecular assays. 12/12 isolated CD4+ T cells from whole blood with yields comparable to isolations performed by trained personnel. The OLA-Simple workflow was completed by all, with genotyping results interpreted correctly by unaided-eye in 108/120 (90%) and by software in 116/120 (97%) of codons analyzed. In the surveys, participants favorably assessed the use of software guidance. The Aquarium digital instructions enabled first-time users in Kenya to complete the OLA-simple kit workflow with minimal training. Aquarium could increase the accessibility of laboratory assays in low-resource-settings and potentially standardize implementation of clinical laboratory tests.

Economic Evaluation of Total Laboratory Automation in the Clinical Laboratory of a Tertiary Care Hospital

Background

Total laboratory automation (TLA) is an innovation in laboratory technology; however, the high up-front costs restrict its widespread adoption. To examine whether the capital investment for TLA is worthwhile, we analyzed its clinical- and cost-effectiveness for the expected payback period.

Methods

Clinical chemistry tests and immunoassays performed in the clinical laboratory of a tertiary care hospital were divided into a post-TLA group, including 1,182,419 tests performed during December 2019, and a pre-TLA group, including 1,151,501 tests performed during December 2018. Laboratory information system data were used to measure clinical effectiveness, and depreciation data were used to calculate TLA costs.

Results

Laboratory performance improved after TLA adoption in all four key performance indicators mean turn-around time (TAT), representing the timeliness of result reporting, decreased by 6.1%; the 99th percentile of TAT, representing the outlier rate, decreased by 13.3%; the TAT CV, representing predictability, decreased by 70.0%; and weighted tube touch moment (wTTM), representing staff safety, improved by 77.6%. Based on these effectiveness results, economic evaluation was performed using two approaches. First, the incremental cost-effectiveness ratio and wTTM were used as the most cost-effective performance indicators. Second, the expected payback period was calculated. Considering only staff cost reduction, it was anticipated that 4.75 yrs would be needed to payback the initial investment.

Conclusions

TLA can significantly enhance laboratory performance, has a relatively quick payback period, and can reduce total hospital expenses in the long term. Therefore, the capital investment for TLA adoption is considered to be worthwhile.

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.

IoT Based Predictive Maintenance Management of Medical Equipment

Technological advancements are the main drivers of the healthcare industry as it has a high impact on delivering the best patient care. Recent years witnessed unprecedented growth in the number of medical equipment manufactured to aid high-quality patient care at a fast pace. With this growth of medical equipment, hospitals need to adopt optimal maintenance strategies that enhance the performance of their equipment and attempt to reduce their maintenance costs and effort. In this work, a Predictive Maintenance (PdM) approach is presented to help in failure diagnosis for critical equipment with various and frequent failure mode(s). The proposed approach relies on the understanding of the physics of failure, real-time collection of the right parameters using the Internet of Things (IoT) technology, and utilization of machine learning tools to predict and classify healthy and faulty equipment status. Moreover, transforming traditional maintenance into PdM has to be supported by an economic analysis to prove the feasibility and efficiency of transformation. The applicability of the approach was demonstrated using a case study from a local hospital in the United Arab Emirates (UAE) where the Vitros-Immunoassay analyzer was selected based on maintenance events and criticality assessment as a good candidate for transforming maintenance from corrective to predictive. The dominant failure mode is metering arm belt slippage due to wear out of belt and movement of pulleys which can be predicted using vibration signals. Vibration real data is collected using wireless accelerometers and transferred to a signal analyzer located on a cloud or local computer. Features extracted and selected are analyzed using Support Vector Machine (SVM) to detect the faulty condition. In terms of economics, the proposed approach proved to provide significant diagnostic and repair cost savings that can reach up to 25% and an investment payback period of one year. The proposed approach is scalable and can be used across medical equipment in large medical centers.

Integrity of serum samples is changed by modified centrifugation conditions

Background Serum samples should be centrifuged for at least 10 min at 1300-2500 × g. Changed centrifugation conditions could compromise sample quality. The objective of this study was to compare the serum quality and turnaround time (TAT) using different centrifugation conditions. Methods The study was done in four different periods (A, B, C and D) at different conditions: for 10, 5 and 7 (A, B and C, respectively) at 2876 × g, and 7 (D) min at 4141 × g. Sample quality was assessed as the proportion of samples with: (a) aspiration errors, (b) H index >0.5 g/L and (c) suppressed reports of potassium (K) due to hemolysis. TAT was calculated for emergency samples. The proportions of samples (a), (b) and (c) were compared according to period A. Results The number of aspiration errors was significantly higher in samples centrifuged at 2876 × g for 5 min (p = 0.021) and remained unchanged when centrifuged for 7 min (p = 0.066 and 0.177, for periods C and D, respectively). In periods B, C and D, the proportion of samples with hemolysis was higher than that in period A (p-values 0.039, 0.009 and 0.042, respectively). TAT differed between all periods (p < 0.001), with the lowest TAT observed for B and D. The lowest number of samples exceeding 60-min TAT was observed in period D (p = 0.011). Conclusions The integrity of serum samples is changed with different centrifugation conditions than those recommended. Our study showed that shorter centrifugation at higher force (7 min at 4141 × g) significantly decreases TAT, with unchanged proportion of samples with aspiration errors.