Laboratory Automation and Intra-Laboratory Turnaround Time: Experience at the University Hospital Campus Bio-Medico of Rome

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.

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).

Innovation, Automation and Informatics Improves Quality in Lerdsin Hospital, Thailand

This paper describes a planned, continuous improvement journey, of a laboratory that has installed a system with a single sample touch from blood draw to result. To achieve this, physical connectivity of systems from phlebotomy through pre-analytical to the analytical phase were paired with informatics connectivity from the patient's national identity card to the hospital and laboratory informatics management systems (LIMS) and associated middleware. This allowed accurate time stamps to track turnaround time (TAT). TAT metrics were collected from the LIMS for inpatient, emergency room and outpatient samples and tests over a period of 7 months. This time span incorporated the 2-month period before automation was implemented. The results for all tests and specific tests are shown and the results of an analysis of the outpatient phlebotomy workflow are given. The implemented solution has improved outpatient TAT by over 54% and has shown that samples can be collected, and results obtained without touching the sample. Improving intra-laboratory TAT is an important quality goal for all laboratories. The implementation of automation is important in achieving this albeit more about obtaining predictable TAT. Automation does not necessarily improve TAT it removes variation which leads to predictable TAT (PTAT). Automation should only be considered with a strategic vision for the future as it is important to have clear goals and objectives based on the individual laboratories process and needs. Automating a poor process leads to an automated poor process. Here, an innovative use of automation, hardware and software has resulted in marked improvement in TAT across all samples processed in the central laboratory.

Quality improvement of outpatient clinical chemistry tests through a novel middleware-laboratory information system solution

OBJECTIVES

Rapid and accurate laboratory tests are essential to support clinical decision-making. Despite the various efforts to control quality in the laboratory, our outpatient chemistry turnaround time (TAT) has deteriorated since 2018. Moreover, these difficulties have accelerated further due to the COVID-19 pandemic. Therefore, we aimed to improve laboratory work efficiency by identifying and eliminating the causes of reduced laboratory work efficiency.

DESIGN & METHODS

We surveyed to identify tasks that reduce work efficiency. Based on our survey, a new-concept of work assistance middleware linked to laboratory information system (LIS) was developed. The middleware supports test end-time prediction, automatic real-time TAT monitoring, and urgent test requests so that medical technologists can focus on their chemistry tests. The developed middleware was used for 6 months in laboratory and outpatient clinics, and its effectiveness was evaluated.

RESULTS

The median TAT for outpatient chemistry tests was reduced by 6.6 min, from 72.4 min to 65.8 min. And not only did the maximum TAT for the sample decrease from 353 min to 214 min, but the proportion of samples exceeding the TAT target (120 min) also decreased by 77%; from 2.00% in 2010 (1,905 out of 94,989 samples) to 0.46% in 2021 (453 out of 98,117 samples). 2,199 samples were urgently requested through middleware, and they were processed about 15% faster than other samples, effectively performing urgent tests. The test end-time prediction showed an error of 8.6 min in the evaluation using the MAE (Mean Absolute Error) index.

CONCLUSIONS

Through this study, the quality and efficiency of the laboratory were improved, and while reducing the workload of medical staff, it contributed to enhancing patient safety and satisfaction.

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.

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.

Improving turnaround time of molecular diagnosis of Middle East respiratory syndrome coronavirus in a hospital in Saudi Arabia

Background

There have been 2562 laboratory-confirmed cases of Middle East respiratory syndrome coronavirus (MERS-CoV) in 27 countries, with a case fatality rate of 34.5%. Data on the turnaround time (TAT) are lacking. We report TAT for MERS-CoV samples over time.

Methods

This is a monocentric study and the TAT for the reporting of 2664 MERS-CoV polymerase chain reaction (PCR) results were calculated in hours from the time of the receipt of respiratory samples to the reporting of the results.

Results

The mean TAT±standard deviation was significantly lower in 2018 compared with previous years (19.25±13.8). The percentage of samples processed within 24 h increased from 42.3% to 73.8% in 2015 and 2018, respectively (p<0.0001). The mean TAT was 19.2 h in 2018 and was significantly lower than previous years.

Conclusions

The TAT for the MERS-CoV results decreased during the study period. Timely reporting of MERS-CoV PCR results may aid in further enhancing infection control measures.

Consolidation of Clinical Microbiology Laboratories and Introduction of Transformative Technologies

Clinical microbiology is experiencing revolutionary advances in the deployment of molecular, genome sequencing-based, and mass spectrometry-driven detection, identification, and characterization assays. Laboratory automation and the linkage of information systems for big(ger) data management, including artificial intelligence (AI) approaches, also are being introduced. The initial optimism associated with these developments has now entered a more reality-driven phase of reflection on the significant challenges, complexities, and health care benefits posed by these innovations. With this in mind, the ongoing process of clinical laboratory consolidation, covering large geographical regions, represents an opportunity for the efficient and cost-effective introduction of new laboratory technologies and improvements in translational research and development. This will further define and generate the mandatory infrastructure used in validation and implementation of newer high-throughput diagnostic approaches. Effective, structured access to large numbers of well-documented biobanked biological materials from networked laboratories will release countless opportunities for clinical and scientific infectious disease research and will generate positive health care impacts. We describe why consolidation of clinical microbiology laboratories will generate quality benefits for many, if not most, aspects of the services separate institutions already provided individually. We also define the important role of innovative and large-scale diagnostic platforms. Such platforms lend themselves particularly well to computational (AI)-driven genomics and bioinformatics applications. These and other diagnostic innovations will allow for better infectious disease detection, surveillance, and prevention with novel translational research and optimized (diagnostic) product and service development opportunities as key results.

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.

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.