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.

Development of a Low-Power Instrumented Mouthpiece for Directly Measuring Head Acceleration in American Football

Instrumented mouthpieces (IM) offer a means of measuring head impacts that occur in sport. Direct measurement of angular head kinematics is preferential for accuracy; however, existing IMs measure angular velocity and differentiate the measurement to calculate angular acceleration, which can limit bandwidth and consume more power. This study presents the development and validation of an IM that uses new, low-power accelerometers for direct measurement of linear and angular acceleration over a broad range of head impact conditions in American football. IM sensor accuracy for measuring six-degree-of-freedom head kinematics was assessed using two helmeted headforms instrumented with a custom-fit IM and reference sensor instrumentation. Head impacts were performed at 10 locations and 6 speeds representative of the on-field conditions associated with injurious and non-injurious impacts in American football. Sensor measurements from the IM were highly correlated with those from the reference instrumentation located at the maxilla and skull center of gravity. Based on pooled data across headform and impact location, R² ≥ 0.94, mean absolute error (AE) ≤ 7%, and mean relative impact angle ≤ 11° for peak linear and angular acceleration and angular velocity while R² ≥ 0.90 and mean AE ≤ 7% for kinematic-based injury metrics used in helmet tests.

Methodological and statistical issues in developing an External Quality Assessment scheme in laboratory medicine: Focus on biomarker research

External Quality Assessment (EQA) schemes are well-established tools with which to evaluate, monitor and improve the output quality of clinical laboratories, recognising that high quality laboratory medicine is essential for patient care. EQA programs involve the testing of multiple laboratories and the statistical comparison of their results, according to a multistep workflow. New clinical laboratory activities, such as biomarker research, require new EQA schemes. Critical elements in designing EQA programs are choosing the statistical methods and defining reference values and control limits. This article summarizes the key features of an EQA scheme, including designing the study, identifying reference values and control limits for qualitative and quantitative data, and graphically reporting laboratory performance statistics. These steps are illustrated with examples taken from the authors' experience in national and international quality assessment schemes for biomarker research.

Effectiveness of the National Quality Control Program in Mycology of Argentina and performance evaluation of participating laboratories

The National Quality Control Program in Mycology (PNCCM) of Argentina was established in 1996 to improve the quality of the mycological diagnosis, to help establish and to set up standardized procedures and continuous training of laboratory staff. The aim of this study was to assess the effectiveness of the PNCCM in the 1996-2018 period. Data from the National Mycology Laboratory Network (NMLN) and PNCCM database was used to estimate the increase in the number of controlled laboratories and jurisdictions, the percentage of participation, the improvement in the quality of results and the adherence to the program. Satisfaction surveys were performed to assess user satisfaction. The number of controlled laboratories increased from 29 to 146; participation increased from 49% to 93% and general adherence was 72% in the evaluated period (1996-2018). Improvement in the quality of the results was 15% for low complexity samples; 7% for intermediate complexity samples and 14% for the identification of high complexity strains. Up to 84% of the users consider the PNCCM to be "very good" and 16% "satisfactory". These results show the importance of the PNCCM, which is widely accepted by mycological diagnostic laboratories from Argentina.

Implementation of large-scale laboratory-based detection of COVID-19 in the Veterans Health Administration, March 2020 - February 2021

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presented numerous operational challenges to healthcare delivery networks responsible for implementing large scale detection of Coronavirus Disease 2019 (COVID-19), the infection caused by SARS-CoV-2. We describe testing performance, review data quality metrics, and summarize experiences during the scale up of laboratory-based detection of COVID-19 in the Veterans Health Administration, the largest healthcare system in the United States. During March 2020 to February 2021, we observed rapid increase in testing volume, decreases in test turnaround time, improvements in testing of hospitalized persons, changes in test positivity, and varying utilization of different tests. Though performance metrics improved over time, surges challenged testing capacity and data quality remained suboptimal. Future planning efforts should focus on fortifying supply chains for consumables and equipment repair, optimizing distribution of testing workload across laboratories, and improving informatics to accurately monitor operations and intent for testing during a public health emergency.