Impact of reference change value (RCV) based autoverification on turnaround time and physician satisfaction

Approaching sustainability in Laboratory Medicine

INTRODUCTION

Clinical laboratories and the total testing process are major consumers of energy, water, and hazardous chemicals, and produce significant amounts of biomedical waste. Since the processes in the clinical laboratory and the total testing process go hand in hand it mandates a holistic, and comprehensive approach towards sustainability.

CONTENT

This review article identifies the various sources and activities in Laboratory Medicine that challenge sustainability and also discusses the various approaches that can be implemented to achieve sustainability in laboratory operations to reduce the negative impact on the environment.

SUMMARY

The article highlights how the integration of technological advancements, efficient resource management, staff training and sensitization, protocol development towards sustainability, and other environmental considerations contributes significantly to a sustainable healthcare ecosystem.

OUTLOOK

Variables and resources that negatively impact the environment must be identified and addressed comprehensively to attain a long-lasting level of carbon neutrality.

Designing and validating an autoverification system of biochemical test results in Hatay Mustafa Kemal University, clinical laboratory

Introduction

Autoverification (AV) is a postanalytical tool that uses algorithms to validate test results according to specified criteria. The Clinical and Laboratory Standard Institute (CLSI) document for AV of clinical laboratory test result (AUTO-10A) includes recommendations for laboratories needing guidance on implementation of AV algorithms. The aim was to design and validate the AV algorithm for biochemical tests.

Materials and methods

Criteria were defined according to AUTO-10A. Three different approaches for algorithm were used as result limit checks, which are reference range, reference range ± total allowable error, and 2nd and 98th percentile values. To validate the algorithm, 720 cases in middleware were tested. For actual cases, 3,188,095 results and 194,520 reports in laboratory information system (LIS) were evaluated using the AV system. Cohen’s kappa (κ) was calculated to determine the degree of agreement between seven independent reviewers and the AV system.

Results

The AV passing rate was found between 77% and 85%. The highest rates of AV were in alanine transaminase (ALT), direct bilirubin (DBIL), and magnesium (Mg), which all had AV rates exceeding 85%. The most common reason for non-validated results was the result limit check (41%). A total of 328 reports evaluated by reviewers were compared to AV system. The statistical analysis resulted in a κ value between 0.39 and 0.63 (P < 0.001) and an agreement rate between 79% and 88%.

Conclusions

Our improved model can help laboratories design, build, and validate AV systems and be used as starting point for different test groups.

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.

General position of Croatian medical biochemistry laboratories on autovalidation: survey of the Working Group for Post-analytics of the Croatian Society of Medical Biochemistry and Laboratory Medicine

Introduction

Autovalidation (AV) is an algorithm based on predefined rules designed, among others, to automate and standardize the postanalytical phase of laboratory work. The aim of this study was to examine the overall opinion of Croatian medical biochemistry laboratories regarding various aspects of AV.

Material and methods

This retrospective study is an analysis of the responses of a survey about AV comprised of 18 questions, as part of Module 10 (“Postanalytical phase of laboratory testing”) of national External Quality Assessment program, administered by the Croatian Centre for Quality Assessment in Laboratory Medicine. Results were reported as percentages of total number of participants in survey or as proportions of observed data if the overall number of data was <100.

Results

121 laboratories responded to the survey, of which 76% do not use AV, while 11% of laboratories use AV in routine laboratory work. 16/29 laboratories implemented semi-automated AV for general biochemistry (7/29), haematology (5/29), and coagulation (4/29) tests. Analytical measurement ranges, critical values, flags from analysers, interference indices and delta check were the most commonly used rules in the algorithm. 12/29 laboratories performed validation of AV with less than 500 samples (8/29). 7/13 laboratories report the percentage of AV being 20-50%, while 10/13 answered that introduction of AV significantly reduced turnaround time (TAT) (for 20 - 25%), especially for biochemistry tests.

Conclusions

Despite of its numerous benefits (i.e. shorter TAT, less manual validation, standardization of the postanalytical phase), only a small number of Croatian laboratories use AV.

Clinical Application of Overlapping Confidence Intervals for Monitoring Changes in Serial Clinical Chemistry Test Results

Background

Interpretation of changes in serial laboratory results is necessary for both clinicians and laboratories; however, setting decision limits is not easy. Although the reference change value (RCV) has been widely used for auto-verification, it has limitations in clinical settings. We introduce the concept of overlapping confidence intervals (CIs) to determine whether the changes are statistically significant in clinical chemistry laboratory test results.

Methods

In total, 1,202,096 paired results for 33 analytes routinely tested in our clinical chemistry laboratory were analyzed. The distributions of delta% absolute values and cut-off values for certain percentiles were calculated. The CIs for each analyte were set based on biological variation, and data were analyzed at various confidence levels. Additionally, we analyzed the data using RCVs and compared their clinical utility.

Results

Most analytes had low indexes of individuality with large inter-individual variability. The 97.5th percentile cut-offs for each analyte were much larger than conventional RCVs. The percentages of results exceeding RCV_95% and RCV_99% corresponded to those with no overlap at the 83.4% and 93.2% confidence levels, respectively.

Conclusions

The use of overlapping CIs in serial clinical chemistry test results can overcome the limitations of existing RCVs and replace them, especially for analytes with large intra-individual variation.

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.

Autoverification process improvement by Six Sigma approach: Clinical chemistry & immunoassay

OBJECTIVE

This study examines effectiveness of a project to enhance an autoverification (AV) system through application of Six Sigma (DMAIC) process improvement strategies.

DESIGN AND METHODS

Similar AV systems set up at three sites underwent examination and modification to produce improved systems while monitoring proportions of samples autoverified, the time required for manual review and verification, sample processing time, and examining characteristics of tests not autoverified. This information was used to identify areas for improvement and monitor the impact of changes.

RESULTS

Use of reference range based criteria had the greatest impact on the proportion of tests autoverified. To improve AV process, reference range based criteria was replaced with extreme value limits based on a 99.5% test result interval, delta check criteria were broadened, and new specimen consistency rules were implemented. Decision guidance tools were also developed to assist staff using the AV system. The mean proportion of tests and samples autoverified improved from <62% for samples and <80% for tests, to >90% for samples and >95% for tests across all three sites. The new AV system significantly decreased turn-around time and total sample review time (to about a third), however, time spent for manual review of held samples almost tripled. There was no evidence of compromise to the quality of testing process and <1% of samples held for exceeding delta check or extreme limits required corrective action.

CONCLUSIONS

The Six Sigma (DMAIC) process improvement methodology was successfully applied to AV systems resulting in an increase in overall test and sample AV by >90%, improved turn-around time, reduced time for manual verification, and with no obvious compromise to quality or error detection.