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After the passage of the Agriculture Improvement Act of 2018 (Farm Bill) that defined hemp as Cannabis sativa L. if it contained ≤0.3 % Δ9-THC on a dry weight basis, forensic laboratories were faced with the challenge of distinguishing marijuana from hemp in seized drug samples. This study reports the results of an interlaboratory validation of a previously developed qualitative decision-point assay for the differentiation of illegal marijuana from legal hemp, utilizing gas chromatography-mass spectrometry (GC–MS) with a 1 % Δ9-THC threshold. The method was validated in terms of selectivity, linearity, carryover, precision, accuracy, extract stability, dilution integrity, and measurement uncertainty. Other elements such as decarboxylation rate and potential for CBD interference were also evaluated. Specificity and positive predictive value were both 100 % using known cannabis reference materials. A total of 8 false negative results were observed among the 280 analyses, resulting in an overall assay sensitivity of 94 % and a negative predictive value of 95 %. The small number of false negative results near the 1 % threshold were attributed to decarboxylation inefficiency in some laboratories. While qualitative in nature, the measurement uncertainty of the assay at the 0.3 % Δ9-THC cutoff (legal threshold) using a 95.45 % confidence interval (k = 2) ranged from 12.2 % to 21.8 % among laboratories (equivalent to 0.3 % ± 0.04 % to 0.07 % Δ9-THC by weight). A one-year retrospective study across three agencies and fifteen laboratories was undertaken. Among the three agencies participating in the original study, 93–96 % of all suspected cannabis seizures yielded results above the administrative threshold of 1 % Δ9-THC (n = 3,288). The interlaboratory method development highlighted differences in performance between sites, ultimately improving the overall robustness of the method. Observations highlight the need for site-specific validation and vigilance. While multi-agency collaborations and interlaboratory validations are valuable, they do not replace the necessity for full, independent, and rigorous validation.

Laboratory analytical turnaround time represents laboratory effectiveness. Our study aimed to evaluate laboratory analytical turnaround time to optimize workflow and shorten analytical turnaround time. Methods: We used the laboratory information system in a 2000-bed teaching hospital to compute and analyze the 90th percentile turnaround time of the Stat Laboratory from 2001 to 2003. Results: The overall 90th percentile turnaround time in the Stat Laboratory was 40–49 minutes and positively correlated with test volume. The daily test volume in the Stat Laboratory has grown significantly in the latter 2 half-years of the study as compared with the previous 2 half-years (p < 0.05 and p < 0.001, respectively). The daily longest turnaround time occurred in the early morning, and troponin-I testing contributed to the majority of incidences of prolongation of analytical turnaround time. We prioritized the performance of troponin-I testing, which resulted in a reduction of the analytical turnaround time by about 18 minutes (from 66 to 48 minutes) and no increment of overall turnaround time (42 to 44 minutes) despite continuously increasing test volume. Conclusion: These findings demonstrated that a dedicated means of process control was able to significantly improve laboratory efficiency.