Standards, Volume 3, Issue 3 (September 2023) – 5 articles

The translational research community, in general, and the Clinical and Translational Science Awards (CTSA) community, in particular, share the vision of repurposing EHRs for research that will improve the quality of clinical practice. Many members of these communities are also aware that electronic health records (EHRs) suffer limitations of data becoming poorly structured, biased, and unusable out of original context. This creates obstacles to the continuity of care, utility, quality improvement, and translational research. Analogous limitations to sharing objective data in other areas of the natural sciences have been successfully overcome by developing and using common ontologies. This White Paper presents the authors’ rationale for the use of ontologies with computable semantics for the improvement of clinical data quality and EHR usability formulated for researchers with a stake in clinical and translational science and who are advocates for the use of information technology in medicine but at the same time are concerned by current major shortfalls. This White Paper outlines pitfalls, opportunities, and solutions and recommends increased investment in research and development of ontologies with computable semantics for a new generation of EHRs. Full article

This study collected 220 commercially available samples of γ-Aminobutyric acid (GABA) tea produced in Taiwan from 2016 to 2021. The 220 tea samples were categorized into five types of GABA tea, including 108 GABA Oolong tea, 71 GABA Black tea, 21 GABA Paochong tea, 12 GABA Green tea, and 8 GABA Puerh tea samples. The most common type of GABA tea in Taiwan is GABA Oolong tea, followed by GABA Black tea. The physico-chemical constituents and consumer acceptance of the GABA tea samples were analyzed. The GABA content varied among the different types of GABA tea: GABA Oolong tea ranged from 128–286 mg/100 g, GABA Black tea ranged from 182–360 mg/100 g, GABA Paochong tea ranged from 98–203 mg/100 g, GABA Green tea ranged from 56–174 mg/100 g, and GABA Puerh tea ranged from 142–191 mg/100 g. In terms of the commercial standard of GABA tea, 22 out of the 220 GABA tea samples failed to meet the commercial standard, with a failure rate of 10%. During the fermentation process of GABA tea, the contents of GABA increased significantly, but the total polyphenol and total catechin contents remained stable. In terms of consumer acceptance, GABA Black tea is the most accepted by consumers, followed by GABA Puerh tea, GABA Paochong, and GABA Oolong tea. The sour flavor in GABA tea is similar to the original sour sensory properties found in black tea. It is assumed that this is the main reason GABA Black tea has the highest acceptance. Full article

In major metropolitan areas, the growing levels of congestion pose a significant risk of supply chain disruptions by hindering surface transportation of commodities. To address this challenge, cargo drones are emerging as a potential mode of transport that could improve the reliability of the pharmaceutical supply chain and enhance healthcare. This study proposes a novel hybrid workflow that combines machine learning and a geographic information system to identify the fewest locations where providers can initiate cargo drone services to yield the greatest initial benefits. The results show that by starting a service in only nine metropolitan areas across four regions of the contiguous United States, drones with a robust 400-mile range can initially move more than 28% of the weight of all pharmaceuticals. The medical community, supply chain managers, and policymakers worldwide can use this workflow to make data-driven decisions about where to access the largest opportunities for pharmaceutical transport by drones. The proposed approach can inform policies and standards such as Advanced Air Mobility to help address supply chain disruptions, reduce transportation costs, and improve healthcare outcomes. Full article

The aim of this article is to demonstrate the advantages of the Composition of Probabilistic Preferences method in multicriteria problems with data from Likert scales. Multicriteria decision aids require a database as a decision matrix, in which two or more alternatives are evaluated according to two or more variables selected as decision criteria. Several problems of this nature use measures by Likert scales. Depending on the method, parameters from these data (e.g., means, modes or medians) are required for calculations. This parameterization of data in ordinal scales has fueled controversy for decades between authors who favor mathematical/statistical rigor and argue against the procedure, stating that ordinal scales should not be parameterized, and scientists from other areas who have shown gains from the process that compensates for this relaxation. The Composition of Probabilistic Preferences can allay the protests raised and obtain more accurate results than descriptive statistics or parametric models can bring. The proposed algorithm in R-code involves the use of probabilities with empirical distributions and fitting histograms of data measured by Likert scales. Two case studies with simulated datasets having peculiar characteristics and a real case illustrate the advantages of the Composition of Probabilistic Preferences. Full article

Human beings experience a large fraction of their exposure to air pollutants in indoor environments. Air pollution is a large environmental health risk, and exposure to ambient air pollution and indoor air pollution contribute equally to the total number of fatalities worldwide. Although legislative authorities have established limit values for ambient outdoor air and stack emissions, there are inconsistent and variable national and regional limit values for gaseous substances and airborne particulate matter in the built environment (schools, homes, healthcare facilities, offices, and other public spaces). This lack of regulation is unsurprising, because indoor spaces are characterized by complex air chemistry, and their construction materials and types of activities vary significantly. The current understanding of indoor pollutants, including short-lived oxidants, degradation of VOCs, particle formation, and particle composition, is incomplete. It is necessary to identify and assess emerging pollutants and their toxicity, and to consider new consumer products and green construction materials and their impact on indoor air quality (IAQ). Learning from IAQ surveys and audit protocols, research methodologies should be regularized for cross-research comparisons. Some indoor air quality guidance and standards have been written, and several more are in development, with the international ISO 16000 series of indoor standards leading the way for improving indoor air data quality. The WHO has established some ambient air limit values which can mostly be translated into indoor limit values. The built environment needs to harmonize energy efficiency, thermal comfort and air quality standards and guidance. In this review, we discuss the next steps for improving international, regional and national standards and guidance, leading to better and more complete indoor air quality regulations. Full article