This 2021 article published in the journal Practical Laboratory Medicine
examines laboratory testing in the scope of quality management and laboratory stewardship programs and the long-term benefits they bring to an organization. White et al.
first discuss the evolution of quality in the laboratory and then introduce the concept of "laboratory stewardship" and its ability to improve patient care. The rest of the work focuses on implementing such a program and presenting a hypothetical case study to show how a laboratory stewardship program can have a practical impact on a healthcare system. The authors conclude that a well-implemented stewardship program "presents a valuable opportunity for laboratory professionals to engage with clinical colleagues and drive change." They also distill their work down to five key elements required for making such programs have maximum impact: "1) a clear vision and organizational alignment; 2) appropriate skills for program execution and management; 3) resources to support the program; 4) incentives to motivate participation; and, 5) a plan of action that articulates program objectives and metrics."
In this 2021 journal article published in Molecules
, Pieracci et al.
present the results of a two-year analysis and comparison of cannabis constituents in the essential oil of 11 hemp genotypes using gas chromatography—mass spectrometry (GC-MS). Noting prior researchers' difficulties in comparing "the chemical composition of the essential oils extracted from different Cannabis sativa
L. genotypes," the researchers strived to conduct a thorough examination, demonstrating the ability of GC-MS methods to make the analyses necessary to determine most constituents, as well as "promote their employment as value-added by-products based on their peculiar characteristics." Through their efforts, the researchers were able to identify 116 compounds, representing 90.6–99.4% of the total composition of the essential oils (EO) gathered. After analyzing their results, the authors concluded "that both the EO chemical profile and extraction yield were significantly influenced by the genotype of the starting material, the year of cultivation, and the interaction between these two factors."
This brief journal article published in Scientific Bulletin
examines how organizations can mitigate the risks of implementing artificial intelligence (AI) and other Industry 4.0 technologies into manufacturing centers, particularly in regards to cybersecurity. Maurice Dawson of the Illinois Institute of Technology first gives a brief introduction on AI and cybersecurity, highlighting that "data science management" incorporates important technology skills, while also highlighting that the recognition of the cybersecurity component of data science management is perhaps lacking. Dawson then discusses the trend of Industry 4.0 and the promise it holds for manufacturers, while also addressing the ever-changing environment and the risks that must be highlighted within it. Noting the importance of manufacturing with the U.S. in particular, the author concludes that "as AI becomes increasingly prominent in critical industries such as manufacturing, it is essential to ensure that proper security controls are in place to thwart any possible threat, whether internal or external."
In this English review article published in the journal Rivista Italiana di Informatica e Diritto
, University of Macerata's Yuan Li "aims to systematically and chronologically describe Chinese regulations for cross-border data exchange." Given the growth of cloud-based systems and data management of clients around the world, doing business with Chinese businesses and individuals requires a better understanding of transfer regulations in the country. Li first explains the concepts behind global data transfer and some of the agreements and soft laws that have sprung up as a result of increasing cross-border transfers, as well as the problems that arise. Li then goes into a detailed review of China's data protection laws, as well as how they are enforced and who enforces them. Then the author examines the data export regulations that have evolved in China. The conclusion is that there are still limitations to China's approaches to building a regulatory framework for data protection and transfer; however, "there are various positive dynamic developments in the framing of China’s cross-border data regulation." Yet further agreements with other nations will still depend on future developments in "multilateral trade and investment negotiations."
In this 2021 article published in Cosmetics
, Jairoun et al.
describe the details of reportedly the first study in the United Arab Emirates (U.A.E.) "to measure to what extent topical cannabinoid-based consumer products contain undeclared tetrahydrocannabinol" (THC). Noting a lack of such studies in the U.A.E. as well as other parts of the world, the researchers used gas chromatography–mass spectrometry (GC-MS) to analyze 18 cannabinoid-based cosmetics manufactured in the U.S. and E.U. and sold in the U.A.E. to determine their undeclared THC and tetrahydrocannabinolic acid (THCA) content. After discussing their methodology, materials, and results, the authors conclude that with the discovery of "high levels of undeclared tetrahydrocannabinol" during their study, there is a real need for "cannabinoid-based cosmetic product producers to issue quality certificates ... [and provide] stricter monitoring and control regarding their [products'] safety and quality."
In this mini review published in Frontiers in Water
, Carriço and Ferreira draw from their personal experiences in the Portuguese water industry to present a case for better data and information management in assessing the condition of existing urban water infrastructure. The authors turn to a collection of 16 performance indicators for assessing water supply systems (WSS) or district metering areas (DMA), as well as the poor state of data management overall in water utilities, as a an impetus for improving the information systems that handle those indicators and related data. They further explain the data requirements for condition assessment of water utility assets, the importance of data integration and interoperability, and the existing barriers to getting those things correct. They close by suggesting future trends, noting however that "many small and medium size utilities worldwide are yet resistant" to such trends, while recommending that "utilities should start to investigate the data they collect and to rethink existing data models."
This mini review article published in the journal Frontiers in Digital Health
outlines the field of "diagnostic informatics," one that uses "digital health tools to facilitate the accuracy and timeliness of health information transfer and enhance the effectiveness of the decision-making processes." Georgiou et al.
focus on three primary aspects of diagnostic informatics: diagnostic errors and associated research, safety and efficacy of test result and follow-up management, and the enhancement of clinical decision support systems. The authors succinctly discuss these three aspects and then provide brief discussion about their findings. They close by noting that for the digital health tools of diagnostic informatics to prove most useful, they must consider the importance of patient-centered care, existing diagnostic processes in use, and of course the critical organizational communication processes in use.
Noting a dearth of published research on "cannabinoid and terpenoid profiles in different hemp phenotypes within the same variety," Eržen et al.
of the Slovenian Institute of Hop Research and Brewing and the University of Ljubljana took matters into their own hands and conducted such a study. The researchers examined 11 phenotypes from three different Cannabis
varieties: Carmagnola Selected (CS), Tiborszallasi (TS), and Finola Selection (FS). Their objective? They intended "to establish a connection between the chemical composition and morphological characteristics of hemp plants and to identify phenotypes with an interesting ratio between cannabinoids for further pharmaceutical applications." After providing background and reasoning for selecting their varieties, the authors discuss the results of their chemical analyses as well as the materials and methods they used. The authors conclude by summarizing their findings, including specific phenotypes that could be prime targets for further cannabis pharmaceutical research.
In this 2021 paper published in BMC Medical Informatics and Decision Making
, Jin et al.
discuss an autoverification and validation system they implemented in their laboratory information systems (LIS) to decrease validation workloads and reduce reporting risks in their healthcare systems. Using a human-machine dialog approach, the authors developed and implemented their autoverification system so that it could record personnel review steps and determine whether the human–machine review results are consistent, while also allowing laboratory personnel to tweak the system further for improved autoverification accuracy. After describing their system in detail, the authors present the results of their two years of use with the system, noting that "in the two years that our online validation has been in use, there have never been any defects or reporting risks due to autoverification" while at the same time enjoying more efficient laboratory verification.
In this 2021 article published in Frontiers in Digital Health
, Spanakis et al.
describe their research efforts towards the development of a more advanced way of securely transmitting and sharing sensitive healthcare data, including by use of blockchain and cloud computing. They first provide background on interoperability and data sharing in healthcare and the challenges that face such activities. This background also includes discussion of an interoperability framework in the European context and how it could be applied to their research. The authors then discuss the current status of standards-based health data exchange and how blockchain fits into that picture. They then propose their Innovative Secure Information Sharing Platform (InSISP), which is "able to support a fast and efficient medical information sharing at both national and cross-national levels, taking into account sharing constraints, including those imposed by the General Data Protection Regulation (GDPR)." Afterwards, they summarize their recommendations based on their InSISP, concluding that their platform has the potential improve healthcare data sharing and transmission, though not without challenges in addressing blockchained healthcare data in the scope of GDPR, as well as the overall complexity and heterogeneity of big healthcare data and the problems that arise.
Electronic health or eHealth solutions are increasingly used in healthcare, including in cloud computing settings. The cloud adds benefits to the use of those eHealth systems, but it also brings with it a number of security challenges, as Sivan and Zukarnain highlight in this 2021 paper. The duo turn to a literature review of eHealth technologies in relation to cloud computing and use that literature to show the strengths and weaknesses of a cloud-based eHealth systems approach. After providing background on cloud computing and the advantages it brings to eHealth systems, the authors identify security issues introduced by moving to the cloud and what security methods can help resolve those issues. They close with a recap of the proposed solutions, future directions for cloud-based eHealth security, and the conclusion that "the future of cloud-based eHealth services will be the integration of file-based and cloud-based applications that integrate a computer-based hybrid IT solution that measures the flexibility and scalability associated with cloud management and healthcare data security."
In this 2020 paper published in PLOS Computational Biology
, Davies et al.
of the University of Manchester discuss their experiences implementing Jupyter Notebooks in bioinformatics and health informatics education. Through these digital notebooks, the researchers were able to help teach future laboratorians with little coding experience how to perform basic coding of health informatics applications, as well as more advanced postgraduates how to apply the notebook to bioinformatics activities. After discussing the inner working of the Jupyter Notebook, the authors explain how such notebooks are able to enhance collaboration and reproducibility in laboratories, as well as improve learning and student assessment in the classroom. They then describe two education-based case studies of using the notebooks, and they describe the end results. The authors conclude that, given their experience as well as their students' experience, such notebooks can act "as a useful resource for learning to code and communicate research findings and analysis" in higher education, with the experience being transferrable to professional work in the future.
Informatics expert Joe Liscouski is back with another short laboratory informatics guide, this time geared towards those who are relatively new to the concept. In this 2021 guide, Liscouski approaches the various concepts surrounding laboratory informatics, but by first addressing the laboratory itself. What types of scientific and laboratory work are conducted? What's the difference between a research laboratory and a testing or "service" laboratory? How are the workflows different between the two? Liscouski notes that documenting data is critical to both types of laboratories, and historically, the paper-based laboratory notebook has been that tool. From there, he makes the logical leap to electronically documenting the same data, first by word processor and then by the electronic laboratory notebook (ELN). From there, integrating these electronic variations is addressed, as well as how other informatics tools like a laboratory information management system (LIMS) and scientific data management system (SDMS) shape and fit into laboratory workflows. He concludes by discussing the actual planning necessary for implementing these and other laboratory informatics solutions in the lab.
What do the internet of things (IoT), cloud computing, and blockchain all bring to the table of healthcare, particularly during a pandemic? Celesti et al.
demonstrate in this 2020 article published in Sensors
that the considerate combination of these technologies can lead to a "scenario where nurses, technicians, and medical doctors belonging to different hospitals cooperate through their federated hospital clouds to form a virtual health team able to carry out a healthcare workflow in secure fashion." The authors show how an IoT-connected laboratory that feeds its instrument data into a federated hospital cloud integrated with a blockchain engine can lead to less patient movement and better security of patient data, while also allowing nurses, doctors, and other practitioners from any of the connected hospitals to review results and issue treatments from a distance. They show the results of several experiments with such a system and conclude by promoting its benefits, as well as the possibility of extending the system to the pharmacy world.
In this brief 2020 paper published in JAMIA Open
, Seifert et al.
of the University of Florida health system share their experiences with taking their Beaker LIS implementation and improving its data tracking capabilities, which in turn improved "trainee education, slide logistics, staffing and instrumentation lobbying, and task tracking" within their anatomic pathology laboratories. After a brief introduction, the authors demonstrate Beaker's status board and weaknesses, and then show how they used Beaker's MyReports module to develop an improved status board. They then state six significant challenges and how they used the adapted LIS tools to solve them. They conclude that "the technical and/or functionality framework that we demonstrated in this manuscript could be adapted by other institutions to address common problems encountered by anatomic pathology laboratories."
In this 2021 paper published in Frontiers in Chemistry
, Myers et al.
of Restek and Verity Analytics provide details of their research on better terpene analysis methods in Cannabis
plant materials. The research focused on " the more traditional headspace syringe (HS syringe) and liquid injection syringe (LI syringe) approaches" compared to two more modern approaches to terpene analysis: HS-solid-phase microextraction Arrow (HS-SPME Arrow) and DI-SPME Arrow. After describing their experiments and results, the authors made multiple conclusions. First, they found "that DI-SPME Arrow performed better than HS-SPME Arrow; however, both of these approaches outperformed HS syringe for the extraction and analyses of terpenes." However, their results that LI syringe proved best, though not without some considerations concerning instrument uptime, compared to other methods. They also make a number of recommendations to cannabis laboratories and scientific researchers based upon their findings in order to "improve the science of cannabis testing."
In this 2021 paper published in BMC Bioinformatics
, Xie et al.
examine big omics and gene set analysis, a common analytical technique for bioinformaticists. The authors argue that biomedical science researchers have not been using the best GSA methods among those that are actually available. They chose to conduct research of popularity and performance based on prior described validation strategies and existing benchmark studies. Noting that popular methods are not always the best methods, the authors conclude with five points from their research, including a need for deeper discussions and research about GSA methods and more rigorous validation procedures for GSA tools. They also provide examples of evaluational tools for GSA and other bioinformatics software.
Last week's article looked at inference attacks in the cloud, but they happen in other ways as well. In this 2021 paper, Chong reviews the available literature on inference and other privacy attacks that occur on published healthcare information originally sourced from electronic health records (EHRs) and other health informatics systems. Ethical sharing of this kind of data with researchers conducting statistical analysis, improving clinical decision making, etc. is important, but it must preserve the privacy of the underlying individuals. After briefly discussing data publishing with a strong privacy focus, Chong looks at how healthcare data is typically stored and what threats exist against such data. The author then examines two well-established privacy models, including their strengths and limitations, that can be used to limit those threats: data anonymization and differential privacy. Despite these models, Chong closes by noting that "preserving privacy in healthcare data still poses several unsolved privacy and utility challenges" and expounds on areas for future research into those challenges.
Inference attacks occur when a (typically) malicious actor is able to infer from public information more sensitive information about a database or individual without directly accessing the original database or sensitive user information. That these sorts of attacks can be successful against applications and data on cloud-based infrastructure should not be a surprise to seasoned IT veterans. Recognizing these concerns, Jebali et al.
examine the state of inference attacks in the cloud, the research that has been conducted so far on such attacks, and what can be done about them. Aside from examining a variety of security concerns in both communicating and non-communicating servers, the authors also address the inference problem, offering a potential solution that will in theory "optimize data distribution without the need to query the workload, then partition the database in the cloud by taking into consideration access control policies and data utility, before finally running a query evaluation model on a big data framework to securely process distributed queries while retaining access control." They conclude by recognizing more research must be done to test their proposed solution, and noting that past research has often overlooked other types of inference sources such as inclusion dependencies, join dependencies, and multivalued dependencies, which should be further examined.
In this 2020 paper published in Forensic Science International
, Casey and Souvignet present their ideas on how forensic laboratories with significant digital footprints should best prepare their operations to ensure their processes and digital data can be independently verified. Their recommendations come as they recognize how "digital transformations can undermine the core principles and processes of forensic laboratories" when those transformations are not planned and implemented well, risking the lab's integrity as well as the fundamental rights of individuals being forensically examined. After a brief introduction, the authors then present five risk scenarios and four technological improvement scenarios, followed by a discussion of a series of risk management practices to ensure digital transformations in the forensic laboratory are optimized and effective. They finally touch upon the value of in-house expertise and quality assurance practices in the lab, before concluding that "with proper forethought and preparation, forensic laboratories can employ technology and advanced data analytics to enhance existing services and create new services, while respecting fundamental human rights."