A scoping review of distributed ledger technology in genomics: thematic analysis and directions for future research

Objective Rising interests in distributed ledger technology (DLT) and genomics have sparked various interdisciplinary research streams with a proliferating number of scattered publications investigating the application of DLT in genomics. This review aims to uncover the current state of research on DLT in genomics, in terms of focal research themes and directions for future research. Materials and Methods We conducted a scoping review and thematic analysis. To identify the 60 relevant papers, we queried Scopus, Web of Science, PubMed, ACM Digital Library, IEEE Xplore, arXiv, and BiorXiv. Results Our analysis resulted in 7 focal themes on DLT in genomics discussed in literature, namely: (1) Data economy and sharing; (2) Data management; (3) Data protection; (4) Data storage; (5) Decentralized data analysis; (6) Proof of useful work; and (7) Ethical, legal, and social implications. Discussion Based on the identified themes, we present 7 future research directions: (1) Investigate opportunities for the application of DLT concepts other than Blockchain; (2) Explore people’s attitudes and behaviors regarding the commodification of genetic data through DLT-based genetic data markets; (3) Examine opportunities for joint consent management via DLT; (4) Investigate and evaluate data storage models appropriate for DLT; (5) Research the regulation-compliant use of DLT in healthcare information systems; (6) Investigate alternative consensus mechanisms based on Proof of Useful Work; and (7) Explore DLT-enabled approaches for the protection of genetic data ensuring user privacy. Conclusion While research on DLT in genomics is currently growing, there are many unresolved problems. This literature review outlines extant research and provides future directions for researchers and practitioners

A scoping review of distributed ledger technology in genomics: thematic analysis and directions for future research

OBJECTIVE: Rising interests in distributed ledger technology (DLT) and genomics have sparked various interdisciplinary research streams with a proliferating number of scattered publications investigating the application of DLT in genomics. This review aims to uncover the current state of research on DLT in genomics, in terms of focal research themes and directions for future research. MATERIALS AND METHODS: We conducted a scoping review and thematic analysis. To identify the 60 relevant papers, we queried Scopus, Web of Science, PubMed, ACM Digital Library, IEEE Xplore, arXiv, and BiorXiv. RESULTS: Our analysis resulted in 7 focal themes on DLT in genomics discussed in literature, namely: (1) Data economy and sharing; (2) Data management; (3) Data protection; (4) Data storage; (5) Decentralized data analysis; (6) Proof of useful work; and (7) Ethical, legal, and social implications. DISCUSSION: Based on the identified themes, we present 7 future research directions: (1) Investigate opportunities for the application of DLT concepts other than Blockchain; (2) Explore people’s attitudes and behaviors regarding the commodification of genetic data through DLT-based genetic data markets; (3) Examine opportunities for joint consent management via DLT; (4) Investigate and evaluate data storage models appropriate for DLT; (5) Research the regulation-compliant use of DLT in healthcare information systems; (6) Investigate alternative consensus mechanisms based on Proof of Useful Work; and (7) Explore DLT-enabled approaches for the protection of genetic data ensuring user privacy. CONCLUSION: While research on DLT in genomics is currently growing, there are many unresolved problems. This literature review outlines extant research and provides future directions for researchers and practitioners

Blockchain Technology in Business Organizations: A Scoping Review

The scientific literature on blockchain technology is emerging but increasing rapidly. This review paper aims to provide a deeper understanding of the nature and scope of the extant literature on blockchain technology in the particular context of business organizations. To achieve our main objective, we searched five databases and screened 320 papers for inclusion. As a result of the search and screen process, we identified 39 relevant articles. Data coding was first pilot tested and then performed independently by two teams of researchers. All disagreements were reconciled by a third coder. Our findings reveal that most of the extant literature focuses on how blockchain technology works and, to a lesser extent, on the what , i.e. its potential applications and usages in business organizations. For its part, the why question, which focuses on the organizational motivations for adopting blockchain technology, was scarcely discussed in prior literature. In short, our findings reveal that many issues and questions remain to be investigated. Based on a gap analysis, we propose a few promising avenues that shall guide future research efforts in this important topic

How are hospitals using artificial intelligence in strategic decision making? —a scoping review

Artificial intelligence (AI) is a useful tool for clinical decision-making in hospitals, and for strategic decision-making in other industries. This scoping review provides a comprehensive review of the potential for AI to improve strategic decision-making in hospitals by exploring current applications of AI in this area. Peer-reviewed publications and conference presentations associated with AI for strategic decision-making were identified in Health Administration, Computer Science and Business and Management databases to answer the research question; how are hospitals using AI in strategic decision-making? The review found 19 published AI applications for hospital strategic decision-making. The applications used a variety of knowledge-based, probabilistic reasoning and data-driven AI, that generally followed the course of AI maturity. They focused on specific decisions, with none providing a comprehensive framework for strategic decision-making drawing on existing enterprise- or system-wide data. There was little evidence of evaluation of the AI applications, with no cost-benefit evaluation. The scoping review suggests the need for substantial improvement in the understanding of AI and its application among hospital decision-makers leading to greater organisational maturity. This would suggest that journals and researchers require evaluative and economic research and that training to improve understanding of AI be provided for board members, managers and clinicians

Exploring the potential of artificial intelligence and machine learning to combat COVID-19 and existing opportunities for LMIC: A scoping review

Background: In the face of the current time-sensitive COVID-19 pandemic, the limited capacity of healthcare systems resulted in an emerging need to develop newer methods to control the spread of the pandemic. Artificial Intelligence (AI), and Machine Learning (ML) have a vast potential to exponentially optimize health care research. The use of AI-driven tools in LMIC can help in eradicating health inequalities and decrease the burden on health systems.Methods: The literature search for this Scoping review was conducted through the PubMed database using keywords: COVID-19, Artificial Intelligence (AI), Machine Learning (ML), and Low Middle-Income Countries (LMIC). Forty-three articles were identified and screened for eligibility and 13 were included in the final review. All the items of this Scoping review are reported using guidelines for PRISMA extension for scoping reviews (PRISMA-ScR).Results: Results were synthesized and reported under 4 themes. (a) The need of AI during this pandemic: AI can assist to increase the speed and accuracy of identification of cases and through data mining to deal with the health crisis efficiently, (b) Utility of AI in COVID-19 screening, contact tracing, and diagnosis: Efficacy for virus detection can a be increased by deploying the smart city data network using terminal tracking system along-with prediction of future outbreaks, (c) Use of AI in COVID-19 patient monitoring and drug development: A Deep learning system provides valuable information regarding protein structures associated with COVID-19 which could be utilized for vaccine formulation, and (d) AI beyond COVID-19 and opportunities for Low-Middle Income Countries (LMIC): There is a lack of financial, material, and human resources in LMIC, AI can minimize the workload on human labor and help in analyzing vast medical data, potentiating predictive and preventive healthcare.Conclusion: AI-based tools can be a game-changer for diagnosis, treatment, and management of COVID-19 patients with the potential to reshape the future of healthcare in LMIC

Enhancing pharmaceutical packaging through a technology ecosystem to facilitate the reuse of medicines and reduce medicinal waste

The idea of reusing dispensed medicines is appealing to the general public provided its benefits are illustrated, its risks minimized, and the logistics resolved. For example, medicine reuse could help reduce medicinal waste, protect the environment and improve public health. However, the associated technologies and legislation facilitating medicine reuse are generally not available. The availability of suitable technologies could arguably help shape stakeholders’ beliefs and in turn, uptake of a future medicine reuse scheme by tackling the risks and facilitating the practicalities. A literature survey is undertaken to lay down the groundwork for implementing technologies on and around pharmaceutical packaging in order to meet stakeholders’ previously expressed misgivings about medicine reuse (’stakeholder requirements’), and propose a novel ecosystem for, in effect, reusing returned medicines. Methods: A structured literature search examining the application of existing technologies on pharmaceutical packaging to enable medicine reuse was conducted and presented as a narrative review. Results: Reviewed technologies are classified according to different stakeholders’ requirements, and a novel ecosystem from a technology perspective is suggested as a solution to reusing medicines. Conclusion: Active sensing technologies applying to pharmaceutical packaging using printed electronics enlist medicines to be part of the Internet of Things network. Validating the quality and safety of returned medicines through this network seems to be the most effective way for reusing medicines and the correct application of technologies may be the key enabler

GDPR Compliance for Blockchain Applications in Healthcare

The transparent and decentralized characteristics associated with blockchain can be both appealing and problematic when applied to a healthcare use-case. As health data is highly sensitive, it is also highly regulated to ensure the privacy of patients. At the same time, access to health data and interoperability is in high demand. Regulatory frameworks such as GDPR and HIPAA are, amongst other objectives, meant to contribute to mitigating the risk of privacy violations in health data. Blockchain features can likely improve interoperability and access control to health data, and at the same time, preserve or even increase, the privacy of patients. Blockchain applications should address compliance with the current regulatory framework to increase real-world feasibility. This exploratory work indicates that published proof-of-concepts in the health domain comply with GDRP, to an extent. Blockchain developers need to make design choices to be compliant with GDPR since currently, none available blockchain platform can show compliance out of the box