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Clinical genomics is a complex, innovative medical speciality requiring clinical and organizational engagement to fulfil the clinical reward promised to date. Focus thus far has been on gene discovery and clinicians’ perspectives. The purpose of this study was to use implementation science theory to identify organizational barriers and enablers to implementation of clinical genomics along an organizations’ implementation journey from Preadoption through to Adoption and Implementation.

We used a deductive qualitative approach study design drawing on implementation science theory - (1) Translation Science to Population Impact Framework, to inform semi structured interviews with organizational decision-makers collaborating with Australian and Melbourne Genomics, alongside and (2) Theoretical Domains Framework (TDF), to guide data analysis.

We identified evolving organizational barriers across the implementation journey from Preadoption to Implementation. Initially the organizational focus is on understanding the value of clinical genomics (TDF code: belief about consequences) and setting the scene (TDF code: goals) before organizational (TDF codes: knowledge and belief about consequences) and clinician (TDF codes: belief about capability and intentions) willingness to adopt is apparent. Once at the stage of Implementation, leadership and clarity in organizational priorities (TDF codes: intentions, professional identity and emotion) that include clinical genomics are essential prerequisites to implementing clinical genomics in practice. Intuitive enablers were identified (e.g. ‘providing multiple opportunities for people to come on board) and mapped hypothetically to barriers.

Attention to date has centred on the barriers facing clinicians when introducing clinical genomics into practice. This paper uses a combination of implementation science theories to begin to unravel the organizational perspectives of implementing this complex health intervention.

Gliomas are common intracranial tumors with the characteristic of diffuse and invasive growth. The prognosis is poor, and the recurrence rate and mortality are higher. With the development of big data technology, many methods such as natural language processing, computer vision and image processing have been deeply applied in the medical field. This can help clinicians to provide personalized and precise diagnosis and therapeutic schedule for patients with different type of gliomas to achieve the best therapeutic effect. The purpose of this paper is to summarize and extract useful information from published research results by conducting a secondary analysis of the literature.

The PubMed and China National Knowledge Infrastructure (CNKI) literature database were used to retrieve published Chinese and English research papers about human gliomas. Comprehensive analysis was applied to conduct this research. The factors affecting survival and prognosis were screened and analyzed respectively in this paper, and different methods for multidimensional data of patients were discussed.

This paper identified biomarkers and therapeutic modalities associated with prognosis for different grade of gliomas. This paper investigated the relationship among these clinical prognostic factors and different histopathologic tying and grade of gliomas by comprehensive analysis. This paper summarizes the research progress of biomarker in medical imaging and genomics of gliomas to improve prognosis and the current status of treatment in China.

Combined with multimodal data such as genomics data, medical image data and clinical information data, this paper comprehensively analyzed the prognostic factors of glioma and provided guidance and evidence for rational treatment planning and improvement of clinical treatment prognosis.

This study analyzes the rise of genome instability in the life sciences and traces the problematic of instability as it relates to the sociology of health. Genome instability is the study of how genomes change and become variable between generations and within organisms over the life span. Genome instability reflects a significant departure from the Platonic genome imagined during the Human Genome Project. The aim of this chapter is to explain and analyze research on copy number variation and somatic mosaicism to consider the implications of these sciences for sociologists interested in genomics.

This chapter draws on two multi-sited ethnographies of contemporary biomedical science and literature in the sociology of health, science, and biomedicine to document a shift in thinking about the genome from fixed and universal to highly variable and influenced by time and context.

Genomic instability has become a framework for addressing how genomes change and become variable between generations and within organisms over the life span. Instability is a useful framework for analyzing changes in the life sciences in the post-genomic era.

Genome instability requires life scientists to address how differences both within and between individuals articulate with shifting disease categories and classifications. For sociologists, these findings have implications for studies of identity, sociality, and clinical experience.

This is the first sociological analysis of genomic instability. It identifies practical and conceptual implications of genomic instability for life scientists and helps sociologists delineate new approaches to the study of genomics in the post-genomic era.

Big Data analysis is one of the key challenges to the provision of health care to emerge in the last few years. This challenge has been spearheaded by the huge interest in the “4Ps” of health care (predictive, preventive, personalized, and participatory). Big Data offers striking development opportunities in health care and life sciences. Healthcare research is already using Big Data to analyze the spatial distribution of diseases such as diabetes mellitus at detailed geographic levels. Big Data is also being used to assess location-specific risk factors based on data of health insurance claims. Other studies in systems medicine utilize bioinformatics approaches to human biology which necessitate Big Data statistical analysis and medical informatics tools. Big Data is also being used to develop electronic algorithms to forecast clinical events in real time, with the intent to improve patient outcomes and thus reduce costs.

Yet, this Big Data era also poses critically difficult ethical challenges, since it is breaking down the traditional divisions between what belongs to public and private domains in health care and health research. Big Data in health care raises complex ethical concerns due to use of huge datasets obtained from different sources for varying reasons. The clinical translation of this Big Data is thus resulting in key ethical and epistemological challenges for those who use these data to generate new knowledge and the clinicians who eventually apply it to improve patient care.

Underlying this challenge is the fact that patient consent often cannot be collected for the use of individuals’ personal data which then forms part of this Big Data. There is also the added dichotomy of healthcare providers which use such Big Data in attempts to reduce healthcare costs, and the negative impact this may have on the individual with respect to privacy issues and potential discrimination.

Big Data thus challenges societal norms of privacy and consent. Many questions are being raised on how these huge masses of data can be managed into valuable information and meaningful knowledge, while still maintaining ethical norms. Maintaining ethical integrity may lack behind in such a fast-changing sphere of knowledge. There is also an urgent need for international cooperation and standards when considering the ethical implications of the use of Big Data-intensive information.

This chapter will consider some of the main ethical aspects of this fast-developing field in the provision of health care, health research, and public health. It will use examples to concretize the discussion, such as the ethical aspects of the applications of Big Data obtained from clinical trials, and the use of Big Data obtained from the increasing popularity of health mobile apps and social media sites.

The purpose of this paper is to examine how health scientists’ attitudinal, social, and resource factors affect their data reuse behaviors.

A survey method was utilized to investigate to what extent attitudinal, social, and resource factors influence health scientists’ data reuse behaviors. The health scientists’ data reuse research model was validated by using partial least squares (PLS) based structural equation modeling technique with a total of 161 health scientists in the USA.

The analysis results showed that health scientists’ data reuse intentions are driven by attitude toward data reuse, community norm of data reuse, disciplinary research climate, and organizational support factors. This research also found that both perceived usefulness of data reuse and perceived concern involved in data reuse have significant influences on health scientists’ attitude toward data reuse.

This research evaluated its newly proposed research model based on the theory of planned behavior using a sample from the community of scientists’ scholar database. This research showed an overall picture of how attitudinal, social, and resource factors influence health scientists’ data reuse behaviors. This research is limited due to its sample size and low response rate, so this study is considered as an exploratory study rather than a confirmatory study.

This research suggested for health science research communities, academic institutions, and libraries that diverse strategies need to be utilized to promote health scientists’ data reuse behaviors.

This research is one of initial studies in scientific data reuse which provided a holistic map about health scientists’ data sharing behaviors. The findings of this study provide the groundwork for strategies to facilitate data reuse practice in health science areas.

The purpose of this paper is to explore the perceptions and experiences of early adopters of the technology.

Registered dietitians (RDs) (n=14) were recruited from the UK, Canada, South Africa, Australia, Mexico and Israel. Six qualitative interviews and two focus groups were conducted online using a conference calling platform. Data were recorded, transcribed and thematically analyzed.

Early adopters of nutrigenomics (NGx) were experienced, self-efficacious RDs who actively sought knowledge of NGx through communication with one another and the broader scientific community. They considered NGx an extension of current practice and believed RDs had the skills to deliver it. Perceived barriers to widening the application of NGx were linked to skepticism among the wider dietetics community. Proliferation of unregulated websites offering tests and diets was considered “pseudoscience” and detrimental to dietetics fully embracing NGx. Lack of a sustainable public health model for the delivery of NGx was also perceived to hinder progress. Results are discussed with reference to “diffusion of innovation theory.”

The views of RDs who practice NGx have not been previously studied. These data highlight requirements for future dietetic training provision and more inclusive service delivery models. Regulation of NGx services and formal recognition by professional bodies is needed to address the research/practice translation gap. Further research is required to inquire as to the views of the wider dietetics profession.

This paper aims to provide a review of the applications of robotic technology in laboratories.

Following a short introduction, this paper discusses a range of key applications for laboratory robots and describes a number of products. Some recent research is considered and brief conclusions are drawn.

This shows that robotic laboratory automation offers a number of key economic and operational benefits and is being used in many procedures, ranging from drug discovery and toxicity testing to forensics and environmental monitoring. An intriguing possibility is the creation of new scientific knowledge by combining robotic systems with artificial intelligence.

This paper illustrates the vital role played by robotic automation in laboratory procedures.

Genetic knowledge is advancing steadily while at the same time DNA sequencing prices are dropping fast, but the diffusion of genetic services (GS) has been slow. The purpose of this paper is to identify GS diffusion drivers in the precision medicine (PM) ecosystem.

After reviewing the literature on innovation diffusion, particularly on GS diffusion, the PM ecosystem actors are interviewed to obtain their perspective. Using content analysis, the interviewees’ visions were interplayed with the literature to achieve driver conceptualization, which posteriorly originated broad themes.

The results indicate that GS diffusion depends on satisfying aspects from three broad themes and respective drivers: technology (evidence strength and credibility, customization, knowledge, data and information, tech evolution speed and cost), human (ethics, privacy and security and user power) and business (prevention, holistic view of the individual, public policy and regulation, business model and management).

The main management implications refer to considering health care in a multidisciplinary way, investing in the propagation of genetic knowledge, standardizing medical records and interpreting data.

This study, to the best of authors’ knowledge, is the first attempt to understand GS diffusion from a broad perspective, taking into account the PM stakeholders’ view. The 13 drivers offer a comprehensive understanding of how GS could spread in health care and they can assist researchers and practitioners to discuss and set strategies based on an initial structured map.

This study aims to develop the first Theory of Technological Response and Progress in Chaos (TRPC) and examine the case of technological development during the COVID-19 pandemic. The research objectives of this study were to: identify the key technologies that act as a response mechanism during the chaos event, specifically in the case of COVID-19; examine how technologies evolve, develop and diffuse in an immediate crisis and a chaotic environment; theorise various types and periods of technological response and progress during the emergence of chaos and the stages that unfold; and develop policy-oriented recommendations and establish technological foundations to address subsequent chaos events.

This study used the grounded theory as a methodology with a mixed-method approach that included quantitative and qualitative methods. The authors used the quantitative method to assist with the qualitative step to build the TRPC theory. Accordingly, this study integrated machine learning and text mining approaches to the qualitative data analysis following the steps of the grounded theory approach.

As a result of the TRPC theory development process, the authors identified three types of technologies (survival, essential and enhancement technologies) and five types of periods (stable, initial, survival-dominant, essential-dominant and enhancement-dominant periods) that are specific to chaos-technology interactions. The policy implications of this study demonstrate that a required technological base and know-how must be established before a chaotic event emerges.

Concerning the limitations of this study, social media data has advantages over other data sources, such as the examination of dynamic areas and analyses of immediate responses to chaos. However, other researchers can examine publications and patent sources to augment the findings concerning scientific approaches and new inventions in relation to COVID-19 and other chaos-specific developments. The authors developed the TRPC theory by studying the COVID-19 pandemic, however, other researchers can utilise it to study other chaos-related conditions, such as chaotic events that are caused by natural disasters. Other scholars can investigate the technological response and progress pattern in other rapidly emerging chaotic events of an uncertain and complex nature to augment these findings.

Following the indications of the OECD (2021a) and considering the study conducted by the European Parliamentary Research Service (Kritikos, 2020), the authors identified the key technologies that are significant for chaos and COVID-19 response using machine learning and text intelligence approach. Accordingly, the authors mapped all technological developments using clustering approaches, and examined the technological progress within the immediate chaos period using social media data.

The key policy implication of this study concerns the need for policymakers to develop policies that will help to establish the required technological base and know-how before chaos emerges. As a result, a rapid response can be implemented to mitigate the chaos and transform it into a competitive advantage. The authors also revealed that this recommendation overlaps with the model of dynamic capabilities in the literature (Teece and Pisano, 2003). Furthermore, this study recommends that nations and organisations establish a technological base that specifically includes technologies that bear 3A characteristics. These are the most crucial technologies for the survival- and essential-dominant stages. Moreover, the results of this study demonstrate that chaos accelerates technological progress through the rapid adoption and diffusion of technologies into different fields. Hence, nations and organisations should regard this rapid progress as an opportunity and establish the prior knowledge base and technologies before chaos emerges.

The authors have contributed to the chaos studies and the relationship between chaos and technological development by establishing the first theoretical foundation using the grounded theory approach, hereafter referred to as the TRPC theory. As part of the TRPC theory, the authors present three periods of technological response in the following sequence: survival technology, essential technology and enhancement technology. Moreover, this study illustrates the evolving technological importance and priorities as the periods of technological progress proceed under rapidly developing chaos.