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Purpose – This chapter reports on experts’ perspectives on health information technology (HIT) and how it may be used to improve health care quality and to lower health care costs.

Design/methodology/approach – Two roundtables were convened that focused on how to best use HIT to improve the quality of health care while ensuring it is accessible and affordable. Participants drew upon lessons learned in the Netherlands, the United States, and other countries.

Findings – The first roundtable focused on the use of (1) electronic health records (EHRs) by health care providers, (2) cloud computing for EHRs and health portals for consumers, and (3) data registries and networks for public health surveillance. The second roundtable highlighted (1) the rapid growth of personalized medicine, (2) the corresponding growth and sophistication of bioinformatics and analytics, (3) the increasing presence of mobile HIT, and (4) the disruptive changes in the institutional structures of biomedical research and development.

Practical implications – Governmental sponsorship of small pilot projects to solve practicable health system problems would encourage HIT innovation among key stakeholders. However, large-scale HIT solutions – developed through small pilot projects – should be pursued through public–private partnerships. At the same time, governments should speed up legislative and regulatory procedures to encourage adoption of cost-effective HIT innovations.

Social implications – Mobile HIT and social media are capable of fostering disease prevention and encouraging personal responsibility for improving or stabilizing chronic diseases.

Originality/value – Both health services researchers and policy makers should find this chapter of value since it highlights trends in HIT and addresses how health care quality may be improved while costs are contained.

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.

Evolutionary developmental biology is a highly variable scientific innovation because researchers can adapt their involvement in the innovation to the opportunities provided by their environment. On the basis of comparative case studies in four countries, we link epistemic properties of research tasks to three types of necessary protected space, and identify the necessary and facilitating conditions for building them. We found that the variability of research tasks made contributing to evolutionary developmental biology possible under most sets of authority relations. However, even the least demanding research depends on its acceptance as legitimate innovation by the scientific community and of purely basic research by state policy and research organisations. The latter condition is shown to become precarious.

The US Federal government is a potential source of support for advancing Library and Information Science (LIS) through funding experimentation, innovation, and demonstration. Most agencies are not as much interested in advancing the research front in LIS as they are in LIS contributions that advance other fields. The full potential of federal funding to impact LIS is far from realized. LIS researchers should be aware of each agency's mission as well as the types of research that each one supports. Many people contribute to research agendas but the most influential are researchers themselves. Becoming more successful in winning grants will require researchers to become better grant writers and to collaborate with people outside LIS.

Universities have a long history of training students to work in industry, and in recent years the number and percentage of students, especially those trained in science and engineering, who go to work in industry has grown. Today, three-eights of all PhDs with a degree in science and engineering (S&E) work in the private sector. These placements provide a major means for universities to participate in technology transfer. Students are not only up-to-date in terms of codified knowledge; they also possess tacit knowledge that can only be transferred by face-to-face interaction. They may also have participated as research assistants or as postdocs in the development of a technology that has been licensed by the firm where they are employed. Despite the important role that alumni play in technology transfer, universities rarely track the placements of graduate students in industry. Universities do not also systematically keep information on the contributions that alums make to innovations after graduating. Moreover, few programs socialize students to think of careers in the private sector as a top choice. Instead, many programs, especially in the biomedical sciences, socialize students to aspire to research careers in academe, with industry seen as a distinct second choice. Indeed, many PhDs only take jobs in industry after failing to find an academic position after serving as a postdoc for four or five years.

This paper examines recent placements of doctoral students in industry, using the verbatim records from the Survey of Earned Doctorates (SED) for 1997–2002. An advantage of this data is that we know the name of the firm (and the location of the firm) where the individual plans to work. This permits an exploration of several interesting dimensions regarding technology transfer not explored elsewhere, such as (1) sources (in terms of universities) educating students going to industry; (2) the R&D intensity of the firms where newly trained PhDs go to work and the industrial classification of the firms; (3) the role that proximity plays in facilitating these knowledge spillovers; and (4) the degree to which universities make placements with firms licensing their technologies.

The paper also examines the amount of information that universities provide regarding the placements of their PhDs. We find that although students are ready and willing to provide information regarding work plans after graduation, universities seldom provide information on placements. We conclude with a suggestion regarding the procedures universities could follow to create and make placement data available.

Tremendous measure of data lakes with the exponential mounting rate is produced by the present healthcare sector. The information from differing sources like electronic wellbeing record, clinical information, streaming information from sensors, biomedical image data, biomedical signal information, lab data, and so on brand it substantial as well as mind-boggling as far as changing information positions, which have stressed the abilities of prevailing regular database frameworks in terms of scalability, storage of unstructured data, concurrency, and cost. Big data solutions step in the picture by harnessing these colossal, assorted, and multipart data indexes to accomplish progressively important and learned patterns. The reconciliation of multimodal information seeking after removing the relationship among the unstructured information types is a hotly debated issue these days. Big data energizes in triumphing the bits of knowledge from these immense expanses of information. Big data is a term which is required to take care of the issues of volume, velocity, and variety generally seated in the medicinal services data. This work plans to exhibit a survey of the writing of big data arrangements in the medicinal services part, the potential changes, challenges, and accessible stages and philosophies to execute enormous information investigation in the healthcare sector. The work categories the big healthcare data (BHD) applications in five broad categories, followed by a prolific review of each sphere, and also offers some practical available real-life applications of BHD solutions.

Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and continues to revolutionize business and society. Split into four distinct sections, the book first lays out the theoretical foundations and fundamental concepts of E-Business before moving on to look at internet+ innovation models and their applications in different industries such as agriculture, finance and commerce. The book then provides a comprehensive analysis of E-business platforms and their applications in China before finishing with four comprehensive case studies of major E-business projects, providing readers with successful examples of implementing E-Business entrepreneurship projects.

Internet + and Electronic Business in China is a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society in China.