Search results

A policy of surveillance which interferes with the fundamental right to a private life requires credible justification and a supportive evidence base. The authority for such interference should be clearly detailed in law, overseen by a transparent process and not left to the vagaries of administrative discretion. If a state surveils those it governs and claims the interference to be in the public interest, then the evidence base on which that claim stands and the operative conception of public interest should be subject to critical examination. Unfortunately, there is an inconsistency in the regulatory burden associated with access to confidential patient information for non-health-related surveillance purposes and access for health-related surveillance or research purposes. This inconsistency represents a systemic weakness to inform or challenge an evidence-based policy of non-health-related surveillance. This inconsistency is unjustified and undermines the qualities recognised to be necessary to maintain a trustworthy confidential public health service. Taking the withdrawn Memorandum of Understanding (MoU) between NHS Digital and the Home Office as a worked example, this chapter demonstrates how the capacity of the law to constrain the arbitrary or unwarranted exercise of power through judicial review is not sufficient to level the playing field. The authors recommend ‘levelling up’ in procedural oversight, and adopting independent mechanisms equivalent to those adopted for establishing the operative conceptions of public interest in the context of health research to non-health-related surveillance purposes.

Researchers recommend a reorganization of the medical profession into larger groups with a multispecialty mix. We analyze whether there is evidence for the superiority of these models and if this organizational transformation is underway.

We summarize the evidence on scale and scope economies in physician group practice, and then review the trends in physician group size and specialty mix to conduct survivorship tests of the most efficient models.

The distribution of physician groups exhibits two interesting tails. In the lower tail, a large percentage of physicians continue to practice in small, physician-owned practices. In the upper tail, there is a small but rapidly growing percentage of large groups that have been organized primarily by non-physician owners.

While our analysis includes no original data, it does collate all known surveys of physician practice characteristics and group practice formation to provide a consistent picture of physician organization.

Our review suggests that scale and scope economies in physician practice are limited. This may explain why most physicians have retained their small practices.

Larger, multispecialty groups have been primarily organized by non-physician owners in vertically integrated arrangements. There is little evidence supporting the efficiencies of such models and some concern they may pose anticompetitive threats.

This is the first comprehensive review of the scale and scope economies of physician practice in nearly two decades. The research results do not appear to have changed much; nor has much changed in physician practice organization.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

Background: Artificial intelligence (AI) is a booming sector that has profoundly influenced every walk of life, and the education sector is no exception. In education, AI has helped to develop novel teaching and learning solutions that are currently being tested in various contexts. Businesses and governments across the globe have been pouring money into a wide array of implementations, and dozens of EdTech start-ups are being funded to capitalise on this technological force. The penetration of AI in classroom teaching is also a profound matter of discussion. These have garnered massive amounts of student big data and have a significant impact on the life of both students and educators alike.

Purpose: The prime focus of this chapter is to extensively review and analyse the vast literature available on the utilities of AI in health care, learning, and development. The specific objective of thematic exploration of the literature is to explicate the principal facets and recent advances in the development and employment of AI in the latter. This chapter also aims to explore how the EdTech and healthcare–education sectors would witness a paradigm shift with the advent and incorporation of AI.

Design/Methodology/Approach: To provide context and evidence, relevant publications were identified on ScienceDirect, PubMed, and Google Scholar using keywords like AI, education, learning, health care, and development. In addition, the latest articles were also thoroughly reviewed to underscore recent advances in the same field.

Results: The implementation of AI in the learning, development, and healthcare sector is rising steeply, with a projected expansion of about 50% by 2022. These algorithms and user interfaces economically facilitate efficient delivery of the latter.

Conclusions: The EdTech and healthcare sector has great potential for a spectrum of AI-based interventions, providing access to learning opportunities and personalised experiences. These interventions are often economic in the long run compared to conventional modalities. However, several ethical and regulatory concerns should be addressed before the complete adoption of AI in these sectors.

Originality/Value: The value in exploring this topic is to present a view on the potential of employing AI in health care, medical education, and learning and development. It also intends to open a discussion of its potential benefits and a remedy to its shortcomings.

The Bureau of Economics in the Federal Trade Commission has a three-part role in the Agency and the strength of its functions changed over time depending on the preferences and ideology of the FTC’s leaders, developments in the field of economics, and the tenor of the times. The over-riding current role is to provide well considered, unbiased economic advice regarding antitrust and consumer protection law enforcement cases to the legal staff and the Commission. The second role, which long ago was primary, is to provide reports on investigations of various industries to the public and public officials. This role was more recently called research or “policy R&D”. A third role is to advocate for competition and markets both domestically and internationally. As a practical matter, the provision of economic advice to the FTC and to the legal staff has required that the economists wear “two hats,” helping the legal staff investigate cases and provide evidence to support law enforcement cases while also providing advice to the legal bureaus and to the Commission on which cases to pursue (thus providing “a second set of eyes” to evaluate cases). There is sometimes a tension in those functions because building a case is not the same as evaluating a case. Economists and the Bureau of Economics have provided such services to the FTC for over 100 years proving that a sub-organization can survive while playing roles that sometimes conflict. Such a life is not, however, always easy or fun.

This chapter explains how fundamental organisational change in the UK National Health Service (NHS) is being effected by new practices of digitised information gathering and use. It analyses the taken-for-granted IT infrastructures that lie behind digitisation and considers the relationship between digitisation and big data.

Qualitative research methods including discourse analysis, ethnography of software and key informant interviews were used. Actor-network theories, as developed by Science and technology Studies (STS) researchers were used to inform the research questions, data gathering and analysis. The chapter focuses on the aftermath of legislation to change the organisation of the NHS.

The chapter shows the benefits of qualitative research into specific manifestations information technology. It explains how apparently ‘objective’ and ‘neutral’ quantitative data gathering and analysis is mediated by complex software practices. It considers the political power of claims that data is neutral.

The chapter provides insight into a specific case of healthcare data and. It makes explicit the role of politics and the State in digitisation and shows how STS approaches can be used to understand political and technological practice.

The healthcare sector in India is witnessing phenomenal growth, such that by the year 2022, it will be a market worth trillions of INR. Increase in income levels, awareness regarding personal health, the occurrence of lifestyle diseases, better insurance policies, low-cost healthcare services, and the emergence of newer technologies like telemedicine are driving this sector to new heights. Abundant quantities of healthcare data are being accumulated each day, which is difficult to analyze using traditional statistical and analytical tools, calling for the application of Big Data Analytics in the healthcare sector. Through provision of evidence-based decision-making and actions across healthcare networks, Big Data Analytics equips the sector with the ability to analyze a wide variety of data. Big Data Analytics includes both predictive and descriptive analytics. At present, about half of the healthcare organizations have adopted an analytical approach to decision-making, while a quarter of these firms are experienced in its application. This implies the lack of understanding prevalent in healthcare sector toward the value and the managerial, economic, and strategic impact of Big Data Analytics. In this context, this chapter on “Predictive Analytics in Healthcare” discusses sources, areas of application, possible future areas, advantages and limitations of the application of predictive Big Data Analytics in healthcare.

The chapter deliberates on research ethics and the unanticipated side effects that technological developments have brought in the past decades. It looks at data protection and privacy through the prism of ethics and focuses on the need for safeguarding the fundamental rights of the research participants in the new digital era. Acknowledging the benefits of data analytics for boosting scientific process, the chapter reflects on the main principles and specific research derogations, introduced by the EU General Data Protection Regulation. Further on, it discusses some of the most pressing ethics concerns, related to the use, reuse, and misuse of data; the distinction between publicly available and open data; ethics challenges in online recruitment of research participants; and the potential bias and representativeness problems of Big Data research. The chapter underscores that all challenges should be properly addressed at the outset of research design. Highlighting the power asymmetries between Big Data studies and individuals’ rights to data protection, human dignity, and respect for private and family life, the chapter argues that anonymization may be reasonable, yet not the ultimate ethics solution. It asserts that while anonymization techniques may protect individual data protection rights, the former may not be sufficient to prevent discrimination and stigmatization of entire groups of populations. Finally, the chapter suggests some approaches for ensuring ethics compliance in the digital era.