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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.

Joint working between health and social services has been a key focus of policy in recent years, albeit ‐ underneath the protocols and partnership agreements ‐ integrated processes have developed unevenly. A single shared assessment tool has been one of the concrete expressions of policy on the ground. This paper explores the implementation of the shared assessment process in Scotland. It discusses the broader policy agenda, before exploring the introduction of the shared assessment tool in a large urban authority. Based primarily on interviews with front‐line staff in health and social work and managers charged with delivering shared assessment, the paper suggests a lack of engagement on issues such as working cultures and equity of workloads, while some of the main reasons behind the implementation of shared assessment, such as overcoming duplication, have not generally materialised for staff. Overall, single shared assessment has been driven by process rather than by engagement with wider ideas about joint working, which has led to uneven and at times unwilling implementation.

The purpose of this paper is twofold: to explore how gatekeepers’ ways of regulating the researchers’ access to knowledge in/about care services reflect the systemic and interpersonal values that inform Danish welfare systems’ daily workings at the street level; and also explore how the authors’ methodological experiences mirror the value-informed regulatory strategies that professionals and users themselves experience in their daily encounters in the same local practices that the authors have studied.

The paper takes its empirical point of departure in a multisited ethnographic field study of the management of citizens with complex problems in Danish welfare systems.

By means of Michael Lipsky’s outline of access regulation, the authors will analyze the following regulatory strategies that are identified during the fieldwork: “Gatekeepers’ sympathy and creaming,” “Queuing and delay,” and ‘Withdrawal of consent and “no resources.” The paper suggests that trust, shared goals and sympathy seem to be key to the process of getting access.

Despite principles of neutrality, equal rights and access to services in welfare systems, the authors’ experiences thus tend to support other research within bureaucratic and care organizations, which has found that interpersonal relations, sympathy, dislikes, norms and values, etc., can heavily influence timely access to services, tailored information and support.

Distributed trust technologies, such as blockchain, propose to permit peer-to-peer transactions without trusted third parties. Yet not all implementations of such technologies fully decentralize. Information professionals make strategic choices about the level of decentralization when implementing such solutions, and many organizations are taking a hybrid (i.e. partially decentralized) approach to the implementation of distributed trust technologies. This paper conjectures that while hybrid approaches may resolve some challenges of decentralizing information governance, they also introduce others. To better understand these challenges, this paper aims first to elaborate a framework that conceptualizes a centralized–decentralized information governance continuum along three distinct dimensions: custody, ownership and right to access data. This paper then applies this framework to two illustrative blockchain case studies – a pilot Brazilian land transfer recording solution and a Canadian health data consent sharing project – to exemplify how the current transition state of blockchain pilots straddles both the old (centralized) and new (decentralized) worlds. Finally, this paper outlines the novel challenges that hybrid approaches introduce for information governance and what information professionals should do to navigate this thorny transition period. Counterintuitively, it may be much better for information professionals to embrace decentralization when implementing distributed trust technologies, as hybrid models could offer the worst of both the centralized and future decentralized worlds when consideration is given to the balance between information governance risks and new strategic business opportunities.

This paper illustrates how blockchain is transforming organizations and societies by highlighting new strategic information governance challenges using our original analytic framework in two detailed blockchain case studies – a pilot solution in Brazil to record land transfers (Flores et al., 2018) and another in Canada to handle health data sharing consent (Hofman et al., 2018). The two case studies represent research output of the first phase of an ongoing multidisciplinary research project focused on gaining an understanding of how blockchain technology generates organizational, societal and data transformations and challenges. The analytic framework was developed inductively from a thematic synthesis of the findings of the case studies conducted under the auspices of this research project. Each case discussed in detail in this paper was chosen from among the project's case studies, as it represents a desire to move away from the old centralized world of information governance to a new decentralized one. However, each case study also represents and embodies a transition state between the old and new worlds and highlights many of the associated strategic information governance challenges.

Decentralization continues to disrupt organizations and societies. New emerging distributed trust technologies such as blockchain break the old rules with respect to the trust and authority structures of organizations and how records and data are created, managed and used. While governments and businesses around the world clearly see value in this technology to drive business efficiency, open up new market opportunities and create new forms of value, these advantages will not come without challenges. For information executives then, the question is not if they will be disrupted, but how. Understanding the how as will be discussed in this paper provides the business know how to leverage the incredible innovation and transformation that decentralized trust technology enables before being leapfrogged by another organization. It requires a change of mindset to consider an organization as one part of a broader ecosystem, and for those who successfully do so, this paper views this as a strategic opportunity for those responsible for strategic information governance to design the future instead of being disrupted by it.

This paper presents a novel analytic framework for strategic information governance challenges as we transition from a traditional world of centralized records and information management to a new decentralized world. This paper analyzes these transitions and their implications for strategic information governance along three trajectories: custody, ownership and right to access records and data, illustrating with reference to our case studies.

This paper predicts a large number of organizations will miss the opportunities of the new decentralized trust world, resulting in a rather major churning of organizations, as those who successfully participate in building the new model will outcompete those stuck in the old world or the extremely problematic hybrid transition state. Counterintuitively, this paper argues that it may be much less complex for information executives to embrace decentralization as fast as they can, as in some ways the hybrid model seems to offer the worst of both the centralized and future decentralized worlds with respect to information governance risks.

This paper anticipates broader societal consequences of the predicted organization churn, in particular with respect to uncertainty about the evidence that records provide for public accountability and contractual rights and entitlements.

Decentralized trust technologies, such as blockchain, permit peer-to-peer transactions without trusted third parties. Of course, such radical shifts do not happen overnight. The current transition state of blockchain pilots straddles both the old and new worlds. This paper presents a theoretical framework categorizing strategic information governance challenges on a spectrum of centralized to decentralized in three primary areas: custody, ownership and right to access records and data. To illustrate how decentralized trust is transforming organizations and societies, this paper presents these strategic information governance challenges in two blockchain case studies – a pilot Brazilian land transfer recording solution and a Canadian health data consent sharing project. Drawing on the theoretical framework and case studies, this paper outlines what information executives should do to navigate this thorny transition period.

This article highlights the importance of information‐sharing in mental health care and in particular the difficulties that can follow when practitioners do not perceive lay carers as ‘partners in care’. The findings of a survey, in which views were sought on poor information‐sharing as a barrier to engagement in mental health services, are reported. The authors conclude with a number of recommendations on improving the way information is shared.

One of the currently debated changes in scientific practice is the implementation of data sharing requirements for peer-reviewed publication to increase transparency and intersubjective verifiability of results. However, it seems that data sharing is a not fully adopted behavior among researchers. The theory of planned behavior was repeatedly applied to explain drivers of data sharing from the perspective of data donors (researchers). However, data sharing can be viewed from another perspective as well: survey participants. The research questions (RQs) for this study were as follows: 1 Does data sharing increase participant's nonresponse? 2 Does data sharing influence participant's response behavior? The purpose of this paper is to address these issues.

To answer the RQs, a mixed methods approach was applied, consisting of a qualitative prestudy and a quantitative survey including an experimental component. The latter was a two-group setup with an intervention group (A) and a control group (B). A list-based recruiting of members of the Medical Faculty of the University of Freiburg was applied for 15 days. For exploratory data analysis of dropouts and nonresponse, we used Fisher's exact tests and binary logistic regressions.

In sum, we recorded 197 cases for Group A and 198 cases for Group B. We found no systematic group differences regarding response bias or dropout. Furthermore, we gained insights into the experiences our sample made with data sharing: half of our sample already requested data of other researchers or shared data on request of other researchers. Data repositories, however, were used less frequently: 28% of our respondents used data from repositories and 19% stored data in a repository.

To the authors’ knowledge, their study is the first study that includes researchers as survey subjects investigating the effect of data sharing on their response patterns.

In the Web 2.0 era, users massively communicate through social networking services (SNS), often under false expectations that their communications and personal data are private. This paper aims to analyze privacy requirements of personal communications over a public medium.

This paper systematically analyzes SNS services as communication models and considers privacy as an attribute of users’ communication. A privacy threat analysis for each communication model is performed, based on misuse scenarios, to elicit privacy requirements per communication type.

This paper identifies all communication attributes and privacy threats and provides a comprehensive list of privacy requirements concerning all stakeholders: platform providers, users and third parties.

Elicitation of privacy requirements focuses on the protection of both the communication’s message and metadata and takes into account the public–private character of the medium (SNS platform). The paper proposes a model of SNS functionality as communication patterns, along with a method to analyze privacy threats. Moreover, a comprehensive set of privacy requirements for SNS designers, third parties and users involved in SNS is identified, including voluntary sharing of personal data, the role of the SNS platforms and the various types of communications instantiating in SNS.

The purpose of this study was to investigate the difference between South Africa (SA) and the United Kingdom (UK) in terms of data protection compliance with the aim to establish if a country that has had data protection in place for a longer period of time has a higher level of compliance with data protection requirements in comparison with a country that is preparing for compliance.

An insurance industry multi-case study within the online insurance services environment was conducted. Personal information of four newly created consumer profiles was deposited to 10 random insurance organisation websites in each country to evaluate a number of data privacy requirements of the Data Protection Act and Protection of Personal Information Act.

The results demonstrate that not all the insurance organisations honored the selected opt-out preference for receiving direct marketing material. This was evident in direct marketing material that was sent from the insurance organisations in the sample to both the SA and UK consumer profiles who opted out for it. A total of 42 unsolicited third-party contacts were received by the SA consumer profiles, whereas the UK consumer profiles did not receive any third-party direct marketing. It was also found that the minimality principle is not always met by both SA and UK organisations.

As a jurisdiction with a heavy stance towards privacy implementation and regulation, it was found that the UK is more compliant than SA in terms of implementation of the evaluated data protection requirements included in the scope of this study, however not fully compliant.

Based upon the results obtained from this research, it suggests that the SA insurance organisations should ensure that the non-compliance aspects relating to direct marketing and sharing data with third parties are addressed. SA insurance companies should learn from the manner in which the UK insurance organisations implement these privacy requirements. Furthermore, the UK insurance organisations should focus on improved compliance for direct marking and the minimality principle. The study indicates the positive role that data protection legislation plays in a county like the UK, with a more mature stance toward compliance with data protection legislation.

This paper briefly describes a small research study, and presents its findings on the factors that have had an impact on the implementation of single shared assessment (SSA) in co‐located primary care and social work teams. Findings highlight continuing difficulties of policy implementation and the need for ongoing support, guidance and training.

This paper aims to assess the increasing challenges to governing the personal health information (PHI) essential for advancing artificial intelligence (AI) machine learning innovations in health care. Risks to privacy and justice/equity are discussed, along with potential solutions.

This conceptual paper highlights the scale and scope of PHI data consumed by deep learning algorithms and their opacity as novel challenges to health data governance.

This paper argues that these characteristics of machine learning will overwhelm existing data governance approaches such as privacy regulation and informed consent. Enhanced governance techniques and tools will be required to help preserve the autonomy and rights of individuals to control their PHI. Debate among all stakeholders and informed critique of how, and for whom, PHI-fueled health AI are developed and deployed are needed to channel these innovations in societally beneficial directions.

Health data may be used to address pressing societal concerns, such as operational and system-level improvement, and innovations such as personalized medicine. This paper informs work seeking to harness these resources for societal good amidst many competing value claims and substantial risks for privacy and security.

This is the first paper focusing on health data governance in relation to AI/machine learning.