Ethics and Big Data in health

When we talk about Big Data, What do we really mean? Toward a more precise definition of Big Data

Despite the lack of consensus on an official definition of Big Data, research and studies have continued to progress based on this "no consensus" stance over the years. However, the lack of a clear definition and scope for Big Data results in scientific research and communication lacking a common ground. Even with the popular "V" characteristics, Big Data remains elusive. The term is broad and is used differently in research, often referring to entirely different concepts, which is rarely stated explicitly in papers. While many studies and reviews attempt to draw a comprehensive understanding of Big Data, there has been little systematic research on the position and practical implications of the term Big Data in research environments. To address this gap, this paper presents a Systematic Literature Review (SLR) on secondary studies to provide a comprehensive overview of how Big Data is used and understood across different scientific domains. Our objective was to monitor the application of the Big Data concept in science, identify which technologies are prevalent in which fields, and investigate the discrepancies between the theoretical understanding and practical usage of the term. Our study found that various Big Data technologies are being used in different scientific fields, including machine learning algorithms, distributed computing frameworks, and other tools. These manifestations of Big Data can be classified into four major categories: abstract concepts, large datasets, machine learning techniques, and the Big Data ecosystem. This study revealed that despite the general agreement on the "V" characteristics, researchers in different scientific fields have varied implicit understandings of Big Data. These implicit understandings significantly influence the content and discussions of studies involving Big Data, although they are often not explicitly stated. We call for a clearer articulation of the meaning of Big Data in research to facilitate smoother scientific communication.

Emerging Strategies in Drug Development and Clinical Care in the Era of Personalized and Precision Medicine

In the ever-changing landscape of modern medicine, we face an important moment where the interplay of disease, drugs, and patients defines a new paradigm [...].

Protective alleles and precision healthcare in crewed spaceflight

Common and rare alleles are now being annotated across millions of human genomes, and omics technologies are increasingly being used to develop health and treatment recommendations. However, these alleles have not yet been systematically characterized relative to aerospace medicine. Here, we review published alleles naturally found in human cohorts that have a likely protective effect, which is linked to decreased cancer risk and improved bone, muscular, and cardiovascular health. Although some technical and ethical challenges remain, research into these protective mechanisms could translate into improved nutrition, exercise, and health recommendations for crew members during deep space missions.

Multi-Level Ethical Considerations of Artificial Intelligence Health Monitoring for People Living with Parkinson's Disease

Artificial intelligence (AI) has garnered tremendous attention in health care, and many hope that AI can enhance our health system's ability to care for people with chronic and degenerative conditions, including Parkinson's Disease (PD). This paper reports the themes and lessons derived from a qualitative study with people living with PD, family caregivers, and health care providers regarding the ethical dimensions of using AI to monitor, assess, and predict PD symptoms and progression. Thematic analysis identified ethical concerns at four intersecting levels: personal, interpersonal, professional/institutional, and societal levels. Reflecting on potential benefits of predictive algorithms that can continuously collect and process longitudinal data, participants expressed a desire for more timely, ongoing, and accurate information that could enhance management of day-to-day fluctuations and facilitate clinical and personal care as their disease progresses. Nonetheless, they voiced concerns about intersecting ethical questions around evolving illness identities, familial and professional care relationships, privacy, and data ownership/governance. The multi-layer analysis provides a helpful way to understand the ethics of using AI in monitoring and managing PD and other chronic/degenerative conditions.

The Roadmap toward Personalized Medicine: Challenges and Opportunities

In 2019, the International Consortium for Personalised Medicine (ICPerMed) developed a vision on how the use of personalized medicine (PM) approaches will promote "next-generation" medicine in 2030 more firmly centered on the individual's personal characteristics, leading to improved health outcomes within sustainable healthcare systems through research, development, innovation, and implementation for the benefit of patients, citizens, and society. Nevertheless, there are significant hurdles that healthcare professionals, researchers, policy makers, and patients must overcome to implement PM. The ICPerMed aims to provide recommendations to increase stakeholders' awareness on actionable measures to be implemented for the realization of PM. Starting with best practice examples of PM together with consultation of experts and stakeholders, a careful analysis that underlined hurdles, opportunities, recommendations, and information, aiming at developing knowledge on the requirements for PM implementation in healthcare practices, has been provided. A pragmatic roadmap has been defined for PM integration into healthcare systems, suggesting actions to overcome existing barriers and harness the potential of PM for improved health outcomes. In fact, to facilitate the adoption of PM by diverse stakeholders, it is mandatory to have a comprehensive set of resources tailored to stakeholder needs in critical areas of PM. These include engagement strategies, collaboration frameworks, infrastructure development, education and training programs, ethical considerations, resource allocation guidelines, regulatory compliance, and data management and privacy.

Navigating Informed Consent Requirements and Expectations in Cluster Randomized Trials: Research Ethics Board Members' and Researchers' Views

Informed consent is a cornerstone of ethical human research. However, as cluster randomized trials (CRTs) are increasingly popular to evaluate health service interventions, especially as health systems aspire toward the learning health system, questions abound how research teams and research ethics boards (REBs) should navigate intertwining consent and data-use considerations. Methodological and ethical questions include who constitute the participants, whose and what types of consent are necessary, and how data from people who have not consented to participation should be managed to optimize the balance of trust in the research enterprise, respect for persons, the promotion of data integrity, and the pursuit of the public good in the research arena. In this paper, we report the findings and lessons learned from a qualitative study examining how researchers and REB members consider the ethical dimensions of when data can be collected and used in CRTs in the evolving research landscape.

Developing Policy for the Healthy Life Trajectories Initiative: Going from National to International

Background: Scientific research is becoming an increasingly collaborative and global venture. The Healthy Life Trajectories Initiative (HeLTI), for instance, is an international Developmental Origins of Health and Disease research collaboration developed to address the increasing burden of noncommunicable diseases around the world. It comprises four separate but harmonized cohort trials in Canada, China, India, and South Africa. These cohorts will generate rich data and biosample sets that can be shared both within the HeLTI Consortium and with other researchers from around the world. Methods: To ensure the coordination and operation of these types of collaborative research initiatives, a standardized and harmonized governance model is required to regulate the processes and interactions between all involved actors. To develop the governance models, frameworks and related policies from other longitudinal cohort studies and biobanks were used, as were guidance documents on biobank and database governance and relevant literature on data and biobank governance. Results: This article outlines the key components of the governance model for the HeLTI Consortium, including management of the cohorts' respective databases and biobanks, access to data and biosamples, and considerations related to intellectual property and publications. Conclusion: Governance within international collaborative research ventures is critical to ensure the operations and benefits of these types of research apparatuses. Although this article focuses on the HeLTI Consortium as a model, it may nonetheless serve as a model for both current and future collaborative consortium-based research initiatives. Clinical Trial Registration Numbers: Canada, ISRCTN13308752; China, ChiCTR1800017773; India, ISRCTN20161479; South Africa, PACTR201903750173871.

Ethics challenges in sharing data from pragmatic clinical trials

Numerous arguments have been advanced for broadly sharing de-identified, participant-level clinical trials data, and trial sponsors and journals are increasingly requiring it. However, data sharing in pragmatic clinical trials presents ethical challenges related to the use of waivers or alterations of informed consent for some pragmatic clinical trials and corresponding limitations of informed consent to guide sharing decisions; the potential for data sharing in pragmatic clinical trials to present risks not only for individual patient-subjects, but also for health systems and the clinicians within them; sharing of data from electronic health records instead of data newly collected for research purposes; and researchers' limited capacity to control sensitive data within an electronic health record and potential implications of such limits for meeting obligations inherent to Certificates of Confidentiality. These challenges raise questions about the extent to which traditional research ethics governance structures are capable of guiding decisions about pragmatic clinical trial data sharing. This article identifies and examines these ethical challenges for pragmatic clinical trial data sharing. We suggest several areas for future empirical scholarship, including the need to identify patient and public attitudes regarding pragmatic clinical trial data sharing as well as to assess the demand for pragmatic clinical trial data and the correspondingly likely benefit of such sharing. Further conceptual work is also needed to explore how requirements to respect patient-subjects about whom data are shared in the context of pragmatic clinical trials should be understood, particularly in the absence of informed consent for initial research activities, and the appropriate balance between promoting the generation of socially valuable knowledge and respecting autonomy.

Opportunities for a population-based cohort in Belgium

Population-based cohorts allow providing answers to a wide range of policy-relevant research questions. In Belgium, existing cohort-like initiatives are limited by their focus on specific population groups or specific topics, or they lack a true longitudinal design. Since 2016, consultations and deliberative processes have been set up to explore the opportunities for a population-based cohort in Belgium. Through these processes, several recommendations emerged to pave the way forward – i.e., to facilitate the establishment of administrative linkages, increase digitalisation, secure long-term financial and organisational efforts, establish a consortium of the willing, and identify and tackle ethical and legal bottlenecks. This comment summarizes these recommendations, as these opportunities should be explored in depth to consolidate the existing collaborations between different stakeholders, and refers to current initiatives that can further facilitate the establishment of a Belgian population-based cohort and, more generally, administrative and health data linkage and reuse for research and policy-making.

Sociotechnical safeguards for genomic data privacy

Recent developments in a variety of sectors, including health care, research and the direct-to-consumer industry, have led to a dramatic increase in the amount of genomic data that are collected, used and shared. This state of affairs raises new and challenging concerns for personal privacy, both legally and technically. This Review appraises existing and emerging threats to genomic data privacy and discusses how well current legal frameworks and technical safeguards mitigate these concerns. It concludes with a discussion of remaining and emerging challenges and illustrates possible solutions that can balance protecting privacy and realizing the benefits that result from the sharing of genetic information.

Interaction Empowerment in Mobile Health: Concepts, Challenges, and Perspectives

In its most trending interpretation, empowerment in health care is implemented as a patient-centered approach. In the same sense, many mobile health (mHealth) apps are being developed with a primary focus on the individual user. The integration of mHealth apps into the health care system has the potential to counteract existing challenges, including incomplete or nonstandardized medical data and lack of communication, especially in the intersectional context (eg, patients, medical forces). However, concerns about data security and privacy, regional differences in regulations, lack of accessibility, and nontransparent apps hinder the successful integration of mHealth into the health care system. One approach to address this is to rethink the interpretation of empowerment. On that basis, we here examine existing approaches of individual empowerment and subsequently analyze a different view of empowerment in digital health, namely interaction empowerment. Such a change of perspective could positively influence intersectoral communication and facilitate secure data and knowledge sharing. We discuss this novel viewpoint on empowerment, focusing on more efficient integration and development of mHealth approaches. A renewed interpretation of empowerment could thus buffer current limitations of individual empowerment while also advancing digitization of the health system.

Challenges and Ethical Issues in Data Privacy

This paper strived to find out the challenges and ethical issues in the stream of data privacy in the present era of the twenty-first century. The present study focuses on empirical studies that were published during the period 2008-2020. During the research, efforts have been made to identify the challenges and ethical issues that the researchers have to face during the research process. In the study, it has been noticed that such kind of matters gained popularity after the year 2011. In the research, it has been found that there are many challenges to data privacy. It becomes challenging to follow ethics to maintain data confidentiality and security in this technology-driven era. In the end, based on available data, it has been concluded that there is an excellent need for stiff punishments for that person who misuse the data for their own sake and demoralize those people who make hard efforts to carry out the research and generate new concepts and ideas. So the government should take necessary measures toward data privacy areas for security and safety purposes.

International data governance for neuroscience

As neuroscience projects increase in scale and cross international borders, different ethical principles, national and international laws, regulations, and policies for data sharing must be considered. These concerns are part of what is collectively called data governance. Whereas neuroscience data transcend borders, data governance is typically constrained within geopolitical boundaries. An international data governance framework and accompanying infrastructure can assist investigators, institutions, data repositories, and funders with navigating disparate policies. Here, we propose principles and operational considerations for how data governance in neuroscience can be navigated at an international scale and highlight gaps, challenges, and opportunities in a global brain data ecosystem. We consider how to approach data governance in a way that balances data protection requirements and the need for open science, so as to promote international collaboration through federated constructs such as the International Brain Initiative (IBI).

AUR-RRA Review: Logistics of Academic-Industry Partnerships in Artificial Intelligence

The Radiology Research Alliance (RRA) of the Association of University Radiologists (AUR) convenes Task Forces to address current topics in radiology. In this article, the AUR-RRA Task Force on Academic-Industry Partnerships for Artificial Intelligence, considered issues of importance to academic radiology departments contemplating industry partnerships in artificial intelligence (AI) development, testing and evaluation. Our goal was to create a framework encompassing the domains of clinical, technical, regulatory, legal and financial considerations that impact the arrangement and success of such partnerships.

Power of big data to improve patient care in gastroenterology

Big data is defined as being large, varied or frequently updated, and usually generated from real-world interaction. With the unprecedented availability of big data, comes an obligation to maximise its potential for healthcare improvements in treatment effectiveness, disease prevention and healthcare delivery. We review the opportunities and challenges that big data brings to gastroenterology. We review its sources for healthcare improvement in gastroenterology, including electronic medical records, patient registries and patient-generated data. Big data can complement traditional research methods in hypothesis generation, supporting studies and disseminating findings; and in some cases holds distinct advantages where traditional trials are unfeasible. There is great potential power in patient-level linkage of datasets to help quantify inequalities, identify best practice and improve patient outcomes. We exemplify this with the UK colorectal cancer repository and the potential of linkage using the National Endoscopy Database, the inflammatory bowel disease registry and the National Health Service bowel cancer screening programme. Artificial intelligence and machine learning are increasingly being used to improve diagnostics in gastroenterology, with image analysis entering clinical practice, and the potential of machine learning to improve outcome prediction and diagnostics in other clinical areas. Big data brings issues with large sample sizes, real-world biases, data curation, keeping clinical context at analysis and General Data Protection Regulation compliance. There is a tension between our obligation to use data for the common good and protecting individual patient's data. We emphasise the importance of engaging with our patients to enable them to understand their data usage as fully as they wish.

Data Ethics in Digital Health and Genomics

The digital revolution has disruptively reshaped the way health services are provided and how research is conducted. This transformation has produced novel ethical challenges. The digitalization of health records, bioinformatics, molecular medicine, wearable biomedical technologies, biotechnology, and synthetic biology has created new biological data niches. How these data are shared, stored, distributed, and analyzed has created ethical problems regarding privacy, trust, accountability, fairness, and justice. This study investigates issues related to data-sharing permissions, fairness in secondary data distribution, and commercial and political conflicts of interest among individuals, companies, and states. In conclusion, establishing an agency to act as deputy trustee on behalf of individuals is recommended to intermediate the complex nature of informed consent. Focusing on decentralized digital technologies is recommended in order to catalyze the utilization of data and prevent discrimination without circulating data unnecessarily.

Surveillance in Next-Generation Personalized Healthcare: Science and Ethics of Data Analytics in Healthcare

Advances in science and technology have allowed for incredible improvements in healthcare. Additionally, the digital revolution in healthcare provides new ways of collecting and storing large volumes of patient data, referred to as big healthcare data. As a result, healthcare providers are now able to use data to gain a deeper understanding of how to treat an individual in what is referred to as personalized healthcare. Regardless, there are several ethical challenges associated with big healthcare data that affect how personalized healthcare is delivered. To highlight these issues, this article will review the role of big data in personalized healthcare while also discussing the ethical challenges associated with it. The article will also discuss public health surveillance, its implications, and the challenges associated with collecting participants' information. The article will proceed by highlighting next generation technologies, including robotics and 3D printing. The article will conclude by providing recommendations on how patient privacy can be protected in next-generation personalized healthcare.

What Makes Artificial Intelligence Exceptional in Health Technology Assessment?

The application of artificial intelligence (AI) may revolutionize the healthcare system, leading to enhance efficiency by automatizing routine tasks and decreasing health-related costs, broadening access to healthcare delivery, targeting more precisely patient needs, and assisting clinicians in their decision-making. For these benefits to materialize, governments and health authorities must regulate AI, and conduct appropriate health technology assessment (HTA). Many authors have highlighted that AI health technologies (AIHT) challenge traditional evaluation and regulatory processes. To inform and support HTA organizations and regulators in adapting their processes to AIHTs, we conducted a systematic review of the literature on the challenges posed by AIHTs in HTA and health regulation. Our research question was: What makes artificial intelligence exceptional in HTA? The current body of literature appears to portray AIHTs as being exceptional to HTA. This exceptionalism is expressed along 5 dimensions: 1) AIHT's distinctive features; 2) their systemic impacts on health care and the health sector; 3) the increased expectations towards AI in health; 4) the new ethical, social and legal challenges that arise from deploying AI in the health sector; and 5) the new evaluative constraints that AI poses to HTA. Thus, AIHTs are perceived as exceptional because of their technological characteristics and potential impacts on society at large. As AI implementation by governments and health organizations carries risks of generating new, and amplifying existing, challenges, there are strong arguments for taking into consideration the exceptional aspects of AIHTs, especially as their impacts on the healthcare system will be far greater than that of drugs and medical devices. As AIHTs begin to be increasingly introduced into the health care sector, there is a window of opportunity for HTA agencies and scholars to consider AIHTs' exceptionalism and to work towards only deploying clinically, economically, socially acceptable AIHTs in the health care system.

Ethical funding for trustworthy AI: proposals to address the responsibilities of funders to ensure that projects adhere to trustworthy AI practice

AI systems that demonstrate significant bias or lower than claimed accuracy, and resulting in individual and societal harms, continue to be reported. Such reports beg the question as to why such systems continue to be funded, developed and deployed despite the many published ethical AI principles. This paper focusses on the funding processes for AI research grants which we have identified as a gap in the current range of ethical AI solutions such as AI procurement guidelines, AI impact assessments and AI audit frameworks. We highlight the responsibilities of funding bodies to ensure investment is channelled towards trustworthy and safe AI systems and provides case studies as to how other ethical funding principles are managed. We offer a first sight of two proposals for funding bodies to consider regarding procedures they can employ. The first proposal is for the inclusion of a Trustworthy AI Statement’ section in the grant application form and offers an example of the associated guidance. The second proposal outlines the wider management requirements of a funding body for the ethical review and monitoring of funded projects to ensure adherence to the proposed ethical strategies in the applicants Trustworthy AI Statement. The anticipated outcome for such proposals being employed would be to create a ‘stop and think’ section during the project planning and application procedure requiring applicants to implement the methods for the ethically aligned design of AI. In essence it asks funders to send the message “if you want the money, then build trustworthy AI!”.

An African Relational Approach to Healthcare and Big Data Challenges

Big Data has amplified some challenges in the healthcare context. One significant challenge is how to use healthcare big data (HBD) in ways that honor individual rights to informed consent or privacy. Careful analysis from diverse backgrounds will be vital in contributing ethical guidelines that can adequately address healthcare Big Data's growing complexities globally. Especially, the study argues that an under-explored African philosophy of Ubuntu can usefully influence big data practices in ways that address this challenge without undermining its benefits. Ubuntu emphasizes harmonious relationships. Harmonious relations entail identifying with one another and exhibiting solidarity to each other. One can identify or exhibit solidarity with others through psychological attitudes such as thinking of oneself as part of a "we" and acting in ways that will more likely improve the quality of life of others. The African relational philosophy of Ubuntu deserves to be given an audience not only for epistemic justice but also because the continued absence of African perspective in the discourse on ethical use of HBD science represents a missed opportunity to enrich ethical thinking about HBD from diverse backgrounds. Research is, however, required to provide greater specificity on how Ubuntu values may be integrated into HBD analytic techniques.

Systemic Fairness for Sharing Health Data: Perspectives From Swiss Stakeholders

Introduction: Health research is gradually embracing a more collectivist approach, fueled by a new movement of open science, data sharing and collaborative partnerships. However, the existence of systemic contradictions hinders the sharing of health data and such collectivist endeavor. Therefore, this qualitative study explores these systemic barriers to a fair sharing of health data from the perspectives of Swiss stakeholders.

Methods: Purposive and snowball sampling were used to recruit 48 experts active in the Swiss healthcare domain, from the research/policy-making field and those having a high position in a health data enterprise (e.g., health register, hospital IT data infrastructure or a national health data initiative). Semi-structured interviews were then conducted, audio-recorded, verbatim transcribed with identifying information removed to guarantee the anonymity of participants. A theoretical thematic analysis was then carried out to identify themes and subthemes related to the topic of systemic fairness for sharing health data.

Results: Two themes related to the topic of systemic fairness for sharing health data were identified, namely (i) the hypercompetitive environment and (ii) the legal uncertainty blocking data sharing. The theme, hypercompetitive environment was further divided into two subthemes, (i) systemic contradictions to fair data sharing and the (ii) need of fair systemic attribution mechanisms.

Discussion: From the perspectives of Swiss stakeholders, hypercompetition in the Swiss academic system is hindering the sharing of health data for secondary research purposes, with the downside effect of influencing researchers to embrace individualism for career opportunities, thereby opposing the data sharing movement. In addition, there was a perceived sense of legal uncertainty from legislations governing the sharing of health data, which adds unreasonable burdens on individual researchers, who are often unequipped to deal with such facets of their data sharing activities.

The evolution of personalized healthcare and the pivotal role of European regions in its implementation

Personalized medicine (PM) moves at the same pace of data and technology and calls for important changes in healthcare. New players are participating, providing impulse to PM. We review the conceptual foundations for PM and personalized healthcare and their evolution through scientific publications where a clear definition and the features of the different formulations are identifiable. We then examined PM policy documents of the International Consortium for Personalised Medicine and related initiatives to understand how PM stakeholders have been changing. Regional authorities and stakeholders have joined the race to deliver personalized care and are driving toward what could be termed as the next personalized healthcare. Their role as a key stakeholder in PM is expected to be pivotal.

Attitudes towards Risk Prediction in a Help Seeking Population of Early Detection Centers for Mental Disorders-A Qualitative Approach

Big Data approaches raise hope for a paradigm shift towards illness prevention, while others are concerned about discrimination resulting from these approaches. This will become particularly important for people with mental disorders, as research on medical risk profiles and early detection progresses rapidly. This study aimed to explore views and attitudes towards risk prediction in people who, for the first time, sought help at one of three early detection centers for mental disorders in Germany (Cologne, Munich, Dresden). A total of 269 help-seekers answered an open-ended question on the potential use of risk prediction. Attitudes towards risk prediction and motives for its approval or rejection were categorized inductively and analyzed using qualitative content analysis. The anticipated impact on self-determination was a driving decision component, regardless of whether a person would decide for or against risk prediction. Results revealed diverse, sometimes contrasting, motives for both approval and rejection (e.g., the desire to control of one’s life as a reason for and against risk prediction). Knowledge about a higher risk as a potential psychological burden was one of the major reasons against risk prediction. The decision to make use of risk prediction is expected to have far-reaching effects on the quality of life and self-perception of potential users. Healthcare providers should empower those seeking help by carefully considering individual expectations and perceptions of risk prediction.

Ethical challenges of precision cancer medicine

Amongst common diseases, cancer is often both a leader in self-regulatory policy, or the field for contentious ethical issues such as the patenting of the BRCA1/2 genes. With the advent of genomic sequencing technologies, achieving precision cancer medicine requires prospective norms due to the large and varied sources of data involved. Here, we discuss the ethical and legal aspects of the policy debate around the relevant topics in precision cancer medicine: the return of incidental findings and sequencing raw data to patients, the communication of genetic results to patients' relatives, privacy and communication risks with concomitant oversight strategies, patient participation and consent models. We present the arguments and empirical data supporting specific policy solutions delineating still contested areas. What type of consent and oversight are required to acquire genomic data or to access it where desired, either by the participant/patient or third-party researchers? Most of the raw sequencing data is still uninterpretable and the variants revealed subject to reinterpretation over time. No doubt the ethical challenges of precision cancer medicine are a prototype of what's to come for other diseases. They are also paradigmatic for regulatory and ethical questions of the translational endeavors since the two worlds - basic science and patient care - are governed by different ethical and legal principles that need to be reconciled in precision cancer medicine.

From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices

The debate about the ethical implications of Artificial Intelligence dates from the 1960s (Samuel in Science, 132(3429):741-742, 1960. https://doi.org/10.1126/science.132.3429.741 ; Wiener in Cybernetics: or control and communication in the animal and the machine, MIT Press, New York, 1961). However, in recent years symbolic AI has been complemented and sometimes replaced by (Deep) Neural Networks and Machine Learning (ML) techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles-the 'what' of AI ethics (beneficence, non-maleficence, autonomy, justice and explicability)-rather than on practices, the 'how.' Awareness of the potential issues is increasing at a fast rate, but the AI community's ability to take action to mitigate the associated risks is still at its infancy. Our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers apply ethics at each stage of the Machine Learning development pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs.

The development of a scoring and ranking strategy for a patient-tailored adverse drug reaction prediction in polypharmacy

Only few applications are currently dealing with personalized adverse drug reactions (ADRs) prediction in case of polypharmacy. The study aimed to develop a patient-tailored ADR web application, considering characteristics from 734 drugs and relevant patient related factors. The application was designed in Python using a scoring and ranking system based on frequency and severity, computed for each ADR and expressed through an online platform. A neural networks algorithm was used for predicting the severity of ADRs. The application inputs are: age, gender, drugs, relevant pathologies. The outputs are: an overall severity profile (hospitalization and mortality risk), a stratified risk on specific ADR groups and a sorted list of the most important ADRs depending on frequency and severity. The Severity prediction model validation resulted in 79.7–85.1% Area Under the Receiver Operating Characteristic Curve Score, which lies in the good cut-off of 75–90%. The program offers a complex view regarding the ADR profile of a given patient and could be used by the physician and clinical pharmacist during patient safety monitoring, for a coherent therapy choice or medication adjustment, due to the good therapy coverage and the inclusion of relevant patient comorbidities.

When information is the treatment? Precision medicine in healthcare

Profoundly more data-intensive than conventional medicine, precision medicine's distinctive informational needs present new challenges for healthcare management. Data protection and privacy law are key determinants in precision medicine's future. This article examines legal and regulatory barriers to the incorporation of precision medicine into healthcare. Specific attention is paid to analyzing recent health privacy laws, court cases, and medical device regulations. Considering the challenges identified, recommendations and guidance are crafted for health leaders with reference to domestic and international initiatives.

Sharing Is Caring-Data Sharing Initiatives in Healthcare

In recent years, more and more health data are being generated. These data come not only from professional health systems, but also from wearable devices. All these 'big data' put together can be utilized to optimize treatments for each unique patient ('precision medicine'). For this to be possible, it is necessary that hospitals, academia and industry work together to bridge the 'valley of death' of translational medicine. However, hospitals and academia often are reluctant to share their data with other parties, even though the patient is actually the owner of his/her own health data. Academic hospitals usually invest a lot of time in setting up clinical trials and collecting data, and want to be the first ones to publish papers on this data. There are some publicly available datasets, but these are usually only shared after study (and publication) completion, which means a severe delay of months or even years before others can analyse the data. One solution is to incentivize the hospitals to share their data with (other) academic institutes and the industry. Here, we show an analysis of the current literature around data sharing, and we discuss five aspects of data sharing in the medical domain: publisher requirements, data ownership, growing support for data sharing, data sharing initiatives and how the use of federated data might be a solution. We also discuss some potential future developments around data sharing, such as medical crowdsourcing and data generalists.

Biobanking for Genomic and Personalized Health Research: Participant Perceptions and Preferences

Introduction: Biospecimens and associated data are invaluable tools in Genomics and Personalized Health (GAPH) research and can aid in the discovery of disease etiology and the development of therapeutics. Objective: To examine the experiences of patients invited to a particular GAPH study, Spectrometry in TIA Rapid Assessment (SpecTRA), and to explore broader biospecimen and data sharing preferences among a larger group of patients who had opted into a Permission to Contact for research program. Methods: An electronic survey was e-mailed to 515 participants. The survey was completed by 38% of participants, an unspecified number of whom were also SpecTRA participants. Results: Of those respondents who recalled participating in SpecTRA, 96% strongly agreed, agreed, or were neutral when asked if they received enough information to make an informed decision. Seventy-two percent agreed and 20% were neutral when asked if their study questions were addressed. Ninety-six percent of all respondents felt that SpecTRA's aim to develop a proteomic test for stroke was a worthwhile investment for health care, 98% said they were willing to provide a sample and/or information to facilitate the project's goals, and 96% to health research in general. Fifty-three percent of all participants suggested they would be comfortable sharing health information collected during SpecTRA with for-profit organizations, 87% with nonprofit organizations, and 38% said it matters to them where in the world their sample/information would be sent. Conclusions: Our results suggest that while there is room for improvement in providing adequate information to enable participants' understanding of the purpose of GAPH studies such as SpecTRA, patients are supportive of GAPH in general. Results also suggest that willingness to participate would likely be impacted by factors such as the study's commercial and national affiliations. This study indicates that further work is required to guide improvements on how the GAPH research community describes studies to potential participants, and to enable participation options that incorporate variable participant preferences.

Humanizing Big Data: Recognizing the Human Aspect of Big Data

The term “big data” refers broadly to large volumes of data, often gathered from several sources, that are then analyzed, for example, for predictive analytics. Combining and mining genetic data from varied sources including clinical genetic testing, for example, electronic health records, what might be termed as “recreational” genetic testing such as ancestry testing, as well as research studies, provide one type of “big data.” Challenges and cautions in analyzing big data include recognizing the lack of systematic collection of the source data, the variety of assay technologies used, the potential variation in classification and interpretation of genetic variants. While advanced technologies such as microarrays and, more recently, next-generation sequencing, that enable testing an individual's DNA for thousands of genes and variants simultaneously are briefly discussed, attention is focused more closely on challenges to analysis of the massive data generated by these genomic technologies. The main theme of this review is to evaluate challenges associated with big data in general and specifically to bring the sophisticated technology of genetic/genomic testing down to the individual level, keeping in mind the human aspect of the data source and considering where the impact of the data will be translated and applied. Considerations in this “humanizing” process include providing adequate counseling and consent for genetic testing in all settings, as well as understanding the strengths and limitations of assays and their interpretation.

Ethics of Using and Sharing Clinical Imaging Data for Artificial Intelligence: A Proposed Framework

In this article, the authors propose an ethical framework for using and sharing clinical data for the development of artificial intelligence (AI) applications. The philosophical premise is as follows: when clinical data are used to provide care, the primary purpose for acquiring the data is fulfilled. At that point, clinical data should be treated as a form of public good, to be used for the benefit of future patients. In their 2013 article, Faden et al argued that all who participate in the health care system, including patients, have a moral obligation to contribute to improving that system. The authors extend that framework to questions surrounding the secondary use of clinical data for AI applications. Specifically, the authors propose that all individuals and entities with access to clinical data become data stewards, with fiduciary (or trust) responsibilities to patients to carefully safeguard patient privacy, and to the public to ensure that the data are made widely available for the development of knowledge and tools to benefit future patients. According to this framework, the authors maintain that it is unethical for providers to "sell" clinical data to other parties by granting access to clinical data, especially under exclusive arrangements, in exchange for monetary or in-kind payments that exceed costs. The authors also propose that patient consent is not required before the data are used for secondary purposes when obtaining such consent is prohibitively costly or burdensome, as long as mechanisms are in place to ensure that ethical standards are strictly followed. Rather than debate whether patients or provider organizations "own" the data, the authors propose that clinical data are not owned at all in the traditional sense, but rather that all who interact with or control the data have an obligation to ensure that the data are used for the benefit of future patients and society.

Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer

A major challenge in radiomics is assembling data from multiple centers. Sharing data between hospitals is restricted by legal and ethical regulations. Distributed learning is a technique, enabling training models on multicenter data without data leaving the hospitals ("privacy-preserving" distributed learning). This study tested feasibility of distributed learning of radiomics data for prediction of two year overall survival and HPV status in head and neck cancer (HNC) patients. Pretreatment CT images were collected from 1174 HNC patients in 6 different cohorts. 981 radiomic features were extracted using Z-Rad software implementation. Hierarchical clustering was performed to preselect features. Classification was done using logistic regression. In the validation dataset, the receiver operating characteristics (ROC) were compared between the models trained in the centralized and distributed manner. No difference in ROC was observed with respect to feature selection. The logistic regression coefficients were identical between the methods (absolute difference <10-7). In comparison of the full workflow (feature selection and classification), no significant difference in ROC was found between centralized and distributed models for both studied endpoints (DeLong p > 0.05). In conclusion, both feature selection and classification are feasible in a distributed manner using radiomics data, which opens new possibility for training more reliable radiomics models.

Precision health: a primer for physiotherapists

Health care provision is changing, and so is the information we use to guide decisions related to patient care. Increasingly, health practitioners will need to deal with genetics and 'big data' in the context of clinical practice. Indeed, commercial packages for consumer genetic testing are already widely available, and devices enabling self-monitoring of health are in daily use by many of our patients. "Precision health" (distinct from "precision medicine") provides a model, which allows us to bring our genome together with our external environment (lifestyles, societal influences etc.) and eventually, our transient internal environment (reflected by 'omics'), to optimise disease prevention and care. Such advancements have given rise to a need for primary health care clinicians to understand basic genetic and precision health concepts. This editorial meets this need, serving as a primer by providing the following: an introduction to current primary health challenges; description of the key elements of the precision health model; an overview of basic genetic, and associated research concepts; a snapshot of some clinically pertinent research in the context of precision health; and a brief discussion of challenges and future directions.

Data Work: Meaning-Making in the Era of Data-Rich Medicine

In the era of data-rich medicine, an increasing number of domains of people’s lives are datafied and rendered usable for health care purposes. Yet, deriving insights for clinical practice and individual life choices and deciding what data or information should be used for this purpose pose difficult challenges that require tremendous time, resources, and skill. Thus, big data not only promises new clinical insights but also generates new—and heretofore largely unarticulated—forms of work for patients, families, and health care providers alike. Building on science studies, medical informatics, Anselm Strauss and colleagues’ concept of patient work, and subsequent elaborations of articulation work, in this article, we analyze the forms of work engendered by the need to make data and information actionable for the treatment decisions and lives of individual patients. We outline three areas of data work, which we characterize as the work of supporting digital data practices, the work of interpretation and contextualization, and the work of inclusion and interaction. This is a first step toward naming and making visible these forms of work in order that they can be adequately seen, rewarded, and assessed in the future. We argue that making data work visible is also necessary to ensure that the insights of big and diverse datasets can be applied in meaningful and equitable ways for better health care.

Technological improvement in modern health care applications using Internet of Things (IoT) and proposal of novel health care approach

Purpose

With the development of emerging engineering technology and industrialization, there are greater changes in the life style of people in smart urban cities; therefore, there is also more chance of various health problems in urban areas. The life style of persons in metro urban areas with the expansive volume of population is similarly influenced by different application and administration frameworks. These are affecting the human health system up to an extended extent and there are more health-related issues and health hazard concerns that can be identified in urban areas. The purpose of this paper is to present an analytical study on various aspects of the smart health care system in a smart perspective by analyzing them with respect to emerging engineering technologies such as mobile network, cloud computing, Internet of Things (IoT), big data analytics and ubiquitous computing. This paper also carries out a detailed survey of health issues and improved solutions in automated systems using these technologies. Second, the paper also presents a novel health care system using smart and safe ambulances and their appropriate control at traffic points with safety and security features in a smart city, so that the valuable life of patients can be saved in time by immediate treatment in nearest hospital or health care units.

Design/methodology/approach

In this paper, an analytical survey was conducted for improvement in the health care sector using computer technology and IoT-based various modern health care applications. An idea of Smart Health Care Hospital using sensors, mobile agent smart vehicle configuration and safety traffic control for ambulance was proposed.

Findings

A simulation was carried out to see the performance of a safety mechanism in the proposed approach. Comparative analysis was carried out with other approaches to know the execution time, response time and probable delay due to the implementation of this approach.

Originality/value

It is an original research work with motivation inspired from current emergent technology to apply in the health care system.

Assessment of the Data Sharing and Privacy Practices of Smartphone Apps for Depression and Smoking Cessation

Importance

Inadequate privacy disclosures have repeatedly been identified by cross-sectional surveys of health applications (apps), including apps for mental health and behavior change. However, few studies have assessed directly the correspondence between privacy disclosures and how apps handle personal data. Understanding the scope of this discrepancy is particularly important in mental health, given enhanced privacy concerns relating to stigma and negative impacts of inadvertent disclosure. Because most health apps fall outside government regulation, up-to-date technical scrutiny is essential for informed decision making by consumers and health care professionals wishing to prescribe health apps.

Objective

To provide a contemporary assessment of the privacy practices of popular apps for depression and smoking cessation by critically evaluating privacy policy content and, specifically, comparing disclosures regarding third-party data transmission to actual behavior.

Design and Setting

Cross-sectional assessment of 36 top-ranked (by app store search result ordering in January 2018) apps for depression and smoking cessation for Android and iOS in the United States and Australia. Privacy policy content was evaluated with prespecified criteria. Technical assessment of encrypted and unencrypted data transmission was performed. Analysis took place between April and June 2018.

Main Outcomes and Measures

Correspondence between policies and transmission behavior observed by intercepting sent data.

Results

Twenty-five of 36 apps (69%) incorporated a privacy policy. Twenty-two of 25 apps with a policy (88%) provided information about primary uses of collected data, while only 16 (64%) described secondary uses. While 23 of 25 apps with a privacy policy (92%) stated in a policy that data would be transmitted to a third party, transmission was detected in 33 of all 36 apps (92%). Twenty-nine of 36 apps (81%) transmitted data for advertising and marketing purposes or analytics to just 2 commercial entities, Google and Facebook, but only 12 of 28 (43%) transmitting data to Google and 6 of 12 (50%) transmitting data to Facebook disclosed this.

Conclusions and Relevance

Data sharing with third parties that includes linkable identifiers is prevalent and focused on services provided by Google and Facebook. Despite this, most apps offer users no way to anticipate that data will be shared in this way. As a result, users are denied an informed choice about whether such sharing is acceptable to them. Privacy assessments that rely solely on disclosures made in policies, or are not regularly updated, are unlikely to uncover these evolving issues. This may limit their ability to offer effective guidance to consumers and health care professionals.

A Tale of Two Capacities: Including Children and Decisionally Vulnerable Adults in Biomedical Research

The participation of individuals who lack decision-making capacity is essential for advancing genomics research and neuroscience, but raises ethical and legal challenges relating to vulnerability, consent, and exclusion. Capacity differences between populations and individuals, the dynamics of capacity over time, and evolving legal consent and capacity regimes all raise uncertainty for researchers, institutional review boards, and policy makers. We review international ethical and legal best practices for including children and decisionally vulnerable adults in health research. Research ethics norms and literature tend to split such groups into narrow silos, which results in inconsistency and conceptual confusion, or to lump them together, which fails to take into account morally relevant differences. Through a narrative review of international norms, we identify challenges common to both groups, while drawing out distinctions reflecting their opposite capacity trajectories. Our comparison between these two populations clarifies underlying ethical concepts and offers opportunities for critique. Children need protection to foster their long-term autonomy, while decisionally vulnerable adults need to be provided with support in order to exercise their autonomy. This leads to differences in how researchers determine who lacks capacity, who has authority to consent, and what criteria guide such decision-making. We also consider how capacity issues color contemporary research governance debates over broad consent, data protection compliance, data sharing, and the return of individual research results and incidental findings.

Responsible data sharing in international health research: a systematic review of principles and norms

BACKGROUND

Large-scale linkage of international clinical datasets could lead to unique insights into disease aetiology and facilitate treatment evaluation and drug development. Hereto, multi-stakeholder consortia are currently designing several disease-specific translational research platforms to enable international health data sharing. Despite the recent adoption of the EU General Data Protection Regulation (GDPR), the procedures for how to govern responsible data sharing in such projects are not at all spelled out yet. In search of a first, basic outline of an ethical governance framework, we set out to explore relevant ethical principles and norms.

METHODS

We performed a systematic review of literature and ethical guidelines for principles and norms pertaining to data sharing for international health research.

RESULTS

We observed an abundance of principles and norms with considerable convergence at the aggregate level of four overarching themes: societal benefits and value; distribution of risks, benefits and burdens; respect for individuals and groups; and public trust and engagement. However, at the level of principles and norms we identified substantial variation in the phrasing and level of detail, the number and content of norms considered necessary to protect a principle, and the contextual approaches in which principles and norms are used.

CONCLUSIONS

While providing some helpful leads for further work on a coherent governance framework for data sharing, the current collection of principles and norms prompts important questions about how to streamline terminology regarding de-identification and how to harmonise the identified principles and norms into a coherent governance framework that promotes data sharing while securing public trust.

From Big Data to Precision Medicine

For over a decade the term "Big data" has been used to describe the rapid increase in volume, variety and velocity of information available, not just in medical research but in almost every aspect of our lives. As scientists, we now have the capacity to rapidly generate, store and analyse data that, only a few years ago, would have taken many years to compile. However, "Big data" no longer means what it once did. The term has expanded and now refers not to just large data volume, but to our increasing ability to analyse and interpret those data. Tautologies such as "data analytics" and "data science" have emerged to describe approaches to the volume of available information as it grows ever larger. New methods dedicated to improving data collection, storage, cleaning, processing and interpretation continue to be developed, although not always by, or for, medical researchers. Exploiting new tools to extract meaning from large volume information has the potential to drive real change in clinical practice, from personalized therapy and intelligent drug design to population screening and electronic health record mining. As ever, where new technology promises "Big Advances," significant challenges remain. Here we discuss both the opportunities and challenges posed to biomedical research by our increasing ability to tackle large datasets. Important challenges include the need for standardization of data content, format, and clinical definitions, a heightened need for collaborative networks with sharing of both data and expertise and, perhaps most importantly, a need to reconsider how and when analytic methodology is taught to medical researchers. We also set "Big data" analytics in context: recent advances may appear to promise a revolution, sweeping away conventional approaches to medical science. However, their real promise lies in their synergy with, not replacement of, classical hypothesis-driven methods. The generation of novel, data-driven hypotheses based on interpretable models will always require stringent validation and experimental testing. Thus, hypothesis-generating research founded on large datasets adds to, rather than replaces, traditional hypothesis driven science. Each can benefit from the other and it is through using both that we can improve clinical practice.

Prospects and challenges for clinical decision support in the era of big data

Recently, there has been burgeoning interest in developing more effective and robust clinical decision support systems (CDSSs) for oncology. This has been primarily driven by the demands for more personalized and precise medical practice in oncology in the era of so-called Big Data (BD); an era that promises to harness the power of large-scale data flow to revolutionize cancer treatment. This interest in BD analytics has created new opportunities as well as new unmet challenges. These include: routine aggregation and standardization of clinical data; patient privacy; transformation of current analytical approaches to handle such noisy and heterogeneous data; and expanded use of advanced statistical learning methods based on confluence of modern statistical methods and machine learning algorithms. In this review, we present the current status of CDSSs in oncology, the prospects and current challenges of BD analytics, and the promising role of integrated modern statistics and machine learning algorithms in predicting complex clinical endpoints, individualizing treatment rules, and optimizing dynamic personalized treatment regimens. We discuss issues pertaining to these topics and present application examples from an aggregate of experiences. We also discuss the role of human factors in improving the utilization and acceptance of such enhanced CDSSs and how to mitigate possible sources of human error to achieve optimal performance and wider acceptance.

Personal health data: A systematic mapping study

BACKGROUND

Personal health data (PHD) research has been intensified over the last years, attracting the attention of scientists from different fields, such as software engineers, computer scientists and medical professionals. The increasing interest of researchers can be attributed to the exponential growth of the available PHD due to the widespread adoption of ubiquitous technology in everyday life, as well as to the potential of the ongoing digital transformation in healthcare. This increasing interest requires that academia has an overview of the published scientific literature to plan future endeavors.

OBJECTIVE

The main objective of this study is to identify and address research gaps in literature regarding PHD.

METHOD

This paper conducts a systematic mapping study to summarize the existing PHD approaches in literature and to organize the selected studies according to six classification criteria: publication source, publication year, research types, empirical types, contribution types and research topic.

RESULTS

In total 79 papers have been included after fulfilling the inclusion criteria and have been classified accordingly. There is an increasing amount of attention that has been paid to PHD since 2014. The majority of papers is published in journals. The two main research types found were solution proposals and evaluation research. The majority of the selected papers were empirically evaluated. The main contribution types were methods and frameworks. Data privacy is the most frequently addressed topic in PHD literature, followed by data sharing.

CONCLUSIONS

The findings of this systematic mapping study have implications for both researchers who are planning new studies in PHD and for practitioners who are working in connected health and would like to have an overview on the existent studies on PHD research area.

The Genomic Commons

Over its 30 or so years of existence, the genomic commons-the worldwide collection of publicly accessible repositories of human and nonhuman genomic data-has enjoyed remarkable, perhaps unprecedented, success. Thanks to the rapid public data release policies initiated by the Human Genome Project, free access to a vast array of scientific data is now the norm, not only in genomics, but in scientific disciplines of all descriptions. And far from being a monolithic creation of bureaucratic fiat, the genomic commons is an exemplar of polycentric, multistakeholder governance. But like all dynamic and rapidly evolving systems, the genomic commons is not without its challenges. Issues involving scientific priority, intellectual property, individual privacy, and informed consent, in an environment of data sets of exponentially expanding size and complexity, must be addressed in the near term. In this review, we describe the characteristics and unique history of the genomic commons, then address some of the trends, challenges, and opportunities that we envision for this valuable public resource in the years to come.