Recent advances in military-funded neurotechnology and novel opportunities for misusing neurodevices show that the problem of dual use is inherent to neuroscience. This paper discusses how the neuroscience community should respond to these dilemmas and delineates a neuroscience-specific biosecurity framework. This neurosecurity framework involves calibrated regulation, (neuro)ethical guidelines, and awareness-raising activities within the scientific community.
On the 1st of January 2012, Switzerland introduced the diagnosis-related group hospital tariff structure (SwissDRG). It was recognised that healthcare provided to the most vulnerable patient groups would be a challenge for the new SwissDRG. Coincident with the implementation of SwissDRG, we explored hospital experts' perceptions of which patient groups are vulnerable under the SwissDRG system, what has changed for this group, as well as solutions to ensure adequate access to health care for them. We interviewed 43 experts from 40 Swiss hospitals. Participating experts named several vulnerable patient groups who share some common characteristics. These hospital experts were concerned about the patient groups that are not financially profitable and questioned the practicability of the current regulation. At the same time, they highlighted the complexity associated with caring for this group under the new SwissDRG and reported measures at the macro, meso, and micro levels to protect vulnerable patient groups from negative effects. To curb negative outcomes for vulnerable patient groups after the introduction of the SwissDRG, the Swiss legislation has introduced various instruments including the acute and transitional care (ATC) measures. We conclude that ATC measures do not produce the expected effect the legislators had hoped for. More health data is needed to identify situations where vulnerable patient groups are more susceptible to inadequate health care access in Switzerland.
AbstractThe increasing implementation of programs supported by machine learning in medical contexts will affect psychiatry. It is crucial to accompany this development with careful ethical considerations informed by empirical research involving experts from the field, to identify existing problems, and to address them with fine-grained ethical reflection. We conducted semi-structured qualitative interviews with 15 experts from Germany and Switzerland with training in medicine and neuroscience on the assistive use of machine learning in psychiatry. We used reflexive thematic analysis to identify key ethical expectations and attitudes towards machine learning systems. Experts' ethical expectations towards machine learning in psychiatry partially challenge orthodoxies from the field. We relate these challenges to three themes, namely (1) ethical challenges of machine learning research, (2) the role of explainability in research and clinical application, and (3) the relation of patients, physicians, and machine learning system. Participants were divided regarding the value of explainability, as promoted by recent guidelines for ethical artificial intelligence, and highlighted that explainability may be used as an ethical fig leaf to cover shortfalls in data acquisition. Experts recommended increased attention to machine learning methodology, and the education of physicians as first steps towards a potential use of machine learning systems in psychiatry. Our findings stress the need for domain-specific ethical research, scrutinizing the use of machine learning in different medical specialties. Critical ethical research should further examine the value of explainability for an ethical development of machine learning systems and strive towards an appropriate framework to communicate ML-based medical predictions.
The employment of Big Data as an increasingly used research method has introduced novel challenges to ethical research practices and to ethics committees (ECs) globally. The aim of this study is to explore the experiences of scholars with ECs in the ethical evaluation of Big Data projects. Thirty-five interviews were performed with Swiss and American researchers involved in Big Data research in psychology and sociology. The interviews were analyzed using thematic coding. Our respondents reported lack of support from ECs, absence of appropriate expertise among members of the boards, and lack of harmonized evaluation criteria between committees. To implement ECs practices we argue for updating the expertise of board members and the institution of a consultancy model between researchers and ECs.
Background: Various types of computational technologies can be used to access, store and wirelessly share private and sensitive user-related information. The 'big data' provided by these technologies may enable researchers and clinicians to better identify behavioral patterns and to provide a more personalized approach to care. The information collected, however, can be misused or potentially abused, and therefore could be detrimental to the very people who provided their medical data with the hope of improving care. This article focuses on the use of emerging mobile technologies that allow the collection of data about patients experiencing schizophrenia spectrum and related disorders. Schizophrenia has been recognized by the Sustainable Development Goals of the United Nations for its burden on our health care system and society [1]. Our analysis provides an overview of the range of available mobile technologies for people with schizophrenia and related conditions along with the technology's reported capabilities and limitations, and efficacy of mHealth interventions based on the data from articles. Thus, the focus of this review is first and foremost to update the scope of existing technologies as previous systematic reviews such as the ones by Alvarez-Jimenez et al. and by Firth and Torous are outdated [2, 3]. Although we also examine the ethical issues arising from the use of these technologies, for an in-depth analysis of the ethical implications of mobile Health technologies (mHealth), we refer the readers to our follow-up article. In terms of the ubiquitous availability of technology on the internet, our article summarizes significant information for mental health specialists and apprises the reader about the existence of these technologies. Objectives: In terms of the ubiquitous availability of technology on the internet, our article summarizes significant information for mental health specialists and apprises the reader about the existence of these technologies.
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.
INTRODUCTION The digitalization of medicine has led to a considerable growth of heterogeneous health datasets, which could improve healthcare research if integrated into the clinical life cycle. This process requires, amongst other things, the harmonization of these datasets, which is a prerequisite to improve their quality, re-usability and interoperability. However, there is a wide range of factors that either hinder or favor the harmonized collection, sharing and linkage of health data. OBJECTIVE This systematic review aims to identify barriers and facilitators to health data harmonization-including data sharing and linkage-by a comparative analysis of studies from Denmark and Switzerland. METHODS Publications from PubMed, Web of Science, EMBASE and CINAHL involving cross-institutional or cross-border collection, sharing or linkage of health data from Denmark or Switzerland were searched to identify the reported barriers and facilitators to data harmonization. RESULTS Of the 345 projects included, 240 were single-country and 105 were multinational studies. Regarding national projects, a Swiss study reported on average more barriers and facilitators than a Danish study. Barriers and facilitators of a technical nature were most frequently reported. CONCLUSION This systematic review gathered evidence from Denmark and Switzerland on barriers and facilitators concerning data harmonization, sharing and linkage. Barriers and facilitators were strictly interrelated with the national context where projects were carried out. Structural changes, such as legislation implemented at the national level, were mirrored in the projects. This underlines the impact of national strategies in the field of health data. Our findings also suggest that more openness and clarity in the reporting of both barriers and facilitators to data harmonization constitute a key element to promote the successful management of new projects using health data and the implementation of proper policies in this field. Our study findings are thus meaningful beyond these two countries.
In line with the policy objectives of the United Nations Sustainable Development Goals, this commentary seeks to examine the extent to which provisions of international health research guidance promote capacity building and equitable partnerships in global health research. Our evaluation finds that governance of collaborative research partnerships, and in particular capacity building, in resource-constrained settings is limited but has improved with the implementation guidance of the International Ethical Guidelines for Health-related Research Involving Humans by The Council for International Organizations of Medical Sciences (CIOMS) (2016). However, more clarity is needed in national legislation, industry and ethics guidelines, and regulatory provisions to address the structural inequities and power imbalances inherent in international health research partnerships. Most notably, ethical partnership governance is not supported by the principal industry ethics guidelines - the International Conference on Harmonization Technical Requirements for Registration of Pharmaceutical for Human Use (ICH) Good Clinical Practice (ICH-GCP). Given the strategic value of ICH-GCP guidelines in defining the role and responsibility of global health research partners, we conclude that such governance should stipulate the minimal requirements for creating an equitable environment of inclusion, mutual learning, transparency and accountability. Procedurally, this can be supported by i) shared research agenda setting with local leadership, ii) capacity assessments, and iii) construction of a memorandum of understanding (MoU). Moreover, the requirement of capacity building needs to be coordinated amongst partners to support good collaborative practice and deliver on the public health goals of the research enterprise; improving local conditions of health and reducing global health inequality. In this respect, and in order to develop consistency between sources of research governance, ICH-GCP should reference CIOMS ethical guidelines as the established standard for collaborative partnership. Moreover, greater commitment and support should be given to co-ordinate, strengthen and enforce local laws requiring equitable research partnerships and health system strengthening.
With increased complexity in various global health challenges comes a need for increased precision and the adoption of more tailored health interventions. Building on precision public health, we propose precision global health (PGH), an approach that leverages life sciences, social sciences, and data sciences, augmented with artificial intelligence (AI), in order to identify transnational problems and deliver targeted and impactful interventions through integrated and participatory approaches. With more than four billion Internet users across the globe and the accelerating power of AI, PGH taps on our current augmented capacity to collect, integrate, analyse and visualise large volumes of data, both non-specific and specific to health. With the support of governments and donors, and together with international and non-governmental organisations, universities and research institutions can generate innovative solutions to improve health and wellbeing of the most vulnerable populations around the world. In line with the Sustainable Development Goals, we propose here a road map for the development and implementation of PGH.
