Resumen Ésta es la primera traducción al español de las guías “Atención después de la investigación: un marco para los comités de ética de investigación del National Health Service (NHS) (borrador versión 8.0)”. El documento afirma que existe una fuerte obligación moral de garantizar que los participantes enfermos de un estudio clínico hagan una transición después del estudio hacia una atención de la salud apropiada. Con “atención de la salud apropiada” se hace referencia al acceso para los (...) participantes a la atención de la salud, proporcionada principalmente por el National Health Service (en adelante NHS), el sistema de salud del Reino Unido, y/o a la intervención en estudio, también llamada producto o tratamiento en investigación. Las guías “Atención después de la investigación” están dirigidas principalmente a los miembros de los 79 comités de ética de investigación del NHS y a quienes presentan sus estudios de investigación ante estos comités. Se trata de un documento borrador muy avanzado, trabajado en numerosas reuniones, durante más de 3 años, que ha sido discutido por participantes de estudios y miembros de la comunidad, miembros y presidentes de comités de ética de investigación del Reino Unido, especialistas internacionales en ética de la investigación, representantes de la industria y otras partes interesadas. La redacción de las guías es producto de la colaboración de Neema Sofaer y Penney Lewis, ambas investigadoras del King’s College London, en el Centre of Medical Law and Ethics del Dickson Poon School of Law, y Hugh Davies, Asesor en Ética de la Investigación de la Health Research Authority (HRA) del NHS. El traductor del presente documento, Ignacio Mastroleo, participó del workshop en la Fundación Brocher (Ginebra, Suiza, diciembre de 2011) donde se revisó el borrador versión 7.0 y contribuyó en la revisión del borrador de la versión 8.0 durante el 2012. -/- Abstract This is the first Spanish translation of the guidelines “Care after research: a framework for NHS RECs (8th draft)”. The document states that there is a strong moral obligation to ensure that participants in a clinical study who are ill transition after the study to appropriate healthcare. The terms "appropriate healthcare" refer to participants’ access to health care, mainly provided by the National Health Service (NHS), the health system in the UK, and/or to the intervention study also named investigational treatment or product. The guides "Care after research" are mainly directed to members of the 79 NHS committees of research ethics (RECs) and to those who submit their research to these committees. This is a very advanced draft document, worked in several meetings, for more than three years, that has been discussed with study participants and community members, members and chairs of RECs in the UK, international specialists in research ethics, industry representatives and other stakeholders. The drafting of the guidelines is the result of the collaboration of Neema Sofaer and Penney Lewis, both researchers at King's College London, at the Centre of Medical Law and Ethics of Dickson Poon School of Law, and Hugh Davies, Research Ethics Advisor of the Health Research Authority (HRA) of the NHS. The translator of this document, Ignacio Mastroleo, attended the workshop at the Brocher Foundation (Geneva, Switzerland, December 2011) where draft version 7.0 was revised and contributed in the revision of draft version 8.0 during 2012. (shrink)

On 8th August 2019, Secretary of State for Health and Social Care, Matt Hancock, announced the creation of a £250 million NHS AI Lab. This significant investment is justified on the belief that transforming the UK’s National Health Service (NHS) into a more informationally mature and heterogeneous organisation, reliant on data-based and algorithmically-driven interactions, will offer significant benefit to patients, clinicians, and the overall system. These opportunities are realistic and should not be wasted. However, they may (...) be missed (one may recall the troubled Care.data programme) if the ethical challenges posed by this transformation are not carefully considered from the start, and then addressed thoroughly, systematically, and in a socially participatory way. To deal with this serious risk, the NHS AI Lab should create an Ethics Advisory Board and monitor, analyse, and address the normative and overarching ethical issues that arise at the individual, interpersonal, group, institutional and societal levels in AI for healthcare. (shrink)

It has been suggested that to overcome the challenges facing the UK’s National Health Service (NHS) of an ageing population and reduced available funding, the NHS should be transformed into a more informationally mature and heterogeneous organisation, reliant on data-based and algorithmically-driven interactions between human, artificial, and hybrid (semi-artificial) agents. This transformation process would offer significant benefit to patients, clinicians, and the overall system, but it would also rely on a fundamental transformation of the healthcare system in (...) a way that poses significant governance challenges. In this article, we argue that a fruitful way to overcome these challenges is by adopting a pro-ethical approach to design that analyses the system as a whole, keeps society-in-the-loop throughout the process, and distributes responsibility evenly across all nodes in the system. (shrink)

This article highlights the limitations of the tendency to frame health- and wellbeing-related digital tools (mHealth technologies) as empowering devices, especially as they play an increasingly important role in the National Health Service (NHS) in the UK. It argues that mHealth technologies should instead be framed as digital companions. This shift from empowerment to companionship is advocated by showing the conceptual, ethical, and methodological issues challenging the narrative of empowerment, and by arguing that such challenges, as (...) well as the risk of medical paternalism, can be overcome by focusing on the potential for mHealth tools to mediate the relationship between recipients of clinical advice and givers of clinical advice, in ways that allow for contextual flexibility in the balance between patiency and agency. The article concludes by stressing that reframing the narrative cannot be the only means for avoiding harm caused to the NHS as a healthcare system by the introduction of mHealth tools. Future discussion will be needed on the overarching role of responsible design. (shrink)

Corruption deprives people of access to health care and can lead to the wrong treatments being administered. Drug counterfeiting, facilitated by corruption, kills en masse. Cases are recorded of water being substituted for life-saving adrenaline and of active ingredients being diluted by counterfeiters, triggering drug-resistant strains of malaria, tuberculosis and HIV. The poor are disproportionately affected by corruption in the health sector, and cannot afford to pay for private alternatives where corruption has depleted public health services. Analysis (...) of corruption in the health sector of the UK can redress the information imbalance between UK government and service providers and patients. Health is a major global industry, a key responsibility and budget expense for governments and businesses. Corruption deprives people of access to health care and leads to poor health outcomes. (shrink)

Artificial Intelligence (AI) technologies are now widely used in a variety of fields to aid with knowledge acquisition and decision-making. Health information systems, in particular, can gain the most from AI advantages. Recently, symptoms-based illness prediction research and manufacturing have grown in popularity in the healthcare business. Several scholars and organisations have expressed an interest in applying contemporary computational tools to analyse and create novel approaches for rapidly and accurately predicting illnesses. In this study, we present a paradigm for (...) assessing the efficacy of combining Machine Learning (ML) and Natural Language Processing (NLP) technologies in a disease prediction system. We scraped a disease-symptom dataset with NLP characteristics from one of the UK's most trusted National Health Service (NHS) websites as an example. In addition, we will thoroughly examine our data using symptom frequency, similarity, and clustering analysis. As a consequence, we can observe that the forecast has a high efficiency rate, but there are still some challenges to work out. (shrink)