Data-driven research and healthcare: public trust, data governance and the NHS

It is widely acknowledged that trust plays an important role for the acceptability of data sharing practices in research and healthcare, and for the adoption of new health technologies such as AI. Yet there is reported distrust in this domain. Although in the UK, the NHS is one of the most trusted public institutions, public trust does not appear to accompany its data sharing practices for research and innovation, specifically with the private sector, that have been introduced in recent years. In this paper, we examine the question of, what is it about sharing NHS data for research and innovation with for-profit companies that challenges public trust? To address this question, we draw from political theory to provide an account of public trust that helps better understand the relationship between the public and the NHS within a democratic context, as well as, the kind of obligations and expectations that govern this relationship. Then we examine whether the way in which the NHS is managing patient data and its collaboration with the private sector fit under this trust-based relationship. We argue that the datafication of healthcare and the broader 'health and wealth' agenda adopted by consecutive UK governments represent a major shift in the institutional character of the NHS, which brings into question the meaning of public good the NHS is expected to provide, challenging public trust. We conclude by suggesting that to address the problem of public trust, a theoretical and empirical examination of the benefits but also the costs associated with this shift needs to take place, as well as an open conversation at public level to determine what values should be promoted by a public institution like the NHS

Chapter 8 AI in Medicine

AI promises major benefits for healthcare. But along with the benefits come risks. Not so much the risk of powerful super-intelligent machines taking over, but the risk of structural injustices, biases, and inequalities being perpetuated in a system that cannot be challenged because nobody actually knows how the algorithms work. Or, the risk that there might be no doctor or nurse present to hold your hand and reassure you when you are at your most vulnerable. There There are many initiatives to come up with ethical or trustworthy AI and these efforts are important. Yet we should demand more than this. Technological solutionism and the urge to “move fast and break things” often dominate the tech industry but are inappropriate for the healthcare context and incompatible with basic healthcare values of empathy, solidarity, and trust. So how can such socio-political and ethical issues get resolved? It is at this juncture that we have the opportunity to imagine different different different futures. Using Grace's fictional story, this chapter argues that in order to shape the future of healthcare we need to decide whether, to what extent, and how—under what regulatory frameworks and safeguards—these technologies could and should play a part in this future. AI can indeed improve healthcare but, instead of casting ourselves loose and at the mercy of this seemingly inevitable technological drift, drift, we should be actively paddling towards a future of our choice

Sharing whilst caring: solidarity and public trust in a data-driven healthcare system

Background In the UK, the solidaristic character of the NHS makes it one of the most trusted public institutions. In recent years, the introduction of data-driven technologies in healthcare has opened up the space for collaborations with private digital companies seeking access to patient data. However, these collaborations appear to challenge the public’s trust in the. Main text In this paper we explore how the opening of the healthcare sector to private digital companies challenges the existing social contract and the NHS’s solidaristic character, and impacts on public trust. We start by critically discussing different examples of partnerships between the NHS and private companies that collect and use data. We then analyse the relationship between trust and solidarity, and investigate how this relationship changes in the context of digital companies entering the healthcare system. Finally, we show ways for the NHS to maintain public trust by putting in place a solidarity grounded partnership model with companies seeking to access patient data. Such a model would need to serve collective interests through, for example, securing preferential access to goods and services, providing health benefits, and monitoring data access. Conclusion A solidarity grounded partnership model will help establish a social contract or licence that responds to the public’s expectations and to principles of a solidaristic healthcare system

Don’t drone?:negotiating ethics of RPAS in emergency response

This paper explores discourses of automation as a key ethical concern in the development of Remotely Piloted Aircraft Systems for disaster response. We discuss problems arising from ‘humanistic’ dichotomies that pit human against machine, military against civil uses and experts against laypersons. We explore how it may be possible to overcome human-technology dichotomies

AI-driven decision support systems and epistemic reliance: a qualitative study on obstetricians' and midwives' perspectives on integrating AI-driven CTG into clinical decision making

Background: Given that AI-driven decision support systems (AI-DSS) are intended to assist in medical decision making, it is essential that clinicians are willing to incorporate AI-DSS into their practice. This study takes as a case study the use of AI-driven cardiotography (CTG), a type of AI-DSS, in the context of intrapartum care. Focusing on the perspectives of obstetricians and midwives regarding the ethical and trust-related issues of incorporating AI-driven tools in their practice, this paper explores the conditions that AI-driven CTG must fulfill for clinicians to feel justified in incorporating this assistive technology into their decision-making processes regarding interventions in labor. Methods: This study is based on semi-structured interviews conducted online with eight obstetricians and five midwives based in England. Participants were asked about their current decision-making processes about when to intervene in labor, how AI-driven CTG might enhance or disrupt this process, and what it would take for them to trust this kind of technology. Interviews were transcribed verbatim and analyzed with thematic analysis. NVivo software was used to organize thematic codes that recurred in interviews to identify the issues that mattered most to participants. Topics and themes that were repeated across interviews were identified to form the basis of the analysis and conclusions of this paper. Results: There were four major themes that emerged from our interviews with obstetricians and midwives regarding the conditions that AI-driven CTG must fulfill: (1) the importance of accurate and efficient risk assessments; (2) the capacity for personalization and individualized medicine; (3) the lack of significance regarding the type of institution that develops technology; and (4) the need for transparency in the development process. Conclusions: Accuracy, efficiency, personalization abilities, transparency, and clear evidence that it can improve outcomes are conditions that clinicians deem necessary for AI-DSS to meet in order to be considered reliable and therefore worthy of being incorporated into the decision-making process. Importantly, healthcare professionals considered themselves as the epistemic authorities in the clinical context and the bearers of responsibility for delivering appropriate care. Therefore, what mattered to them was being able to evaluate the reliability of AI-DSS on their own terms, and have confidence in implementing them in their practice

"You have to keep fighting": maintaining healthcare services and professionalism on the frontline of austerity in Greece.

BACKGROUND: Greece has been severely affected by the 2008 global economic crisis and its health system was, and still is, among the national institutions most shaped by its effects. METHODS: In 2014, this qualitative study examined these changes through in-depth interviews with 22 frontline healthcare professionals in five different locations in mainland Greece. These interviews with nurses, doctors and pharmacists explored perceptions of austerity and how ideas of professionalism were challenged and revised by these measures. RESULTS: Participants reported working conditions characterised by dramatic increases in public hospital admissions alongside decreases in personnel, consumables, materials, and also many hospital closures. Many drew on analogies of war and fighting to describe the effects of healthcare reforms on their working lives and professional conduct. Despite accounts of deteriorating conditions and numerous challenges, healthcare professionals presented themselves as making every effort to meet patients' needs, while battling to resist guidelines which they perceived diminished their roles to production-line operatives. CONCLUSIONS: Participants considered it their duty to defend their professional ethos and serve patients without compromising standards, even if this meant liberal interpretation and implementation of regulations. These professionals regarded themselves on the frontline of healthcare provision but also the frontline defence in a war on their professional standards from austerity