Machine Learning and Gene Editing at the Helm of a Societal Evolution

The integration of artificial intelligence (AI) and biotechnology, whilst in its infancy, presents significant opportunities and risks, and proactive policy is needed to manage these emerging technologies. Whilst AI continues to have significant and broad impact, its relevance and complexity magnify when integrated with other emerging technologies. The confluence of Machine Learning (ML), a subset of AI, with gene editing (GE) in particular can foster substantial benefits as well as daunting risks that range from ethics to national security. These complex technologies have implications for multiple sectors, ranging from agriculture and medicine to economic competition and national security. Consideration of technology advancements and policies in different geographic regions, and involvement of multiple organisations further confound this complexity. As the impact of ML and GE expands, forward looking policy is needed to mitigate risks and leverage opportunities. Thus, this study explores the technological and policy implications of the intersection of ML and GE, with a focus on the United States (US), the United Kingdom (UK), China, and the European Union (EU). Analysis of technical and policy developments over time and an assessment of their current state have informed policy recommendations that can help manage beneficial use of technology advancements and their convergence, which can be applied to other sectors. This study is intended for policymakers to prompt reflection on how to best approach the convergence of the two technologies. Technical practitioners may also find it valuable as a resource to consider the type of information and policy stakeholders engage with.

Assessing the Impact of Developments in Genetic Testing on Insurers' Risk Exposure

Predictive genetic testing provides individuals with information about their future risk of developing health conditions. Theoretically, predictive genetic tests could have positive or negative impacts on the insurance industry. If genetic test results stimulate actions to reduce health risks, they may reduce costs to insurers. If disclosed to insurers, such information may allow them to better understand individual- and population-level risks and make insurance more affordable. However, if individuals who know they are at high genetic risk of becoming ill or dying are more likely to apply for insurance than those not at high risk, this may lead to an unanticipated increase in claims. It may be exacerbated if people at low genetic risk are less likely to apply for insurance compared to the general population. If this happened on a large scale it could make the insurance market unsustainable. Determining whether a genetic test could affect the insurance industry is complex and needs to be evaluated on a per-test basis. The Cambridge Centre for Health Services Research, a collaboration between RAND Europe and the University of Cambridge, developed a framework for evaluating the potential impacts on the UK insurance industry arising from predictive genetic tests. It considers the characteristics of genetic tests and behavioural aspects that influence their uptake. It is intended to provide a transparent approach for evaluating whether a specific condition for which a test is available could impact the insurance industry, currently or in the future, and understanding the key factors that influence this.

Innovation as a Driver of Quality and Productivity in UK Healthcare: Creating and Connecting Receptive Places-Emerging Insights

The demand for health services in England is both growing and changing in nature, yet resources are limited in their ability to respond to the scale and scope of need. As a result, the NHS is under increasing pressures to realise productivity gains, while continuing to deliver high quality care. RAND Europe and the University of Manchester have been commissioned to conduct a study to examine the potential of innovation to respond to the challenges the NHS faces, and to help deliver value for money, efficient and effective services. "Innovation" in this study refers to any product, technology or service that is new to the NHS, or applied in a new way, aimed at delivering affordable and improved care. The learning we have gained adds considerable depth to the practical discussions presented regarding how innovation can be first nurtured and then made meaningful and actionable in a variety of settings. This is important given the complexity of health innovation systems and the diversity of elements that need to interact and work together for the overall system to function effectively. We share insights related to skills, capabilities and leadership; motivations and accountabilities; information and evidence; relationships and networks; patient and public engagement; and funding and commissioning. We will develop these detailed learning points into a more systematic analysis as the research evolves. The research is funded by the Department of Health Policy Research Programme, in close collaboration with NHS England and the Office of Life Sciences.