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Background:The use of evidence in the European Union's (EU) policymaking remains largely ad hoc, political and opaque. These flaws are substantial, with a residual lack of appropriate methodology and transparency in the selection and use of evidence - including from stakeholder consultations - to inform policymaking across all EU institutions.
Aims and objectives:To build on previous research and suggest a systematic solution to improving the use of evidence in EU policymaking.
Methods:The solution was assessed using principles of good governance of evidence.
Findings:This paper presents the idea of a European Evidence Ombudsman (EEO), a body to systematically and independently make proposals to improve the EU's governance.
Discussion and conclusion:Through better evidenced and more transparent policymaking, the EEO could help improve the legitimacy, quality and predictability of the EU's legislation. Its mandate could include designing, recommending, and upon their approval by the EU institutions, enforcing improvements to evidence governance. This advisory mandate could see validated methods to select and weigh evidence in a transparent manner become a norm. Through using pre-existing EU expertise and engaging independent academics, the ombudsman could have a role as an evidence ambassador, providing an independent voice to strongly criticise the misuse of evidence, and improve evidence accountability and governance across all EU institutions.

National and international medicines agencies have developed innovative methods to expedite promising new medicines to the market and facilitate early patient access. Some of these approval pathways are the conditional approval and the adaptive pathways by the European Medicines Agency (EMA); the Promising Innovative Medicine (PIM) designation and the Early Access to Medicines Scheme (EAMS) by the Medicines and Healthcare Products Regulatory Agency (MHRA), as well as the Fast Track, Breakthrough or Accelerated Approval methods by the Food and Drug Administration (FDA). However, at least in Europe, these methods cannot achieve the goal of improving timely access for patients to new medicines on their own; the reimbursement process also has to become adaptive and flexible. In the past 2 years, the effective access (national patient access) to newly approved oncology drugs ranged from 1 to 30 months, with an extremely high variability between European countries. The goal of early patient access in Europe can only be achieved if the national health technology assessment bodies, such as NICE (ENG), HAS (FR), G-BA (DE) or AIFA (IT), provide harmonized, transparent, flexible, conditional and adaptive methods that adopt the level of evidence accepted by the medicines agencies. The efforts from medicines agencies are welcome but will be in vain if health technology assessments do not follow with similar initiatives, and the European 'postcode' lottery will continue.

BACKGROUND: Although current efforts are made to diminish the incidence and burden of disease, cancer is still widely identified late at stage. This study aims to conduct a systematic review mapping the existent and emerging clinical research on artificial intelligence (AI) in the treatment of cancer and to underpin its integration challenges and opportunities in the European Union (EU) health sector. METHODS: A systematic literature review (SLR) evaluating global clinical trials (CTs; published between 2010 and 2020 or forthcoming) was concluded. Additionally, a horizon scanning (HS) exercise focusing on emerging trends (published between 2017 and 2020) was conducted. RESULTS: Forty-four CTs were identified and analyzed. Selected CTs were divided into three research areas: (i) potential of AI combined with imaging techniques, (ii) AI's applicability in robotic surgery interventions and (iii) AI's potential in clinical decision making. Twenty-one studies presented an interventional nature, nine papers were observational and 14 articles did not explicitly mention the type of study performed. The papers presented an increased heterogeneity in sample size, type of tumour, type of study and reporting of results. In addition, a shift in research is observed and only a small fraction of studies were completed in the EU. These findings could be further linked to the current socio-economic, political, scientific, technological and environmental state of the EU in regard to AI innovation. CONCLUSION: To overcome the challenges threatening the EU's integration of such technology in the healthcare field, new strategies taking into account the EU's socio-economic and political environment are deemed necessary.

<b><i>Introduction:</i></b> Currently, abundances of highly relevant health data are locked up in data silos due to decentralized storage and data protection laws. The health data cooperative (HDC) model is established to make this valuable data available for societal purposes. The aim of this study is to analyse the HDC model and its potentials and challenges. <b><i>Results:</i></b> An HDC is a health data bank. The HDC model has as core principles a cooperative approach, citizen-centredness, not-for-profit structure, data enquiry procedure, worldwide accessibility, cloud computing data storage, open source, and transparency about governance policy. HDC members have access to the HDC platform, which consists of the "core," the "app store," and the "big data." This, respectively, enables the users to collect, store, manage, and share health information, to analyse personal health data, and to conduct big data analytics. Identified potentials of the HDC model are digitization of healthcare information, citizen empowerment, knowledge benefit, patient empowerment, cloud computing data storage, and reduction in healthcare expenses. Nevertheless, there are also challenges linked with this approach, including privacy and data security, citizens' restraint, disclosure of clinical results, big data, and commercial interest. <b><i>Limitations and Outlook:</i></b> The results of this article are not generalizable because multiple studies with a limited number of study participants are included. Therefore, it is recommended to undertake further elaborate research on these topics among larger and various groups of individuals. Additionally, more pilots on the HDC model are required before it can be fully implemented. Moreover, when the HDC model becomes operational, further research on its performances should be undertaken.

Digitization is considered to radically transform healthcare. As such, with seemingly unlimited opportunities to collect data, it will play an important role in the public health policy-making process. In this context, health data cooperatives (HDC) are a key component and core element for public health policy-making and for exploiting the potential of all the existing and rapidly emerging data sources. Being able to leverage all the data requires overcoming the computational, algorithmic, and technological challenges that characterize today's highly heterogeneous data landscape, as well as a host of diverse regulatory, normative, governance, and policy constraints. The full potential of big data can only be realized if data are being made accessible and shared. Treating research data as a public good, creating HDC to empower citizens through citizen-owned health data, and allowing data access for research and the development of new diagnostics, therapies, and public health policies will yield the transformative impact of digital health. The HDC model for data governance is an arrangement, based on moral codes, that encourages citizens to participate in the improvement of their own health. This then enables public health institutions and policymakers to monitor policy changes and evaluate their impact and risk on a population level.

<b><i>Background:</i></b> Knowledge in the era of Omics and Big Data has been increasingly conceptualized as a public good. Sharing of de-identified patient data has been advocated as a means to increase confidence and public trust in the results of clinical trials. On the other hand, research has shown that the current research and development model of the biopharmaceutical industry has reached its innovation capacity. In response to that, the biopharmaceutical industry has adopted open innovation practices, with sharing of clinical trial data being among the most interesting ones. However, due to the free rider problem, clinical trial data sharing among biopharmaceutical companies could undermine their innovativeness. <b><i>Method:</i></b> Based on the theory of public goods, we have developed a commons arrangement and devised a model, which enables secure and fair clinical trial data sharing over a Virtual Knowledge Bank based on a web platform. Our model uses data as a virtual currency and treats knowledge as a club good. <b><i>Results:</i></b> Fair sharing of clinical trial data over the Virtual Knowledge Bank has positive effects on the innovation capacity of the biopharmaceutical industry without compromising the intellectual rights, proprietary interests and competitiveness of the latter. <b><i>Conclusion:</i></b> The Virtual Knowledge Bank is a sustainable and self-expanding model for secure and fair clinical trial data sharing that allows for sharing of clinical trial data, while at the same time it increases the innovation capacity of the biopharmaceutical industry.

The integration of genomics into public health research, policy and practice will be one of the most important future challenges that our health care systems will face. The next decade will provide a window of opportunity to establish infrastructures that will enable the scientific advances to be translated into evidence-based policies and interventions that improve population health. Approaches for national, European and international institutionalization of public health genomics are shown that aim to champion these challenges.

It is now recognized worldwide that anticipation and prevention of diseases have significant advantages for the health and healthy ageing of the population. Early recognition of the disease in a vulnerable population such as in children aged <5 years and adults aged >60 years enhances our preparedness for any eventualities and future burden of the diseases to society. It is also recognized that current public health practices alone cannot bring about the desired outcome. When tackling public health-related issues, such problems must be recognized and state-of-the-art principles and innovations from genomic sciences, information technologies, and medical specialties must be encompassed and embraced. These will enhance strategies for preparedness and provide us with a better understanding of how to identify, manage, and control disease burdens. The ever expanding landscape of genomics research also includes experimental and computational approaches for effectively utilizing DNA sequence information. From these perspectives, the intricacies of Mendelian single gene disorders are the least challenging compared to intricacies of multi-dimensional host factors for infectious diseases or complex disorders such as cancer. The concepts of public health in India are on firm footing; however, integration of contemporary advances to implement public health principles into practice has neither been attempted nor impacted on disease burden or our preparedness to prevent eventualities. At the same time, translational genomics is gradually paving the way for personalized medicine. Principles of personalized medicine remain to be fully understood and practiced despite the pharmacogenomics-based future of drug development, and treatment has not been as exciting as the advances in genomics we are witnessing today. The relevance, importance, and translational impediments of these advances will be discussed.

Scientific knowledge and our understanding of the human body and diseases have limited any possible treatment tailoring to each patient. The technological advances enabling the integration of various data sets (e.g. '-omics', microbiome, epigenetics and environmental exposure) have facilitated a greater understanding of the human body, the molecular basis of disease and all the factors influencing disease onset, progression and response to treatment, thereby ushering in the era of personalized medicine. We evaluate the regulatory approaches available to facilitate early patient access to efficacious and safe compounds in the EU and the USA in order to make more informed recommendations in the future as to the gaps in regulations for early patient access. An in-depth analysis of conditional approvals (EU) and accelerated approvals (USA) is performed based on the publicly available information (European public assessment reports and a summary review of products approved under both programmes). The types of product, indications, time to approval and type of evidence submitted were analysed. Between 2007 and early 2015, 17 products were conditionally approved in the EU and 25 in the USA, most of them in the area of oncology and based on evidence from phase II clinical trial data. Early approval of promising products based on data from early phases of development is already possible in the EU and the USA. Some of the improvements could entail implementing a rolling assessment of evidence in Europe and extending the scope of early dialogues.