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Machine Intelligence (MI) is rapidly becoming an important approach across biomedical discovery, clinical research, medical diagnostics/devices, and precision medicine. Such tools can uncover new possibilities for researchers, physicians, and patients, allowing them to make more informed decisions and achieve better outcomes. When deployed in healthcare settings, these approaches have the potential to enhance efficiency and effectiveness of the health research and care ecosystem, and ultimately improve quality of patient care. In response to the increased use of MI in healthcare, and issues associated when applying such approaches to clinical care settings, the National Institutes of Health (NIH) and National Center for Advancing Translational Sciences (NCATS) co-hosted a Machine Intelligence in Healthcare workshop with the National Cancer Institute (NCI) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) on 12 July 2019. Speakers and attendees included researchers, clinicians and patients/ patient advocates, with representation from industry, academia, and federal agencies. A number of issues were addressed, including: data quality and quantity; access and use of electronic health records (EHRs); transparency and explainability of the system in contrast to the entire clinical workflow; and the impact of bias on system outputs, among other topics. This whitepaper reports on key issues associated with MI specific to applications in the healthcare field, identifies areas of improvement for MI systems in the context of healthcare, and proposes avenues and solutions for these issues, with the aim of surfacing key areas that, if appropriately addressed, could accelerate progress in the field effectively, transparently, and ethically. [ABSTRACT FROM AUTHOR]

This article develops a taxonomy of health care programmes, and assesses whether or not these programmes represent a viable option for addressing key health policy objectives, including efficiency, equity and sustainable financing. A broad literature review was undertaken to identify the various types of health care programmes to develop a taxonomy. Then, a targeted literature review of each type of health care programme was undertaken to collect evidence on the ability of programmes to address health policy objectives. Many health care programmes were identified and grouped into six categories: financial support, patient education, integrated care, special population, national/regional plans, and disease management. Design and implementation of these programmes varied widely within and across categories. Evidence surrounding impact of health care programmes was largely mixed, with evidence of both successful and unsuccessful initiatives. The evidence suggests that when properly implemented, health care programmes can help generate cost savings, improve patient adherence to treatment, address inequities in access to health care, and improve patient health outcomes. However, the evidence relies on a highly heterogeneous sample of studies, study populations and diseases studied; studies are often of variable quality. Further research on this subject would therefore be beneficial. [ABSTRACT FROM AUTHOR]

This article aims to explore the ethical issues arising from attempts to diversify genomic data and include individuals from underserved groups in studies exploring the relationship between genomics and health. We employed a qualitative synthesis design, combining data from three sources: 1) a rapid review of empirical articles published between 2000 and 2022 with a primary or secondary focus on diversifying genomic data, or the inclusion of underserved groups and ethical issues arising from this, 2) an expert workshop and 3) a narrative review. Using these three sources we found that ethical issues are interconnected across structural factors and research practices. Structural issues include failing to engage with the politics of knowledge production, existing inequities, and their effects on how harms and benefits of genomics are distributed. Issues related to research practices include a lack of reflexivity, exploitative dynamics and the failure to prioritise meaningful co-production. Ethical issues arise from both the structure and the practice of research, which can inhibit researcher and participant opportunities to diversify data in an ethical way. Diverse data are not ethical in and of themselves, and without being attentive to the social, historical and political contexts that shape the lives of potential participants, endeavours to diversify genomic data run the risk of worsening existing inequities. Efforts to construct more representative genomic datasets need to develop ethical approaches that are situated within wider attempts to make the enterprise of genomics more equitable., Competing Interests: This review was commissioned by the Diverse Data initiative at Genomics England in January 2022 to gather evidence and learnings from previous data diversification efforts, to inform the initiative’s design. M.M. is the Programme Lead, and L.N. is the Ethics Lead for the initiative. M.M. and L.N. took part in the workshops and commented on drafts of the paper., (© The Author(s) 2023.)