Politics-evidence conflict in national health policy making in Africa: a scoping review.

BACKGROUND: Generally, public health policy-making is hardly a linear process and is characterized by interactions among politicians, institutions, researchers, technocrats and practitioners from diverse fields, as well as brokers, interest groups, financiers and a gamut of other actors. Meanwhile, most public health policies and systems in Africa appear to be built loosely on technical and scientific evidence, but with high political systems and ideologies. While studies on national health policies in Africa are growing, there seems to be inadequate evidence mapping on common themes and concepts across existing literature. PURPOSE: The study seeks to explore the extent and type of evidence that exist on the conflict between politics and scientific evidence in the national health policy-making processes in Africa. METHODS: A thorough literature search was done in PubMed, Cochrane Library, ScienceDirect, Dimensions, Taylor and Francis, Chicago Journals, Emerald Insight, JSTOR and Google Scholar. In total, 43 peer-reviewed articles were eligible and used for this review. RESULT: We found that the conflicts to evidence usage in policy-making include competing interests and lack of commitment; global policy goals, interest/influence, power imbalance and funding, morals; and evidence-based approaches, self-sufficiency, collaboration among actors, policy priorities and existing structures. Barriers to the health policy process include fragmentation among actors, poor advocacy, lack of clarity on the agenda, inadequate evidence, inadequate consultation and corruption. The impact of the politics-evidence conflict includes policy agenda abrogation, suboptimal policy development success and policy implementation inadequacies. CONCLUSIONS: We report that political interests in most cases influence policy-makers and other stakeholders to prioritize financial gains over the use of research evidence to policy goals and targets. This situation has the tendency for inadequate health policies with poor implementation gaps. Addressing these issues requires incorporating relevant evidence into health policies, making strong leadership, effective governance and a commitment to public health.

Artificial intelligence in healthcare: a scoping review of perceived threats to patient rights and safety.

BACKGROUND: The global health system remains determined to leverage on every workable opportunity, including artificial intelligence (AI) to provide care that is consistent with patients' needs. Unfortunately, while AI models generally return high accuracy within the trials in which they are trained, their ability to predict and recommend the best course of care for prospective patients is left to chance. PURPOSE: This review maps evidence between January 1, 2010 to December 31, 2023, on the perceived threats posed by the usage of AI tools in healthcare on patients' rights and safety. METHODS: We deployed the guidelines of Tricco et al. to conduct a comprehensive search of current literature from Nature, PubMed, Scopus, ScienceDirect, Dimensions AI, Web of Science, Ebsco Host, ProQuest, JStore, Semantic Scholar, Taylor & Francis, Emeralds, World Health Organisation, and Google Scholar. In all, 80 peer reviewed articles qualified and were included in this study. RESULTS: We report that there is a real chance of unpredictable errors, inadequate policy and regulatory regime in the use of AI technologies in healthcare. Moreover, medical paternalism, increased healthcare cost and disparities in insurance coverage, data security and privacy concerns, and bias and discriminatory services are imminent in the use of AI tools in healthcare. CONCLUSIONS: Our findings have some critical implications for achieving the Sustainable Development Goals (SDGs) 3.8, 11.7, and 16. We recommend that national governments should lead in the roll-out of AI tools in their healthcare systems. Also, other key actors in the healthcare industry should contribute to developing policies on the use of AI in healthcare systems.