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The study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.

This study is a narrative review of the literature.

The body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.

While research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.

This paper examines whether participation in quality early child education (ECE) lessens special education needs and insulates children against requiring costly, intensive supports. Sixty years of longitudinal data coupled with new research in the United Kingdom and Canada were examined to demonstrate how quality ECE reduces special education needs and mitigates the intensity of later supports for children with special education needs. Research demonstrates that quality ECE strengthens children's language, literacy/numeracy, behavioural regulation, and enhances high-school completion. International longitudinal studies confirm that two years of quality ECE lowers special education placement by 40–60% for children with cognitive risk factors and 10–30% for social/behavioural risk factors. Explicit social-emotional learning outcomes also need to be embedded into ECE curricular frameworks, as maladaptive behaviours, once entrenched, are more difficult (and costly) to remediate. Children who do not have the benefit of attending quality ECE in the earliest years are more likely to encounter learning difficulties in school, in turn impacting the well-being and prosperity of their families and societies.

While it has long been established that social factors, such as housing, transportation, and income, influence health and health care outcomes, over the last decade, attention to this topic has grown dramatically. Reforms that promote high-quality care as well as responsibility for total cost of care have shifted focus among health care providers toward upstream determinants of health care outcomes. As a result, there has been a proliferation of activity focused on integrating and aligning social and medical care, many of which depend critically on cross-sector alliances. Despite considerable activity in this area, cross-sector alliances in health care remain largely undertheorized. Both literatures stand to gain from more attention to carefully knitting together the theoretical and management literature on alliances with the empirical, health policy and health services literature on cross-sector alliances in health care. In this chapter, we lay out what exists in the current scientific literature as well as a framework for considering much needed work in this area. We organize the literature and our commentary around the lifecycle of alliances: alliance formation, including factors prompting alliance formation, partner selection, and alliance goals; alliance maturity, including the work of these cross-sector alliances, governance, finance and contracts, staffing structure, and rewards; and critical crossroads, including alliance timelines, definitions of success, and dissolution. We also lay out critical areas for future inquiry, including better theorizing on cross-sector alliances, developing typologies of these cross-sector health care alliances, and the role of policy in cross-sector alliances.