HealthcarePapers最新文献

Canada's public healthcare system grapples with persistent challenges, including long waiting times and limited coverage for essential services. Fierlbeck and Berman propose a "third way" for Canadian healthcare, which involves a greater role for private service provision and private insurance, alongside improved price controls and efficiency measures. While they provide many sensible policy ideas, several critical issues require clarification, particularly the definition of private insurance, the scope of services it would cover and the potential financial impact on low-income populations. Rather than leaning heavily on private insurance and market-based solutions, expanding the public benefits package and reinforcing regulatory frameworks for integrating private capacity would be more equitable and sustainable paths forward.

The lead article interestingly suggests that there might be a third way between publicly funded health systems and profit-driven private offerings. Securing the principles of universality and equity underpinning the Canada Health Act (1985) is at stake. However, healthcare cost is growing at a pace that is not sustainable; all the while, our health systems fail to keep up with demand. No matter how useful a third way system may prove to be, can it be a complete and sustainable solution? Deeper societal questions such as universality's affordability may also need to be answered. Built-in forces driving healthcare costs up, such as profit-driven research and development of all kinds, medical science advancement and population needs keep increasing costs unsustainably higher. What should we do? This commentary builds on the lead article to help reflect on the matter. Protecting universality and equity may mean redefining them in order to tame powerful hidden forces at play.

The influence of artificial intelligence (AI) is driving transformation in healthcare systems in parallel with similar disruption in other sectors and society at large. In this article, we draw on Kueper and Pandit's (2025) paper to emphasize that this global infusion of AI requires immediate attention from the world's largest group of healthcare practitioners. Nurses have a critical role to play not only in how this technology will change healthcare delivery and their professional practice but also in how it will change the world. Promising nursing-led AI initiatives include improved clinical decision making and prediction, personalized care, digital documentation and resource allocation.

Artificial intelligence (AI) has the potential to improve the patient and provider experience, contribute to better health outcomes, and strengthen the productivity and sustainability of health systems and advance equity. Or to do the opposite. A policy lab involving diverse interest holders identified four guiding principles to help tip the balance toward positive outcomes. Participants noted the importance of bearing in mind the unique characteristics of AI as a technology, right-sizing its use, co-designing solutions and ensuring a focus on equity. The paper also includes reflections from an experienced patient partner on how these core principles apply from her perspective.

Northern and rural health systems experience unique challenges and opportunities for adopting artificial intelligence (AI). An embedded AI researcher could help these systems capitalize on existing strengths to better consider AI use. This professional would collect and manage meaningful health data; bridge the gap between the health workforce and AI tools; and ensure that these tools are adapted to the specific social, economic and cultural needs in the region. Critical research and use of AI tools could advance northern and rural learning health systems to achieve better outcomes while contributing to the global AI agenda.

Artificial intelligence (AI)-enabled tools are transforming healthcare, offering potential benefits such as alleviating administrative burdens, optimizing workflows and supporting diagnostics and personalized treatment for improved patient outcomes. With the increasing availability of AI-enabled tools, it is important to consider the potential for both benefit and harm and what is needed to support generalizable and beneficial, equitable progress. This paper provides a brief history of AI advancements leading to the current state in Canada, reviews trends in applications and research, and discusses the balancing act between achieving positive and negative outcomes. Woven throughout are high-level overviews of concepts and references to key initiatives, regulations and guidelines relevant to the Canadian context as well as more in-depth, contrasting examples to highlight how the apparent explosion of AI is happening at varied paces across applications, specialties and regions. The piece includes system- and population-level perspectives on suspected future implications and needs as the number and type of AI-enabled tools used in healthcare increases.

Artificial intelligence (AI) adoption has progressed unevenly across healthcare disciplines, even for low-risk applications aimed at easing administrative burdens. This commentary examines AI scribes as valuable tools to reduce administrative workload and improve provider well-being. A two-phase evaluation demonstrated significant reductions in documentation time and positive provider feedback, prompting provincial procurement. Highlighting the need for tailored, inclusive evaluations, we propose a structured approach to support broader AI adoption in primary care, focusing on fit-for-purpose assessments, robust simulations and diverse partnerships. This approach aims to foster equitable AI deployment across primary care settings in Canada, improving access and quality of care.

Kueper and Pandit (2025) describe potential benefits and harms of technologies that incorporate artificial intelligence (AI), including bias and equity issues, effects on end-users and downstream impacts on quality of care and cost. They advocate for an iterative, life cycle approach in developing and monitoring "trustworthy" AI. Their model suggests that safe and effective deployment of AI requires "training" for end-users but leave ill-defined what such training might entail. The design of learning programs to facilitate safe incorporation of AI into healthcare must be proactive and deliberate and not an afterthought.

Effective regulations can ensure a minimum level of performance from artificial intelligence (AI) systems. Canadian regulators face two major categories of challenges. First, the AI-specific challenges stem from the unpredictable developments, use, evidence, and acceptable ethical trade-offs around AI systems. These uncertainties can drive the need for flexible definitions of risk, evidentiary threshold, change plan, and post hoc determination of ethical trade-off. These regulatory flexibilities could neglect impactful AI systems, allow regulatory capture, and undermine public oversight. Second, the jurisdictional challenges obfuscate the scope of products, regulatory boundaries, and division of power across regulations. Clarifying regulatory definitions, the responsibilities of professional bodies, and the need for provincial and territorial legislations may help. However, the lack of reason to believe that regulators have clear motivation and capacity to meaningfully protect patient health is worrisome.

The application of artificial intelligence (AI) in healthcare is not a "flash in the pan." As Howell et al. (2024) have described, AI has been evolving since the 1950s, from decision trees to machine learning to generative AI that can create new content. These developments were foreshadowed by science fiction writer Isaac Asimov in a story first published in 1942 in which he outlined three rules of robotics, to the effect that they must not harm humans (Asimov 1950). Fast forward to 2015; Ashrafian (2015) proposed an additional law for AI systems that interact with each other: "all robots endowed with comparable human reason and conscience should act towards one another in a spirit of brotherhood."