Polypharmacy Thresholds that Best Predict Emergency Room Visits and Mortality in Older Adults: A Population-Based Study in Québec, Canada.

A virtual revolution is ongoing in the use of simulation technology for clinical and educational purposes. When discussion of the potential use of extended reality (XR) applications for human research and clinical intervention first emerged in the early 1990s, the technology required further development for feasibility in both clinical and educational spheres. Consequently, during these early years, XR suffered from a somewhat imbalanced “expectation-to-delivery” ratio. Technologically driven innovations in health care were considered and prototyped during the “computer revolution” in the 1990s. Early advances from this period focused on research and development (R&D) that aimed at enhancing productivity in patient documentation and recordkeeping, improving access to clinical care via internet-based teletherapy, and using virtual reality (VR) simulations to deliver exposure therapy for treating specific phobias and post-traumatic stress disorder (PTSD) and for neurocognitive assessment and rehabilitation. However, since those early days, the technology required to deliver health care applications has significantly matured. This can be observed in the continuing advances of underlying technologies (e.g., computational speed, 3D graphics rendering, audio/visual/haptic displays, user interfaces/tracking, voice recognition, wearable sensors, artificial intelligence [AI], and authoring software) that support the creation of low-cost, yet sophisticated XR systems capable of running on commodity-level personal computers, mobile devices, and stand-alone head-mounted display systems. Driven largely by advancements in the digital gaming and entertainment industries, these technological developments have provided the hardware and software platforms needed for practical, high-fidelity XR experiences in human research and clinical interventions. Thus, evolving mental and physical health care applications can now usefully leverage the interactive and immersive assets that XR affords as the technology continues to get faster, better, and cheaper. While such advances have now enabled the creation of more believable context-relevant “structural” VR environments (e.g., combat scenes, homes, hospital settings, classrooms, offices, markets, and natural and imaginative surreal worlds with the focus on representing places and spatial context rather than interactive agents), the next step in the evolution of medical XR involves the creation of virtual human (VH) representations that can engage real human users in credible and meaningful interactions. The stage is set for a transformative leap in the use of VH agents, leveraging advanced AI technologies such as natural language processing (NLP), machine learning, and deep neural networks to serve as virtual interactors across a wide range of clinical applications. These specific kinds of VHs (presented in the literature as chatbot, conversational agents, conversational AI, virtual assistant, intelligent personal assistant, conversational interface, natural language interface, embodied conversational agent, cognitive agent, or interactive AI) can be used to populate immersive VR/augmented reality environments or be delivered on non-immersive displays via home computer screens and mobile phones for user interaction. For the purpose of this article, we refer to these systems collectively as (AICAs). AICAs now operate across a wide range of digital contexts, opening new possibilities for patient-facing health care delivery and clinical training. These applications can support user self-awareness, enhance access to engaging self-care content, and provide emotional support and guidance—all through low-stigma, user-friendly interactions.