ABSTRACT
Virtual care is here to stay. The explosive expansion of telehealth caused by the SARS-CoV-2 pandemic is more than a necessary measure of protection. The key drivers of this transition in healthcare delivery to a virtual setting are changes in patient behavior and expectations and societal attitudes, and prevailing technologies that are impossible to ignore. The younger population - Generation Z - is increasingly connected and mobile-first. We are heading to a world where we expect to see healthcare in general and neurology, in particular, delivered virtually. The medical community should prepare for this overhaul; proper implementation of virtual care from the ground up is the need of the hour. In an era of virtualization, it is up to the medical community to ensure a well-informed patient population, overcome cultural differences and build digital infrastructure with enhanced access and equity in care delivery, especially for the aging neurological patient population, which is not technologically savvy. Virtual care is a continuum of care that needs deeper integration at systematic levels. The design principles of a patient's journey need to be incorporated while simultaneously placing physician satisfaction with a better user experience at the center of implementation. In this paper, we discuss common challenges and pitfalls of virtual care implementation in neurology - logistical, technical, medicolegal, and those faced in incorporating health and medical education into virtual care - intending to provide solutions and strategies.
ABSTRACT
Intracranial aneurysms (IAs) are a significant public health concern. In populations without comorbidity and a mean age of 50 years, their prevalence is up to 3.2%. An efficient method for identifying subjects at high risk of an IA is warranted to provide adequate radiological screening guidelines and effectively allocate medical resources. Artificial intelligence (AI) has received worldwide attention for its impressive performance in image-based tasks. It can serve as an adjunct to physicians in clinical settings, improving diagnostic accuracy while reducing physicians' workload. AI can perform tasks such as pattern recognition, object identification, and problem resolution with human-like intelligence. Based on the data collected for training, AI can assist in decisions in a semi-autonomous manner. Similarly, AI can identify a likely diagnosis and also, select a suitable treatment based on health records or imaging data without any explicit programming (instruction set). Aneurysm rupture prediction is the holy grail of prediction modeling. AI can significantly improve rupture prediction, saving lives and limbs in the process. Nowadays, deep learning (DL) has shown significant potential in accurately detecting lesions on medical imaging and has reached, or perhaps surpassed, an expert-level of diagnosis. This is the first step to accurately diagnose UIAs with increased computational radiomicis. This will not only allow diagnosis but also suggest a treatment course. In the future, we will see an increasing role of AI in both the diagnosis and management of IAs.
ABSTRACT
Teleneurology has provided access to neurological expertise and state-of-the-art stroke care where previously they have been inaccessible. The use of Artificial Intelligence with machine learning to assist telestroke care can be revolutionary. This includes more rapid and more reliable diagnosis through imaging analysis as well as prediction of hospital course and 3-month prognosis. Intelligent Electronic Medical Records can search free text and provide decision assistance by analyzing patient charts. Speech recognition has advanced enough to be reliable and highly convenient. Smart contextually aware communication and alert programs can enhance efficiency of patient flow and improve outcomes. Automated data collection and analysis can make quality improvement and research projects quicker and much less burdensome. Despite current challenges, these synergistic technologies hold immense promise in enhancing the clinician experience, helping to reduce physician burnout while improving patient health outcomes at a lower cost. This brief overview discusses the multifaceted potential of AI use in telestroke.
