Paddy Padmanabhan, founder and CEO of Damo Consulting Inc., is a digital health entrepreneur, author and host of The Big Unlock podcast. As someone who works in digital health and telehealth and advises large health systems on their digital transformation road maps, I am a true believer in the potential for remote and real-time virtual care modalities to transform healthcare. The Covid-19 pandemic made telehealth and remote care a necessity because of restrictions on in-person care. More recently, the growth in the virtual care and telehealth models is due to an acute shortage of workers at physical locations. In fact, according to the American College of Healthcare Executives' annual survey of top issues confronting hospitals, personnel shortages ranked as the top concern for hospital CEOs in 2021.

The benefit of multi-task learning over single-task learning relies on the ability to use relations across tasks to improve performance on any single task. While sharing representations is an important mechanism to share information across tasks, its success depends on how well the structure underlying the tasks is captured. In some real-world situations, we have access to metadata, or additional information about a task, that may not provide any new insight in the context of a single task setup alone but inform relations across multiple tasks. While this metadata can be useful for improving multi-task learning performance, effectively incorporating it can be an additional challenge. We posit that an efficient approach to knowledge transfer is through the use of multiple context-dependent, composable representations shared across a family of tasks. In this framework, metadata can help to learn interpretable representations and provide the context to inform which representations to compose and how to compose them. We use the proposed approach to obtain state-of-the-art results in Meta-World, a challenging multi-task benchmark consisting of 50 distinct robotic manipulation tasks.

Healthcare is an essential, dynamic, and opportunity-rich industry. The demand for innovation to drive simultaneous improvement in health outcomes, affordability, quality, and access will continue to be high. As we look ahead, we suggest keeping an eye on the following seven trends. Multiple forces (including the mitigation of additional funding from the Affordable Care Act) are combining to form headwinds against profit pool growth in healthcare. New business models that create significant healthcare value (that is, substantially better cost, quality, and outcomes) will be critical--and are emerging.

The unique nature of the Science article is its reference to race. Doing scientific work to test healthcare algorithms is difficult. Many are concealed behind a wall of intellectual property protection. They may be trade secrets, and unlike patents, unavailable to the public. Discussing the methodologies used to do this is beyond the scope of this article.

A report from research firm IDC has thrown more light on the emerging importance of Artificial Intelligence (AI). According to a new Worldwide Semiannual Cognitive/Artificial Intelligence Systems Spending Guide, spending on AI technologies is all set to surge from $8 billion in 2016 to $47 billion in 2020. With healthcare being one of the industries that will invest the most on cognitive/ AI systems, it is apparent that this industry is betting big on AI technologies. Virtual assistants, intelligent automation and cognitive computing all are going to impact various facets of healthcare – from operations to patient-centric care to precision medicine. Data is a huge driving force in the world of healthcare today.