The literature review used the keyword 'Six Sigma, Prevention Infection'. Method: The database used to identify suitable articles obtained from Scopus, Pubmed, Ebscohost and Summon was limited to the last 5 years of publication from 2014 to 2019, English, and fulltext articles. Purpose: to examine the available evidence about the application of the Six Sigma method in the prevention and control of infections in hospitals. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.īackground: Prevention and control of infections is a priority in the current health sector, and prevention of infection is an indicator of the quality of services provided. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Though foundation models are based on conventional deep learning and transfer learning, their scale results in new emergent capabilities, and their effectiveness across so many tasks incentivizes homogenization. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles (e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). We call these models foundation models to underscore their critically central yet incomplete character. AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.

Dr stefan kaiser virginia mason wa