Background Improvement science has supported the methodological foundations for the application of quality improvement (QI) ...

Software engineers developing artificial intelligence (AI) models using standard frameworks such as Keras, PyTorch, and TensorFlow are usually not well-equipped to translate those models into ...

Artificial Intelligence (AI) deployment in healthcare is accelerating, yet governance frameworks remain fragmented and often assume extensive resources. Through a systematic review of 35 frameworks ...

This workshop will provide an introduction to the types of theories, models, and frameworks (TMFs) commonly used in dissemination and implementation science, including pros and cons and application of ...

Generative AI is designed to create new content from trained parameters. Learning from large amounts of data, many of these models aim to simulate human conversation. Generative AI is being applied to ...

inMorphis (where ServiceNow is an investor), an architect of enterprise-grade AI, synchronizes its delivery engine with the new ServiceNow Context Engine and AI Control Tower to convert platform ...

New technologies are often so brimming with potential that they're difficult to define. In turn, that makes them harder to implement as part of an overarching digital transformation strategy. Many ...

Effective pre-implementation planning is critical for successful adoption of intelligent process automation (IPA). The comprehensive IPA pre-implementation framework outlined in this document provides ...

Over the past decade, health researchers have sought to apply the fundamental principles of implementation science as a systematic and comprehensive approach to improving health care practice, ...

Similar to how we synthesized a framework for value-based payment (VBP)-specific design considerations in previous Health Affairs Forefront work, we present here a brief framework for categorizing the ...