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 ...

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 ...

CSA's AICM v1.1 expands the AI security framework into a bundled control, assessment, audit and standards-mapping package.

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 ...

As organizations invest billions of dollars in artificial intelligence, most still struggle to translate those investments into measurable results. Researchers at Carnegie Mellon University’s Software ...

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 ...

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, ...

Adoption of Telemedicine for Postoperative Follow-Up After Inpatient Cancer-Related Surgery Data were collected from an ambulatory oncology clinic at the University of Miami Health System from October ...

The Reserve Bank of India has released a draft circular proposing a comprehensive risk management framework for AI and ...