Digital healthcare: the new frontier of holistic and efficient care

Advancing Health Equity Through Artificial Intelligence: An Educational Framework for Preparing Nurses in Clinical Practice and Research

“…For example, ML-driven predictive analytics facilitate early detection of patient deterioration, thus improving patient outcomes and care quality (Keim-Malpass & Moorman, 2021). By analyzing extensive datasets, ML-driven analytics provides insights that can identify health risks, refine diagnoses and treatments, and prevent adverse health events (Chilla, 2023). This technology is increasingly used in nursing for patient monitoring, chronic disease management, and emergency preparedness (Hwang et al, 2022; Pailaha, 2023).…”

“…However, assembling large, high-quality datasets for AI training presents significant challenges. One major issue is that the fragmentation and incompatibility of patient data across different electronic health records (EHRs) and software platforms (Chilla, 2023) hamper the collection of comprehensive patient information essential for the effective training, testing, and validation of AI systems. The absence of standardized medical data further complicates these processes (Kelkar, 2023).…”

Patient-reported outcome measures in emergency and acute care: looking beyond the emergency room

“…PROMs built with modern measurement theory that can serve multiple purposes, including condition-specific problems and yet retain the ability to compare across diseases, conditions, populations, and systems, need to be developed. These novel, multipurpose PROMs then need to be operationalized by leveraging health IT and interoperability standards [34,35]. PROs data could then "speak a common language" and efficiently track outcomes longitudinally at the individual patient, clinician, community, population, and even global levels.…”