Remote patient monitoring programs collect and analyze a variety of health-related data to detect clinical deterioration with the goal of early intervention. There are many program designs with various deployed devices, monitoring schemes, and escalation protocols. Although several factors are considered, the disease state plays a foundational role when designing a specific program. Remote patient monitoring is used both in chronic disease states and patients with acute self-limited conditions. These programs use health-related data to identify early deterioration and then successfully intervene to improve clinical outcomes and decrease costs of care.
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http://dx.doi.org/10.1016/j.pop.2022.05.005 | DOI Listing |
While telegenetic counseling has increased substantially since the start of the COVID-19 pandemic, previous studies reported concerns around building rapport, nonverbal communication, and the patient-counselor relationship. This qualitative evaluation elicited feedback from genetic counselors, referring clinicians, and patients from a single healthcare organization to understand the user-driven reasons for overall satisfaction and experience. We conducted 22 in-depth, semi-structured interviews with participants from all 3 groups between February 2022 and February 2023.
View Article and Find Full Text PDFJMIR Med Educ
January 2025
Centre for Digital Transformation of Health, University of Melbourne, Carlton, Australia.
Background: Learning health systems (LHS) have the potential to use health data in real time through rapid and continuous cycles of data interrogation, implementing insights to practice, feedback, and practice change. However, there is a lack of an appropriately skilled interprofessional informatics workforce that can leverage knowledge to design innovative solutions. Therefore, there is a need to develop tailored professional development training in digital health, to foster skilled interprofessional learning communities in the health care workforce in Australia.
View Article and Find Full Text PDFFront Public Health
January 2025
Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia.
Introduction: The growing demand for real-time, affordable, and accessible healthcare has underscored the need for advanced technologies that can provide timely health monitoring. One such area is predicting arterial blood pressure (BP) using non-invasive methods, which is crucial for managing cardiovascular diseases. This research aims to address the limitations of current healthcare systems, particularly in remote areas, by leveraging deep learning techniques in Smart Health Monitoring (SHM).
View Article and Find Full Text PDFContemp Clin Trials Commun
February 2025
Department of Public Health, Faculty of Health Sciences, University of Stavanger, 4036 Stavanger, Norway.
Background: Chronic illness research has many challenges making research recruitment difficult. Despite reports of facilitators and barriers to research recruitment challenges remain. The reporting of research strategies and their impact on recruitment and subsequent randomised control trials is not sufficient.
View Article and Find Full Text PDFIndian Dermatol Online J
December 2024
Financial Research and Executive Insights, Everest Group, Gurugram, Haryana, India.
Background: Artificial intelligence (AI) is revolutionizing healthcare by enabling systems to perform tasks traditionally requiring human intelligence. In healthcare, AI encompasses various subfields, including machine learning, deep learning, natural language processing, and expert systems. In the specific domain of onychology, AI presents a promising avenue for diagnosing nail disorders, analyzing intricate patterns, and improving diagnostic accuracy.
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