COVID-19 has provided a unique boost to the use of digital healthcare technology, putting many vulnerable people at risk of digital exclusion. To promote digital healthcare equity, it is important to identify the challenges that may inhibit cancer patients and family caregivers from benefiting from such technology. This study explored the challenges that cancer patients and family caregivers experience in using digital healthcare technology platforms during the COVID-19 pandemic. A qualitative descriptive study using face-to-face semistructured individual interviews was carried out. A purposive sample of 21 participants was recruited from a public cancer hospital in Saudi Arabia. Qualitative content analysis with an inductive approach was utilized. The factors that challenged the ability of participants to benefit from digital healthcare technology were similar. Four themes related to the challenges the two groups experienced emerged: access to platforms, use of platforms for cancer health-related purposes, attitudes toward these platforms, and individual user preferences. This study identified numerous areas for improvement regarding digital healthcare technology platform implementation, which could increase future benefits and equal use. This study's findings also provide useful information to investigators who intend to create digital nursing interventions for both groups amid COVID-19 and other worldwide health crises.
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http://dx.doi.org/10.1097/CIN.0000000000001087 | DOI Listing |
JMIR Perioper Med
January 2025
Societal Participation & Health, Amsterdam Public Health, Amsterdam, The Netherlands.
Background: Day surgery is being increasingly implemented across Europe, driven in part by capacity problems. Patients recovering at home could benefit from tools tailored to their new care setting to effectively manage their convalescence. The mHealth application ikHerstel is one such tool, but although it administers its functions in the home, its implementation hinges on health care professionals within the hospital.
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Background: To successfully design, develop, implement, and deliver digital health services that provide value, they should be cocreated with patients. However, occasionally, the value may also be codestructed. In the field of health care, the concepts of value cocreation and codestruction still need to be better established within emerging digital health services.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Laboratory for Systems Medicine, Department of Medicine, University of Florida, Gainesville, Florida, United States of America.
This paper describes and validates an algorithm to solve optimal control problems for agent-based models (ABMs). For a given ABM and a given optimal control problem, the algorithm derives a surrogate model, typically lower-dimensional, in the form of a system of ordinary differential equations (ODEs), solves the control problem for the surrogate model, and then transfers it back to the original ABM. It applies to quite general ABMs and offers several options for the ODE structure, depending on what information about the ABM is to be used.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Healthcare Economics and Quality Management, School of Public Health, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Background: The COVID-19 pandemic, declared in March 2020, profoundly affected global health, societal, and economic frameworks. Vaccination became a crucial tactic in combating the virus. Simultaneously, the pandemic likely underscored the internet's role as a vital resource for seeking health information.
View Article and Find Full Text PDFBrain Inform
January 2025
Department of Computing, Glasgow Caledonian University, Glasgow, G4 0BA, Scotland.
A digital twin is a virtual model of a real-world system that updates in real-time. In healthcare, digital twins are gaining popularity for monitoring activities like diet, physical activity, and sleep. However, their application in predicting serious conditions such as heart attacks, brain strokes and cancers remains under investigation, with current research showing limited accuracy in such predictions.
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