Background: The use of mobile devices in health (mobile health/mHealth) coupled with related technologies promises to transform global health delivery by creating new delivery models that can be integrated with existing health services. These delivery models could facilitate healthcare delivery into rural areas where there is limited access to high-quality access care. Mobile technologies, Internet of Things and 5G connectivity may hold the key to supporting increased velocity, variety and volume of healthcare data.
Objective: The purpose of this study is to identify and analyse challenges related to the current status of India's healthcare system-with a specific focus on mHealth and big-data analytics technologies. To address these challenges, a framework is proposed for integrating the generated mHealth big-data and applying the results in India's healthcare.
Method: A critical review was conducted using electronic sources between December 2018 and February 2019, limited to English language articles and reports published from 2010 onwards.
Main Outcome: This paper describes trending relationships in mHealth with big-data as well as the accessibility of national opportunities when specific barriers and constraints are overcome. The paper concentrates on the healthcare delivery problems faced by rural and low-income communities in India to illustrate more general aspects and identify key issues. A model is proposed that utilises generated data from mHealth devices for big-data analysis that could result in providing insights into the India population health status. The insights could be important for public health planning by the government towards reaching the Universal Health Coverage.
Conclusion: Biomedical, behavioural and lifestyle data from individuals may enable customised and improved healthcare services to be delivered. The analysis of data from mHealth devices can reveal new knowledge to effectively and efficiently support national healthcare demands in less developed nations, without fully accessible healthcare systems.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7062344 | PMC |
| http://dx.doi.org/10.1136/bmjhci-2019-100071 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Identification of key factors related to digital health observational study adherence and retention by data-driven approaches: an exploratory secondary analysis of two prospective longitudinal studies.
Lancet Digit Health
January 2025
Biomedical Engineering Department, Duke University, Durham, NC, USA; Biostatistics and Bioinformatics Department, Duke University, Durham, NC, USA. Electronic address:
Background: Longitudinal digital health studies combine passively collected information from digital devices, such as commercial wearable devices, and actively contributed data, such as surveys, from participants. Although the use of smartphones and access to the internet supports the development of these studies, challenges exist in collecting representative data due to low adherence and retention. We aimed to identify key factors related to adherence and retention in digital health studies and develop a methodology to identify factors that are associated with and might affect study participant engagement.
View Article and Find Full Text PDFLooking Back on Digital Medical Education Over the Last 25 Years and Looking to the Future: Narrative Review.
J Med Internet Res
December 2024
Queen Square Institute of Neurology, University College London, London, United Kingdom.
Background: The last 25 years have seen enormous progression in digital technologies across the whole of the health service, including health education. The rapid evolution and use of web-based and digital techniques have been significantly transforming this field since the beginning of the new millennium. These advancements continue to progress swiftly, even more so after the COVID-19 pandemic.
View Article and Find Full Text PDFApplication of deep learning in wound size measurement using fingernail as the reference.
BMC Med Inform Decis Mak
December 2024
Department of Information Management, Yuan Ze University, Taoyuan, Taiwan.
Objective: Most current wound size measurement devices or applications require manual wound tracing and reference markers. Chronic wound care usually relies on patients or caregivers who might have difficulties using these devices. Considering a more human-centered design, we propose an automatic wound size measurement system by combining three deep learning (DL) models and using fingernails as a reference.
View Article and Find Full Text PDFIt's late, but not too late to transform health systems: a global digital citizen science observatory for local solutions to global problems.
Front Digit Health
November 2024
DEPtH Lab, School of Health Studies, Faculty of Health Sciences, Western University, London, ON, Canada.
A key challenge in monitoring, managing, and mitigating global health crises is the need to coordinate clinical decision-making with systems outside of healthcare. In the 21st century, human engagement with Internet-connected ubiquitous devices generates an enormous amount of big data, which can be used to address complex, intersectoral problems via participatory epidemiology and mHealth approaches that can be operationalized with digital citizen science. These big data - which traditionally exist outside of health systems - are underutilized even though their usage can have significant implications for prediction and prevention of communicable and non-communicable diseases.
View Article and Find Full Text PDFThe Digital Revolution in Medicine: Applications in Cardio-Oncology.
Curr Treat Options Cardiovasc Med
December 2025
Department of Medicine, Medical College of Wisconsin, Milwaukee, WI.
Purpose Of Review: A critical evaluation of contemporary literature regarding the role of big data, artificial intelligence, and digital technologies in precision cardio-oncology care and survivorship, emphasizing innovative and groundbreaking endeavors.
Recent Findings: Artificial intelligence (AI) algorithm models can automate the risk assessment process and augment current subjective clinical decision tools. AI, particularly machine learning (ML), can identify medically significant patterns in large data sets.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!