Telehealth and Precision Prevention: Bridging the Gap for Individualised Health Strategies

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dc.citation.issue1
dc.citation.volume33
dc.contributor.authorLau ECH
dc.contributor.authorRajput VK
dc.contributor.authorHunter I
dc.contributor.authorFlorez-Arango JF
dc.contributor.authorRanatunga P
dc.contributor.authorVeil KD
dc.contributor.authorKulatunga G
dc.contributor.authorGogia S
dc.contributor.authorKuziemsky C
dc.contributor.authorIto M
dc.contributor.authorIqbal U
dc.contributor.authorJohn S
dc.contributor.authorIyengar S
dc.contributor.authorRamachandran A
dc.contributor.authorBasu A
dc.date.accessioned2025-05-20T02:49:42Z
dc.date.available2025-05-20T02:49:42Z
dc.date.issued2024-08-01
dc.description.abstractINTRODUCTION: Precision prevention has shown an upsurge in popularity among epidemiologists in both developed and developing countries in the past decade. OBJECTIVES: Initially practiced in oncology, this approach is increasingly adopted in public health to guard against other common non-communicable diseases (NCDs), such as diabetes and cardiovascular diseases. It aims to tailor preventive measures according to each individual's unique characteristics, such as genomic data, socio-demographic features, environmental factors, and cultural background. METHODS: Healthcare information technologies, including telehealth and artificial intelligence (AI), have served as a vital catalyst in the expansion of this field in the past decade. Under this framework, real-time contemporaneous clinical data is collected via a wide range of digital health devices, such as telehealth monitors, wearables, etc., and then analyzed by AI or non-AI prediction models, which then generate preventive recommendations. RESULTS: The utilization of telehealth technologies in the precision prevention of cardiovascular diseases (CVDs) is a very illustrative application. This paper explores these topics as well as certain limitations and unintended consequences (UICs) and outlines telehealth as a core enabler of precision prevention as well as public health.
dc.description.confidentialfalse
dc.format.pagination64-69
dc.identifier.citationLau ECH, Rajput VK, Hunter I, Florez-Arango JF, Ranatunga P, Veil KD, Kulatunga G, Gogia S, Kuziemsky C, Ito M, Iqbal U, John S, Iyengar S, Ramachandran A, Basu A. (2024). Telehealth and Precision Prevention: Bridging the Gap for Individualised Health Strategies. Yearbook of medical informatics. 33. 1. (pp. 64-69).
dc.identifier.doi10.1055/s-0044-1800720
dc.identifier.eissn2364-0502
dc.identifier.elements-typejournal-article
dc.identifier.issn0943-4747
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/72914
dc.languageEnglish
dc.publisherGeorg Thieme Verlag KG
dc.publisher.urihttps://www.thieme-connect.de/products/ejournals/abstract/10.1055/s-0044-1800720
dc.relation.isPartOfYearbook of medical informatics
dc.rights(c) 2024 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectPrecision
dc.subjectPrevention
dc.subjectTelehealth
dc.titleTelehealth and Precision Prevention: Bridging the Gap for Individualised Health Strategies
dc.typeJournal article
pubs.elements-id500674
pubs.organisational-groupOther
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