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CC BY 4.0 · Yearb Med Inform 2024; 33(01): 064-069
DOI: 10.1055/s-0044-1800720
Special Section: Digital Health for Precision in Prevention
Working Group Contributions

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

Authors

  • Edwin Chi Ho Lau

    1   The Hong Kong Hospital Authority

  • Vije Kumar Rajput

    2   London School of Hygiene and Tropical Medicine

  • Inga Hunter

    3   Massey University

  • Jose F. Florez-Arango

    4   Weill Cornell Medicine Department of Population Health Science

  • Prasad Ranatunga

    5   Provincial Department of Health Services, North-Western Province, Sri Lanka

  • Klaus D. Veil

    6   Western Sydney University

  • Gumindu Kulatunga

    7   Health Information Unit, Ministry of Health, Sri Lanka

  • Shashi Gogia

    8   Society for Administration of Telemedicine and Health Care Informatics

  • Craig Kuziemsky

    9   MacEwan University

  • Marcia Ito

    10   Unidade de Pós-Graduação, Extensão e Pesquisa do Centro Paula Souza

  • Usman Iqbal

    11   School of Population Health, Faculty of Medicine and Health, University Of New South Wales, Sydney, Australia

  • Sheila John

    12   Teleophthalmology and E-learning departments, Sankara Nethralaya, Chennai, India

  • Sriram Iyengar

    13   Department of Internal Medicine, University of Arizona College of Medicine

  • Anandhi Ramachandran

    14   Department of Health Information Technology, International Institute of Health Management Research, Delhi, India

  • Arindam Basu

    15   University of Canterbury
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Summary

Introduction: 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.

Keywords

Precision - Prevention - Telehealth


Publication History

Article published online:
08 April 2025

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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  • References

  • 1 Afshin A, Sur PJ, Fay KA, Ferrara G, Salama JS, et al. Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet (British edition) 2019, 393(10184), 1958-1972. https://doi.org/10.1016/S0140-6736(19)30041-8
  • 2 Denny JC, Collins FS. Precision medicine in 2030-seven ways to transform healthcare. Cell. 2021;184(6):1415-1419. https://doi.org/10.1016/j.cell.2021.01.015
  • 3 Shaban-Nejad A, Michalowski M, Peek N, Brownstein JS, Buckeridge DL. Seven pillars of precision digital health and medicine. Artif Intell Med. 2020;103:101793. https://doi.org/10.1016/j.artmed.2020.10179
  • 4 Kuziemsky C, Maeder AJ, John O, Gogia SB, Basu A, Meher S, Ito M. Role of Artificial Intelligence within the Telehealth Domain. Yearb Med Inform 2019, 28(1), 35-40. https://doi.org/10.1055/s-0039-1677897
  • 5 Rose G. Sick Individuals and Sick Populations. International Journal of Epidemiology 1985, 14(1), 32-38. https://doi.org/10.1093/ije/14.1.32
  • 6 Chiolero A, Paradis G, Paccaud F. The pseudo-high-risk prevention strategy. International Journal of Epidemiology 2015, 44(5), 1469-1473. https://doi.org/10.1093/ije/dyv102
  • 7 Delpierre C, Lefèvre T. Precision and personalized medicine: What their current definition says and silences about the model of health they promote. Implication for the development of personalized health. Frontiers in sociology 2023, 8, 1112159. https://doi.org/10.3389/fsoc.2023.1112159
  • 8 Tong HL, Quiroz JC, Kocaballi AB, Fat SCM, Dao KP, Gehringer H et al. Personalized mobile technologies for lifestyle behavior change: A systematic review, meta-analysis, and meta-regression. Preventive Medicine 2021, 148, 106532. https://doi.org/https://doi.org/10.1016/j.ypmed.2021.106532
  • 9 Rebbeck TR. Precision prevention of cancer. Cancer Epidemiol Biomarkers Prev 2014, 23(12), 2713-2715. https://doi.org/10.1158/1055-9965.Epi-14-1058
  • 10 Collins FS, Varmus H. A New Initiative on Precision Medicine. The New England journal of medicine 2015, 372(9), 793-795. https://doi.org/10.1056/NEJMp1500523
  • 11 Taylor-Robinson D, Kee F. Precision public health—the Emperor's new clothes. International Journal of Epidemiology 2019, 48(1), 1-6. https://doi.org/10.1093/ije/dyy184
  • 12 Canfell OJ, Kodiyattu Z, Eakin E, Burton-Jones A, Wong I, Macaulay C, Sullivan C. Real-world data for precision public health of noncommunicable diseases: a scoping review. BMC public health 2022, 22(1), 2166. https://doi.org/10.1186/s12889-022-14452-7
  • 13 Fernald GH, Capriotti E, Daneshjou R, Karczewski KJ, Altman RB. Bioinformatics challenges for personalized medicine. Bioinformatics 2011, 27(13), 1741-48. https://doi.org/10.1093/bioinformatics/btr295
  • 14 Gambhir SS, Ge TJ, Vermesh O, Spitler R. Toward achieving precision health. Science translational medicine 2018, 10(430). https://doi.org/10.1126/scitranslmed.aao3612
  • 15 Morabia A. 120 000 Nurses Who Shook Public Health. American Journal of Public Health 2016, 106(9), 1528-29. https://doi.org/10.2105/ajph.2016.303345
  • 16 Mauch CE, Edney SM, Viana JNM, Gondalia S, Sellak H, Boud SJ, et al. Precision health in behaviour change interventions: A scoping review. Preventive medicine 2022, 163, 107192. https://doi.org/10.1016/j.ypmed.2022.107192
  • 17 Dallagassa MR, dos Santos Garcia C, Scalabrin EE. et al. Opportunities and challenges for applying process mining in healthcare: a systematic mapping study. J Ambient Intell Human Comput 2022 13, 165–82. https://doi.org/10.1007/s12652-021-02894-7
  • 18 Rosa COCS, Ito M, Vieira AB, Gomes ATA. Modelling and Mining of Patient Pathways: A Scoping Review. arXiv:2206.01980v1. 2022. https://doi.org/10.48550/arXiv.2206.01
  • 19 El Saddik A. Digital Twins: The Convergence of Multimedia Technologies. IEEE multimedia 2018, 25(2), 87-92. https://doi.org/10.1109/MMUL.2018.023121167
  • 20 Haleem A, Javaid M, Singh RP, Suman R. Telemedicine for healthcare: Capabilities, features, barriers, and applications. Sensors international 2021, 2, 100117. https://doi.org/10.1016/j.sintl.2021.100117
  • 21 Odone A, Buttigieg S, Ricciardi W, Azzopardi-Muscat N, Staines A. Public health digitalization in Europe: EUPHA vision, action and role in digital public health. European journal of public health 2019, 29, 28-35. https://doi.org/10.1093/eurpub/ckz161
  • 22 Tersalvi G, Winterton D, Cioffi GM, Ghidini S, Roberto M, Biasco L, et al. Telemedicine in Heart Failure During COVID-19: A Step Into the Future [Mini Review]. Frontiers in Cardiovascular Medicine 2020, 7. https://doi.org/10.3389/fcvm.2020.612818
  • 23 Severino P, Prosperi S, D'Amato A, Cestiè C, Myftari V, Maestrini V, et al. Telemedicine: an Effective and Low-Cost Lesson From the COVID-19 Pandemic for the Management of Heart Failure Patients. Current Heart Failure Reports 2023, 20(5), 382-389. https://doi.org/10.1007/s11897-023-00624-y
  • 24 Morillo-Verdugo R, Collado-Borell R, Arrondo-Velasco A, Domínguez-Cantero M, Fernández-Polo A, González-Corominas E, et al. Implementation of pharmaceutical care through Telepharmacy: A guide for professionals and patients. Farm Hosp 2022, 46(7), 115-122.
  • 25 Widmer RJMDP, Allison TGPMPH, Lennon RMS, Lopez-Jimenez FMD, Lerman LOMDP, Lerman AMD. Digital health intervention during cardiac rehabilitation: A randomized controlled trial. The American heart journal 2017, 188, 65-72. https://doi.org/10.1016/j.ahj.2017.02.016
  • 26 Ferguson T, Olds T, Curtis R, Blake H, Crozier AJ, Dankiw K, et al. Effectiveness of wearable activity trackers to increase physical activity and improve health: a systematic review of systematic reviews and meta-analyses. The Lancet Digital Health 2022, 4(8), e615-e626. https://doi.org/https://doi.org/10.1016/S2589-7500(22)00111-X
  • 27 Reid RD, Morrin LI, Beaton LJ, Papadakis S, Kocourek J, McDonnell L, et al. Randomized trial of an internet-based computer-tailored expert system for physical activity in patients with heart disease. Eur J Prev Cardiol 2012, 19(6), 1357-1364. https://doi.org/10.1177/1741826711422988
  • 28 Appel LJ, Clark JM, Yeh H-C, Wang N-Y, Coughlin JW, Daumit G, et al. Comparative Effectiveness of Weight-Loss Interventions in Clinical Practice. New England Journal of Medicine 2011, 365(21), 1959-1968. https://doi.org/10.1056/NEJMoa1108660
  • 29 Gogia SB, Maeder A, Mars M, Hartvigsen G, Basu A, Abbott P. Unintended Consequences of Tele Health and their Possible Solutions: Contribution of the IMIA Working Group on Telehealth. Yearbook of medical informatics 2016, 25(1), 41-46. https://doi.org/10.15265/IY-2016-012
  • 30 Weiner JP, Kfuri T, Chan K, Fowles JB. “e-Iatrogenesis”: the most critical unintended consequence of CPOE and other HIT. J Am Med Inform Assoc 2007, 14(3), 387-388; discussion 389. https://doi.org/10.1197/jamia.M2338
  • 31 Schukat M, McCaldin D, Wang K, Schreier G, Lovell NH, Marschollek M, Redmond SJ. Unintended Consequences of Wearable Sensor Use in Healthcare: Contribution of the IMIA Wearable Sensors in Healthcare WG. Yearbook of medical informatics 2016, 25(1), 73-86. https://doi.org/10.15265/IY-2016-025
  • 32 Kuziemsky C, Hunter I, Udayasankaran JG, Ranatunga P, Kulatunga G, John S, et al. Telehealth as a Means of Enabling Health Equity. Yearbook of medical informatics 2022, 31(1), 060-066. https://doi.org/10.1055/s-0042-1742500
  • 33 Kuziemsky CE, Hunter I, Gogia SB, lyenger S, Kulatunga G, Rajput V, et al. Ethics in Telehealth: Comparison between Guidelines and Practice-based Experience -the Case for Learning Health Systems. Yearbook of medical informatics 2020, 29(1), 044-050. https://doi.org/10.1055/s-0040-1701976
  • 34 Totten AM. Telehealth for acute and chronic care consultations. Agency for Healthcare Research and Quality, 2019.
  • 35 Lewis J, Ray P, Liaw S-T. Recent Worldwide Developments in eHealth and mHealth to more Effectively Manage Cancer and other Chronic Diseases – A Systematic Review. Yearbook of medical informatics 2016, 25(1), 93-108. https://doi.org/10.15265/IY-2016-020
  • 36 Ramakrishna S, Tian L, Wang C, Liao S, Teo WE. (2015). 4 - The process of gaining approval for new medical devices. In Medical Devices (pp. 65-119). https://doi.org/https://doi.org/10.1016/B978-0-08-100289-6.00004-1