OVERVIEW OF PROACTIVE HEALTHCARE: THE ROLE OF MEDICINE 3.0 AND 5P MEDICINE IN PREVENTION AND EARLY INTERVENTION
Nội dung chính của bài viết
Tóm tắt
Objective: This study aims to explore the integration of 5P Medicine (Predictive, Preventive, Personalized, Participatory, and Precision) within the proactive healthcare model of Medicine 3.0. By focusing on preventive strategies and patient-centered approaches, it examines how advanced technologies, data analytics, and personalized interventions can improve healthcare outcomes.
Method: A comprehensive review of literature and current research on 5P Medicine, Medicine 3.0, and their applications in preventive healthcare was conducted. Key sources included peer-reviewed journals, books, and industry reports discussing technological advancements, patient engagement, and systemic healthcare transitions.
Results: Medicine 3.0 marks a shift from reactive disease management to proactive health promotion and prevention. The 5P Medicine model (Predictive, Preventive, Personalized, Participatory, and Precise) provides a structured framework for early diagnosis, targeted prevention, and personalized treatment. Its implementation relies on data collection, advanced analysis, patient engagement, and precise monitoring. With broad applications in cardiovascular care, cancer prevention, diabetes management, mental health, and aging support, 5P Medicine requires interoperability standards like ISO 23903 for secure data sharing. However, challenges such as data security, cost, and ethical concerns must be addressed for successful adoption.
Conclusion: The shift towards proactive, personalized healthcare through Medicine 3.0 and 5P Medicine holds significant potential for improving patient outcomes and reducing chronic disease burdens. However, successful implementation requires collaboration among healthcare professionals, technology developers, policymakers, and patients to overcome barriers and optimize healthcare delivery.
Chi tiết bài viết
Từ khóa
5P Medicine, Medicine 3.0, preventive healthcare, active healthcare
Tài liệu tham khảo

2. Borrás-Blasco, J., Ramírez-Herráiz, E., Navarro-Ruiz, A. (2024). Integration of persistence in the 5P-medicine approach for age-related chronic diseases. International Journal for Quality in Healthcare. J Qual Health Care. 2024 Apr 22;36(2):mzae026. doi: 10.1093/intqhc/mzae026.


3. Technological Innovations and the Advancement of Preventive Healthcare for Society 5.0 [Internet]. 2023. Available from: https://link.springer.com/chapter/10.1007/978-3-031-36461-7_4

4. Healthcare 2023: A Pressing Need to Move from Reactive to Proactive [Internet]. 2023. Available from: https://www.psqh.com/analysis/healthcare-2023-a-pressing-need-to-move-from-reactive-to-proactive/

5. Gardes J, Maldivi C, Boisset D, Aubourg T, Vuillerme N, Demongeot J. Maxwell®: An Unsupervised Learning Approach for 5P Medicine. Stud Health Technol Inform. 2019;264:1464-5.

6. Hood L, Friend SH. Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat Rev Clin Oncol. 2011;8(3):184-7.

7. Pravettoni G, Gorini A. A P5 cancer medicine approach: Why personalized medicine cannot ignore psychology: P5 medicine. J Eval Clin Pract. 2011;17(4):594-6.

8. Meskó B, Drobni Z, Bényei É, Gergely B, Győrffy Z. Digital health is a cultural transformation of traditional healthcare. Mhealth. 2017;3:38.

9. Islam SR, Kwak D, Kabir MH, Hossain M, Kwak KS. The internet of things for health care: A comprehensive survey. IEEE Access. 2015;3:678-708.

10. Vashist SK, Schneider EM, Luong JH. Commercial smartphone-based devices and smart applications for personalized healthcare monitoring and management. Diagnostics. 2014;4(3):104-28.

11. Zheng YL, Ding XR, Poon CCY, Lo BPL, Zhang H, Zhou XL, et al. Unobtrusive Sensing and Wearable Devices for Health Informatics. IEEE Trans Biomed Eng. 2014;61(5):1538-54.

12. Appelboom G, Camacho E, Abraham ME, Bruce SS, Dumont EL, Zacharia BE, et al. Smart wearable body sensors for patient self-assessment and monitoring. Arch Public Health. 2014;72(1):1-

13. Kvedar J, Coye MJ, Everett W. Connected health: A review of technologies and strategies to improve patient care with telemedicine and telehealth. Health Aff. 2014;33(2):194-9.

14. Granja C, Janssen W, Johansen MA. Factors Determining the Success and Failure of eHealth Interventions: Systematic Review of the Literature. J Med Internet Res. 2018;20(5):e10235.

15. Yadav, V. (2024). Predictive Analytics for Preventive Medicine: Analyzing how

16. Predictive Analytics is Utilized for Forecasting Patient Health Trends and Preventive Disease. Progress in medical sciences, 2024, vol 8, no. 4, page 1 – 6, doi: doi.org/10.47363/pms/2024(8)212

