The Future of Healthcare is Personalized: In Pursuit of the Digital Revolution

Öz

Personalized health is a modern healthcare approach that aims to develop individual-specific strategies for the prevention, early diagnosis, and treatment of diseases by evaluating individuals’ genetic, biological, and clinical data, as well as environmental and lifestyle characteristics, together. Supported by technological advancements, this approach moves away from the traditional healthcare model and transforms into a more person-centered structure. In recent years, with advances in genomic science, the proliferation of digital health technologies, and the development of artificial intelligence-based tools, it has begun to be integrated into clinical practice. This review addresses the concept of personalized health and its integration with the existing system, as well as the role of digital technologies and artificial intelligence that contribute to the process

Anahtar Kelimeler

• Personalized health
• digital health
• artificial inteligent

Sağlıkta Gelecek Kişiye Özel: Dijital Devrimin İzinde

Öz

Kişiselleştirilmiş sağlık, bireylerin genetik, biyolojik, klinik verileri, çevresel ve yaşam tarzı özeliklerinin bir arada değerlendirilerek, hastalıkların önlenmesi, erken teşhisi ve tedavisinde kişiye özgü stratejiler geliştirilmesini hedefleyen modern bir sağlık hizmeti yaklaşımıdır. Teknolojik gelişmelerle desteklenen bu yaklaşım, geleneksel sağlık hizmeti modelinden uzaklaşarak daha birey odaklı bir yapıya dönüşmektedir. Son yıllarda genomik bilimindeki ilerlemeler, dijital sağlık teknolojilerinin yaygınlaşması ve yapay zeka temelli araçların gelişimiyle birlikte, klinik pratiğe entegre edilmeye başlanmıştır. Bu derlemede, kişiselleştirilmiş sağlık kavramı ve mevcut sistemle entegrasyonu, sürece katkı sağlayan dijital teknolojiler ve yapay zekanın rolü ele alınmıştır

Anahtar Kelimeler

• Kişiselleştirilmiş sağlık
• dijital sağlık
• yapay zeka

Kaynakça

1. Hood L, Friend SH. Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat Rev Clin Oncol. 2011;8(3):184-187.
2. Olson MV. The human genome project. Proc Natl Acad Sci USA. 1993;90(10):4338–4344.
3. Collins FS, Green ED, Guttmacher AE, Guyer MS; US National Human Genome Research Institute. A vision for the future of genomics research. Nature. 2003;422(6934):835–847.
4. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18(1):83.
5. Chen R, Mias GI, Li-Pook-Than J, Jiang L, Lam HY, Chen R, et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell. 2012;148(6):1293–1307.
6. Wang L, McLeod HL, Weinshilboum RM. Genomics and drug response. N Engl J Med. 2011;364(12):1144–1153.
7. Trein P. Healthy or sick?: Coevolution of health care and public health in a comparative perspective. 1st ed. Cambridge: Cambridge University Press; 2018.
8. Chong HY, Allotey PA, Chaiyakunapruk N. Current landscape of personalized medicine adoption and implementation in Southeast Asia. BMC Med Genomics. 2018;11(1):94.

9. Sabatello M, Appelbaum PS. The precision medicine nation. Hastings Cent Rep. 2017;47(4):19–29.
10. Heart T, Ben-Assuli O, Shabtai I. A review of PHR, EMR and EHR integration: A more personalized healthcare and public health policy. Health Policy Technol. 2017;6(1):20–25.
11. Lu CY, Williams MS, Ginsburg GS, Toh S, Brown JS, Khoury MJ. A proposed approach to accelerate evidence generation for genomic-based technologies in the context of a learning health system. Genet Med. 2018;20(4):390–396.
12. Phillips KA, Douglas MP, Marshall DA. Expanding use of clinical genome sequencing and the need for more data on implementation. JAMA. 2020;324(20):2029–2030.
13. Trein P, Wagner J. Governing personalized health: A scoping review. Front Genet. 2021;12:650504.
14. Bertier G, Carrot-Zhang J, Ragoussis V, Joly Y. Integrating precision cancer medicine into healthcare—policy, practice, and research challenges. Genome Med. 2016;8(1):108.
15. Chin-Yee B, Subramanian SV, Verma A, Laupacis A, Razak F. Emerging trends in clinical research with implications for population health and health policy. Milbank Q. 2018;96(2):369–401.
16. Schork NJ. Personalized medicine: time for one-person trials. Nature. 2015;520(7549):609–611.
17. Messner DA, Al Naber J, Koay P, Cook-Deegan R, Majumder M, Javitt G, et al. Barriers to clinical adoption of next generation sequencing: perspectives of a policy Delphi panel. Appl Transl Genom. 2016;10:19–24.
18. Bach PB, Giralt SA, Saltz LB. FDA approval of tisagenlecleucel: promise and complexities of a $475000 cancer drug. JAMA. 2017;318(19):1861–1862.
19. Williams JS, Walker RJ, Egede LE. Achieving equity in an evolving healthcare system: opportunities and challenges. Am J Med Sci. 2016;351(1):33–43.
20. Manolio TA, Rowley R, Williams MS, Roden D, Ginsburg GS, Bult C, et al. Opportunities, resources, and techniques for implementing genomics in clinical care. Lancet. 2019;394(10197):511–520.
21. Vayena E, Blasimme A, Cohen IG. Machine learning in medicine: addressing ethical challenges. PLoS Med. 2018;15(11):e1002689.
22. Wilk S, Michalowski M, Michalowski W, Rosu D, Carrier M, Kezadri-Hamiaz M. Comprehensive mitigation framework for concurrent application of multiple clinical practice guidelines. J Biomed Inform. 2017;66:52–71.
23. T.C. Sağlık Bakanlığı. e-Nabız Kişisel Sağlık Sistemi. Available from: https://www.enabiz.gov.tr/
24. SATURK. Türkiye’de Sağlık Hizmetlerine Kolay Erişim: Merkezi Hekim Randevu Sistemi (MHRS). Ankara: SATURK; 2016. Available from: http://www.saturk.gov.tr/images/pdf/hst/kolayerisim.pdf
25. Dimitriou N, Arandjelović O, Harrison DJ, Caie PD. A principled machine learning framework improves accuracy of stage II colorectal cancer prognosis. NPJ Digit Med. 2018;1(1):52.
26. Shaban-Nejad A, Michalowski M, Buckeridge DL. Health intelligence: how artificial intelligence transforms population and personalized health. NPJ Digit Med. 2018;1(1):53.
27. Vollmer S, Mateen BA, Bohner G, Király FJ, Ghani R, Jonsson P, et al. Machine learning and artificial intelligence research for patient benefit. BMJ. 2020;368:m392.
28. Johnson KB, Wei WQ, Weeraratne D, Frisse ME, Misulis K, Rhee K, et al. Precision medicine, AI, and the future of personalized health care. Clin Transl Sci. 2021;14(1):86–93.
29. Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A. A survey on bias and fairness in machine learning. ACM Comput Surv. 2021;54(6):1–35.
30. Rachel KEB, Kuntal D, Michael H, Samuel CH, Stephanie H. AI Fairness 360: An extensible toolkit for detecting, understanding, and mitigating unwanted algorithmic bias. arXiv. 2018; arXiv:1810.01943.
31. Zech JR, Badgeley MA, Liu M, Costa AB, Titano JJ, Oermann EK. Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs. PLoS Med. 2018;15(11):e1002683.
32. Sendak MP, D’Arcy J, Kashyap S, Gao M, Nichols M, Corey K, et al. A path for translation of machine learning products into healthcare delivery. EMJ Innov. 2020;10:19-00172.
33. Minari J, Brothers KB, Morrison M. Tensions in ethics and policy created by National Precision Medicine Programs. Hum Genomics. 2018;12(1):22.
34. Mamiya H, Shaban-Nejad A, Buckeridge DL. Online public health intelligence: ethical considerations at the big data era. In: Public Health Intelligence and the Internet. Cham: Springer; 2017. p.129–148.