Digitalization in Health

EnglishTürkçe

CHAPTER


DOI :10.26650/B/LSB40.2024.035.14   IUP :10.26650/B/LSB40.2024.035.14    Full Text (PDF)

Future Digital Health Trends

Erman Gedikli

Digital Health is the integration of digital technologies into traditional healthcare services, with the aim of improving the quality of human capital in the healthcare system. This includes mobile healthcare, electronic health records, medical analytics, telemedicine, and AI systems. While AI can perform many tasks in healthcare as well as, or better than, humans, the large-scale automation of the jobs of healthcare professionals will be prevented for a considerable period due to implementation factors. The objectives of digital health include improving the quality of care and service outcomes, population health improvement, improving the patient experience, improving the experience of physicians and other non-physician providers, and tackling health inequalities. Cloud computing has been a key enabler of the digitisation of healthcare since around 2005, and its integration into patient and healthcare management systems has enabled healthcare information to be stored and shared across multiple internet-connected devices within IT infrastructures. The future possibilities for AI in healthcare include pattern recognition, diagnosis, therapy selection and generation, personalization, administration and operations, and public health. Innovative technologies such as robotics and AI-driven diagnostics can enhance healthcare delivery, improve patient outcomes, and potentially reduce healthcare costs. However, precision medicine, the use of AI algorithms for accurate diagnoses based on comprehensive data sets, brings ethical concerns and some risks regarding the sharing of patient data. Overall, the integration of digital technologies and AI in healthcare has the potential to revolutionize healthcare delivery and enable personalized medicine. The purpose of this study is to convey the journey of digital transformation in healthcare services and reveal its status and future perspective. 


Keywords: Artificial intelligence, digital health, digital health market

References

  • Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. In A. Bohr & K. Memar-zadeh (Eds.), Artlfîclal Intelllgence in Healthcare (pp. 25-60). Elsevier. google scholar
  • Chiarini, G., Ray, P., Akter, S., Masella, C., & Ganz, A. (2013). mHealth technologies for chronic diseases and elders: A systematic review. IEEE Journal on Selected Areas ln Communlcatlons, 31(9), 6-18. google scholar
  • Davenport, T. C., & Bean, R. (2022). AI-Based innovations at Mayo Clinic. MIT Sloan Management Revlew. https:// sloanreview.mit.edu/article/ai-based-innovations-at-mayo-clinic/ google scholar
  • Davenport, T. H., & Glaser, J. (2002). Just-in-time delivery comes to knowledge management. Harvard Buslness Revlew. google scholar
  • Davenport, T. H., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94-98. https://doi.org/10.7861/futurehosp.6-2-94 google scholar
  • Dawoodbhoy, F. M., Delaney, J., Cecula, P., Yu, J., Peacock, I., Tan, J., & Cox, B. (2021). AI in patient flow: Applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units. Hellyon, 7(5), e06993. https://doi.org/10.1016/j.heliyon.2021.e06993 google scholar
  • Eshwar, M. (2023). Exploring the potential of artificial intelligence in healthcare: Possibilities and challenges. Interna-tlonal Sclentlflc Journal of Englneerlng and Management, 2(04). https://doi.org/10.55041/isjem00408 google scholar
  • Fogel, A. L., & Kvedar, J. C. (2018). Artificial intelligence and powers of digital medicine. NPJ Dlgltal Medlclne, 1, 5. google scholar
  • Ford, K., Portz, J., Zhou, S., Gornail, S., Moore, S., Zhang, X., & Bull, S. (2021). Benefits, facilitators, and recommendations for digital health academic-industry collaboration: A mini review. Frontlers ln Dlgltal Health, 3. https://doi.org/ 10.3389/fdgth.2021.616278 google scholar
  • Frederix, I., Caiani, E., Dendale, P., Anker, S., Bax, J., Böhm, A., & Velde, E. (2019). ESC e-cardiology working group position paper: Overcoming challenges in digital health implementation in cardiovascular medicine. European Journal of Preventlve Cardlology, 26(11), 1166-1177. https://doi.org/10.1177/2047487319832394 google scholar
  • Gajarawala, S. N., & Pelkowski, J. N. (2021). Telehealth benefits and barriers. The Journal for Nurse Practltloners, 17(2), 218-221. google scholar
  • Georgiou, K. E., Georgiou, E., & Satava, R. M. (2021). 5G use in healthcare: The future is present. Journal of the Soclety of Laparoendoscoplc Surgeons, 25(4), e2021.00064. https://doi.org/10.4293/JSLS.2021.00064 google scholar
  • Gu, D., Yang, X., Deng, S., Liang, C., Wang, X., Wu, J., & Guo, J. (2020). Tracking knowledge evolution in cloud health care research: Knowledge map and common word analysis. Journal of Medlcal Internet Research, 22(2), e15142. https://doi.org/10.2196/15142 google scholar
  • Holmes, A., & Watkins, D. (2021, July 6). 7 Emerging trends in AI for life sciences and healthcare in 2021. Mercury Data Sclence. https://www.mercuryds.com/blog/7-emerging-trends-in-ai google scholar
  • Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018). Artificial intelligence in radiology. Nature Revlews Cancer, 18(8), 500-510. google scholar
  • Hussain, A., Malik, A., Halim, M. U., & Ali, A. M. (2014). The use of robotics in surgery: A review. Internatlonal Journal of Cllnlcal Practlce, 68, 1376-1382. google scholar
  • Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230-243. https://doi.org/10.1136/svn-2017-000101 google scholar
  • Kadurin, A., Aliper, A., Kazennov, A., Mamoshina, P., Vanhaelen, Q., Khrabrov, K., & Zhavoronkov, A. (2017). The cornuco-pia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget, 8(7), 10883-10990. google scholar
  • Kasoju, N., Remya, N. S., Sasi, R., et al. (2023). Digital health: Trends, opportunities and challenges in medical devices, pharma and biotechnology. CSIT, 11, 11-30. https://doi.org/10.1007/s40012-023-00380-3 google scholar
  • Knawy, B., McKillop, M., Abduljawad, J., Tarkoma, S., Adil, M., Schaper, L., & Rhee, K. (2022). Successfully implemen-ting digital health to ensure future global health security during pandemics. JAMA Network Open, 5(2), e220214. https://doi.org/10.1001/jamanetworkopen.2022.0214 google scholar
  • Knowles, M., & Somaiya, M. (2024). 2023 year-end digital health funding: Break on through to the other side. Rock Health. https://rockhealth.com/insights/2023-year-end-digital-health-funding/ google scholar
  • Lee, S. I., Celik, S., Logsdon, B. A., et al. (2018). A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia. Nature Communlcatlons, 9, 42. google scholar
  • Liu, W., & Park, E. (2012). E-healthcare security solution framework. International Conference on Computer Communi-cations and Networks (ICCCN). https://doi.org/10.1109/icccn.2012.6289239 google scholar
  • Machleid, F., KaczmarczYk, R., Johann, D., Balciünas, J., Atienza-Carbonell, B., Maltzahn, F., & Mosch, L. (2020). Percepti-ons of digital health education among European medical students: Mixed methods survey. Journal of Medical Internet Research, 22(8), e19827. https://doi.org/10.2196/19827 google scholar
  • Market and Market. (2022). Healthcare analYtic market bY TYpe, Application, Component, End User & Region-Global Forecast to 2027. Market Research Report, Report Code: HIT 2180. https://www.marketsandmarkets.com/Market-Reports/healthcare-data-analYtics-market-905.html google scholar
  • Mathews, S. C., McShea, M. J., HanleY, C. L., et al. (2019). Digital health: A path to validation. NPJ Digital Medicine, 2, 38. https://doi.org/10.1038/s41746-019-0111-3 google scholar
  • McMillan, B., Eastham, R., Brown, B., Fitton, R., & Dickinson, D. (2018). PrimarY care patient records in the United King-dom: Past, present, and future research priorities. Journal of Medical Internet Research, 20(12), e11293. https:// doi.org/10.2196/11293 google scholar
  • Morrison, C., Rimpilâinen, S., Bosnic, I., Thomas, J., & Savage, J. (2022, June). Emerging trends in digital health and care: A refresh post-COVID. Digital Health & Care Innovation Centre, University of Strathclyde, Glasgow. google scholar
  • Palacholla, R., Fischer, N., Coleman, A., Agboola, S., KirleY, K., Felsted, J., & Jethwani, K. (2019). Provider- and patient-re-lated barriers to and facilitators of digital health technologY adoption for hYpertension management: Scoping review. JMIR Cardio, 3(1), e11951. https://doi.org/10.2196/11951 google scholar
  • Parija, S., & Padmavathi, S. (2022). Artificial intelligence in health care. Annals of SBV, 10(2), 23-23. https://doi.org/10. 5005/jp-journals-10085-10145 google scholar
  • Rezazade Mehrizi, M. H., van Ooijen, P., & Homan, M. (2020). Applications of artificial intelligence (AI) in diagnostic ra-diologY: A technographY studY. European Radiology, 31, 1805-1811. https://doi.org/10.1007/s00330-020-07129-7 google scholar
  • Ronquillo, Y., MeYers, A., & Korvek, S. J. (2023, MaY 1). Digital health. In StatPearls [Internet]. Treasure Island (FL): Stat-Pearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK470260/ google scholar
  • Tkachenko, I. N., & ChesnYukova, L. K. (2023). Digital technologies in the sphere of health care as a waY to ensure the qualitY of human capital. Izvestiya of Saratov University. Economics. https://doi.org/10.18500/1994-2540-2023-23-1-121-128 google scholar
  • Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. https://doi.org/10.1038/s41591-018-0300-7 google scholar
  • Vial, A., Stirling, D., Field, M., McKee, D., Ritz, C., & Carolan, M. (2018). The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: A review. Translational Cancer Research, 7, 803-816. https:// doi.org/10.21037/tcr.2018.07.14 google scholar
  • Wang, Y., & Lin, G. (2022). Exploring AI-healthcare innovation: Natural language processing-based patents analYsis for technologY-driven roadmapping. Kybernetes, 52(4), 1173-1189. https://doi.org/10.1108/K-03-2021-0170 google scholar


SHARE




Istanbul University Press aims to contribute to the dissemination of ever growing scientific knowledge through publication of high quality scientific journals and books in accordance with the international publishing standards and ethics. Istanbul University Press follows an open access, non-commercial, scholarly publishing.