THE ROLE OF BLOCK CHAIN TECHNOLOGY IN SECURING ELECTRONIC HEALTH RECORDS: OPPORTUNITIES AND CHALLENGES

Main Article Content

Muhammad Furqan Kashif
Rabia Ali
Dr Syeda Mahlaqa Hina
Jamroz Khan
Giuseppe Giorgianni
Rahim Iftikhar

Keywords

Wearable technology, assistive technology, immunological, metabolic, pharmacological

Abstract

Background: Medical devices encompass a diverse array of innovations aimed at patient rehabilitation, disease diagnosis, treatment, and prevention without relying on metabolic, immunological, or pharmacological means.


Objective: This review aims to explore notable advancements in medical device development, focusing on wearable technology, assistive technologies such as exoskeletons and communication software for individuals with limited mobility, medical training applications, artificial intelligence (AI) in medical imaging diagnosis, and virtual reality (VR) for pain management.


Methods: A comprehensive search of the literature was conducted to identify key developments in medical device technology. Relevant studies, articles, and reports were reviewed to provide insights into the current landscape of medical device innovation.


Results: The review highlights several significant advancements in medical device development. Wearable technologies offer continuous monitoring and feedback for patients, enabling personalized healthcare interventions. Assistive technologies, such as exoskeletons and communication software, empower individuals with disabilities to enhance their mobility and communication capabilities. Medical training applications facilitate simulation-based learning for healthcare professionals, improving clinical skills and patient outcomes. AI applications in medical imaging aid in accurate diagnosis and treatment planning, enhancing clinical decision-making processes. Virtual reality devices offer promising avenues for pain management, providing immersive experiences that distract patients from discomfort and improve overall well-being.


Conclusion: The rapid evolution of medical device technology continues to drive innovations in patient care, rehabilitation, and disease management. Future research and development efforts should focus on harnessing the potential of these advancements to improve healthcare outcomes and enhance the quality of life for patients worldwide.

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References

1. Abdel-Basset, M., Manogaran, G., Gamal, A., & Smarandache, F. (2019). A group decision making framework based on neutrosophic TOPSIS approach for smart medical device selection. Journal of medical systems, 43, 1-13.
2. Angrick, M., Luo, S., Rabbani, Q., Candrea, D. N., Shah, S., Milsap, G. W., Anderson, W. S., Gordon, C. R., Rosenblatt, K. R., & Clawson, L. (2023). Online speech synthesis using a chronically implanted brain-computer interface in an individual with ALS. medRxiv, 2023.2006. 2030.23291352.
3. Awal, W., Dissabandara, L., Khan, Z., Jeyakumar, A., Habib, M., & Byfield, B. (2021). Effect of smartphone laparoscopy simulator on laparoscopic performance in medical students. Journal of Surgical Research, 262, 159-164.
4. Bernard, M., Jubeli, E., Pungente, M. D., & Yagoubi, N. (2018). Biocompatibility of polymer-based biomaterials and medical devices–regulations, in vitro screening and risk-management. Biomaterials Science, 6(8), 2025-2053.
5. Bonsón, E., & Bednárová, M. (2019). Blockchain and its implications for accounting and auditing. Meditari Accountancy Research.
6. Coventry, L., & Branley, D. (2018). Cybersecurity in healthcare: A narrative review of trends, threats and ways forward. Maturitas, 113, 48-52.
7. Dai, Y., Xu, D., Maharjan, S., Chen, Z., He, Q., & Zhang, Y. (2019). Blockchain and deep reinforcement learning empowered intelligent 5G beyond. IEEE network, 33(3), 10-17.
8. Défossez, A., Caucheteux, C., Rapin, J., Kabeli, O., & King, J.-R. (2022). Decoding speech from non-invasive brain recordings. arXiv preprint arXiv:2208.12266.
9. Desmond, D., Layton, N., Bentley, J., Boot, F. H., Borg, J., Dhungana, B. M., Gallagher, P., Gitlow, L., Gowran, R. J., & Groce, N. (2018). Assistive technology and people: a position paper from the first global research, innovation and education on assistive technology (GREAT) summit. Disability and Rehabilitation: Assistive Technology, 13(5), 437-444.
10. Gauthier, N., Johnson, C., Stadnick, E., Keenan, M., Wood, T., Sostok, M., & Humphrey-Murto, S. (2019). Does cardiac physical exam teaching using a cardiac simulator improve medical students’ diagnostic skills? Cureus, 11(5).
11. Gilbert, M., Zhang, X., & Yin, G. (2016). Modeling and design on control system of lower limb rehabilitation exoskeleton robot. 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI),
12. Griggs, K. N., Ossipova, O., Kohlios, C. P., Baccarini, A. N., Howson, E. A., & Hayajneh, T. (2018). Healthcare blockchain system using smart contracts for secure automated remote patient monitoring. Journal of medical systems, 42, 1-7.
13. Jones, J. S., Hunt, S. J., Carlson, S. A., & Seamon, J. P. (1997). Assessing bedside cardiologic examination skills using “Harvey,” a cardiology patient simulator. Academic Emergency Medicine, 4(10), 980-985.
14. Khezr, S., Moniruzzaman, M., Yassine, A., & Benlamri, R. (2019). Blockchain technology in healthcare: A comprehensive review and directions for future research. Applied sciences, 9(9), 1736.
15. Li, M., Ganni, S., Ponten, J., Albayrak, A., Rutkowski, A.-F., & Jakimowicz, J. (2020). Analysing usability and presence of a virtual reality operating room (VOR) simulator during laparoscopic surgery training. 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR),
16. Liu, Y., Zhang, L., Yang, Y., Zhou, L., Ren, L., Wang, F., Liu, R., Pang, Z., & Deen, M. J. (2019). A novel cloud-based framework for the elderly healthcare services using digital twin. IEEE Access, 7, 49088-49101.
17. Meel, R. (2020). Incorporating multimodality imaging in training in the South African context. SA Heart, 17(3), 381-383.
18. Michael, M., Abboudi, H., Ker, J., Khan, M. S., Dasgupta, P., & Ahmed, K. (2014). Performance of technology-driven simulators for medical students—a systematic review. Journal of Surgical Research, 192(2), 531-543.
19. Norman, G., Dore, K., & Grierson, L. (2012). The minimal relationship between simulation fidelity and transfer of learning. Medical education, 46(7), 636-647.
20. Pamungkas, D. S., Caesarendra, W., Soebakti, H., Analia, R., & Susanto, S. (2019). Overview: Types of lower limb exoskeletons. Electronics, 8(11), 1283.
21. Schreuder, H. W., van Dongen, K. W., Roeleveld, S. J., Schijven, M. P., & Broeders, I. A. (2009). Face and construct validity of virtual reality simulation of laparoscopic gynecologic surgery. American journal of obstetrics and gynecology, 200(5), 540. e541-540. e548.
22. Sengupta, A., Todd, A. J., Leslie, S. J., Bagnall, A., Boon, N. A., Fox, K. A., & Denvir, M. A. (2007). Peer-led medical student tutorials using the cardiac simulator'Harvey'. MEDICAL EDUCATION-OXFORD-, 41(2), 219.
23. Sim, I. (2019). Mobile devices and health. New England Journal of Medicine, 381(10), 956-968.
24. Starczyńska, M., & Sacharuk, A. (2020). Rehabilitation exoskeleton-the perspective of improving the quality of life for people with disabilities. Archives of Physiotherapy & Global Researches, 24(2).
25. Tu, Y., Zhu, A., Song, J., Shen, H., Shen, Z., Zhang, X., & Cao, G. (2020). An adaptive sliding mode variable admittance control method for lower limb rehabilitation exoskeleton robot. Applied sciences, 10(7), 2536.
26. Yang, K., Jiang, Q. F., Wang, X. L., Chen, Y. W., & Ma, X. Y. (2018). Structural design and modal analysis of exoskeleton robot for rehabilitation of lower limb. Journal of Physics: Conference Series,
27. Zhang, Y., Liu, Y., Sui, X., Zheng, T., Bie, D., Wang, Y., Zhao, J., & Zhu, Y. (2019). A mechatronics-embedded pneumatic soft modular robot powered via single air tube. Applied sciences, 9(11), 2260.
28. Zhou, J., Yang, S., & Xue, Q. (2021). Lower limb rehabilitation exoskeleton robot: A review. Advances in Mechanical Engineering, 13(4), 16878140211011862.

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