A conceptual model of the data ecosystem formation in the health 4.0
Digitalization of health and medicine has become a global trend in recent years. The development of this trend is manifested in improving the quality of medical care, increasing access to medical services, and ensuring more effective health decisions. The global health trend is characterized by the transition from e-health to digital health, which is closely related to the formation and development of Industry 4.0. The projection of innovative approaches of Industry 4.0 into the health sector opens up great opportunities for creating a new healthcare paradigm (Health 4.0) by integrating modern technologies, analytical tools and smart medical devices.
This article highlights Industry 4.0 technologies, the integration of which into healthcare has led to the generation of a huge amount of various data. Data collected through new technologies on the Health 4.0 platform, their types, structure, sources and capabilities are examined. It is shown that for the collection, transmission, storage, processing of heterogeneous data of a large volume, the introduction of new approaches and methods is required. In the environment of technological transformation, the evolution of the healthcare system at the data level and the conceptual model of the chain of formation of the Healthcare 4.0 ecosystem are proposed. The content of the components included in the Healthcare 4.0 data ecosystem is determined. Some problems arising in the formation of the data ecosystem on the Healthcare 4.0 platform and the processing of large health data are studied
Mammadova, M., Jabrayilova, Z. (2019). Electronic medicine: formation and scientific-theoretical problems. Baku: “Information Technologies” publishing house, 319. Available at: https://ict.az/uploads/files/E-medicine-monograph-IIT-ANAS.pdf
Firouzi, F., Rahmani, A. M., Mankodiya, K., Badaroglu, M., Merrett, G. V., Wong, P., Farahani, B. (2018). Internet-of-Things and big data for smarter healthcare: From device to architecture, applications and analytics. Future Generation Computer Systems, 78, 583–586. doi: https://doi.org/10.1016/j.future.2017.09.016
Tortorella, G. L., Fogliatto, F. S., Kurnia, S., Thürer, M., Capurro, D. (2022). Healthcare 4.0 digital applications: An empirical study on measures, bundles and patient-centered performance. Technological Forecasting and Social Change, 181, 121780. doi: https://doi.org/10.1016/j.techfore.2022.121780
Vassolo, R. S., Mac Cawley, A. F., Tortorella, G. L., Fogliatto, F. S., Tlapa, D., Narayanamurthy, G. (2021). Hospital Investment Decisions in Healthcare 4.0 Technologies: Scoping Review and Framework for Exploring Challenges, Trends, and Research Directions. Journal of Medical Internet Research, 23 (8), e27571. doi: https://doi.org/10.2196/27571
Haleem, A., Javaid, M., Pratap Singh, R., Suman, R. (2022). Medical 4.0 technologies for healthcare: Features, capabilities, and applications. Internet of Things and Cyber-Physical Systems, 2, 12–30. doi: https://doi.org/10.1016/j.iotcps.2022.04.001
Kumar, A., Krishnamurthi, R., Nayyar, A., Sharma, K., Grover, V., Hossain, E. (2020). A Novel Smart Healthcare Design, Simulation, and Implementation Using Healthcare 4.0 Processes. IEEE Access, 8, 118433–118471. doi: https://doi.org/10.1109/access.2020.3004790
Al-Jaroodi, J., Mohamed, N., Abukhousa, E. (2020). Health 4.0: On the Way to Realizing the Healthcare of the Future. IEEE Access, 8, 211189–211210. doi: https://doi.org/10.1109/access.2020.3038858
Mammadova, M., Jabrayilova, Z. (2022). Synthesis of decision making in a distributed intelligent personnel health management system on offshore oil platform. EUREKA: Physics and Engineering, 4, 179–192. doi: https://doi.org/10.21303/2461-4262.2022.002520
Tayefi, M., Ngo, P., Chomutare, T., Dalianis, H., Salvi, E., Budrionis, A., Godtliebsen, F. (2021). Challenges and opportunities beyond structured data in analysis of electronic health records. WIREs Computational Statistics, 13 (6). doi: https://doi.org/10.1002/wics.1549
How to Navigate Big Data in Healthcare (2014). Available at: https://www.cio.com/article/250848/how-to-navigate-big-data-in-healthcare.html
Dash, S., Shakyawar, S. K., Sharma, M., Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6 (1). doi: https://doi.org/10.1186/s40537-019-0217-0
Radouan Ait Mouha, R. A. (2021). Internet of Things (IoT). Journal of Data Analysis and Information Processing, 09 (02), 77–101. doi: https://doi.org/10.4236/jdaip.2021.92006
Javaid, M., Haleem, A., Khan, I. (2020). Holography applications toward medical field: An overview. Indian Journal of Radiology and Imaging, 30 (3), 354. doi: https://doi.org/10.4103/ijri.ijri_39_20
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