The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
The use of Big Data Analytics in healthcare
Open accessAbstract
The introduction of Big Data Analytics (BDA) in healthcare will allow to use new technologies both in treatment of patients and health management. The paper aims at analyzing the possibilities of using Big Data Analytics in healthcare. The research is based on a critical analysis of the literature, as well as the presentation of selected results of direct research on the use of Big Data Analytics in medical facilities. The direct research was carried out based on research questionnaire and conducted on a sample of 217 medical facilities in Poland. Literature studies have shown that the use of Big Data Analytics can bring many benefits to medical facilities, while direct research has shown that medical facilities in Poland are moving towards data-based healthcare because they use structured and unstructured data, reach for analytics in the administrative, business and clinical area. The research positively confirmed that medical facilities are working on both structural data and unstructured data. The following kinds and sources of data can be distinguished: from databases, transaction data, unstructured content of emails and documents, data from devices and sensors. However, the use of data from social media is lower as in their activity they reach for analytics, not only in the administrative and business but also in the clinical area. It clearly shows that the decisions made in medical facilities are highly data-driven. The results of the study confirm what has been analyzed in the literature that medical facilities are moving towards data-based healthcare, together with its benefits.
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Cites
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Cited by
Sohail, S. S., Farhat, F., Himeur, Y., Nadeem, M., Madsen, D. Ø., Singh, Y., Atalla, S., & Mansoor, W. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101675 · Cited by 192
“For users in that particular domain, this may result in answers that are more precise and tailored (Batko and Slezak, 2022; Himeur et al., 2022a).”
Ayles, J., Lima, M., & Gupta, N. (2024) · International Journal of Population Data Science · doi:10.23889/ijpds.v9i1.2370 · Cited by 2
Santana-Velásquez, A., & Salazar-Sánchez, M. B. (2026) · Computers · doi:10.3390/computers15080506
Metadata
- Authors
- Batko, K., & Ślęzak, A.
- Venue
- Journal of Big Data
- Published
- 2022-01-06
- Volume
- 9
- Issue
- 1
- Pages
- 3-3
- ISSN
- 2196-1115 (ISSN-L)
- OpenAlex
- W4206091887
- Cited by
- 593
- References
- 64
- FWCI
- 89.26
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.