- Title
- Addressing the complexities of big data analytics in healthcare : The diabetes screening case
- Creator
- De Silva, Daswin; Burstein, Frada; Jelinek, Herbert; Stranieri, Andrew
- Date
- 2015
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/160771
- Identifier
- vital:12261
- Identifier
-
https://doi.org/10.3127/ajis.v19i0.1183
- Identifier
- ISBN:1449-8618
- Abstract
- The healthcare industry generates a high throughput of medical, clinical and omics data of varying complexity and features. Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards better management of this data for effective and efficient healthcare delivery and quality assured outcomes. Amass of data across all stages, from disease diagnosis to palliative care, is further indication of the opportunities and challenges to effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. Big Data analytics (BDA) presents the potential to advance this industry with reforms in clinical decision-support and translational research. However, adoption of big data analytics has been slow due to complexities posed by the nature of healthcare data. The success of these systems is hard to predict, so further research is needed to provide a robust framework to ensure investment in BDA is justified. In this paper we investigate these complexities from the perspective of updated Information Systems (IS) participation theory. We present a case study on a large diabetes screening project to integrate, converge and derive expedient insights from such an accumulation of data and make recommendations for a successful BDA implementation grounded in a participatory framework and the specificities of big data in healthcare context. © 2015 De Silva, Burstein, Jelinek, Stranieri.
- Publisher
- Deakin University, School of Information Systems
- Relation
- Australasian Journal of Information Systems Vol. 19, no. (2015), p. S99-S115
- Rights
- http://creativecommons.org/licenses/by-nc/4.0/
- Rights
- Copyright:© 2015 De Silva, Burstein, Jelinek, Stranieri. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 Australia License, which permits non-commercial use, distribution, and reproduction in any medium, provided the original author and AJIS are credited.
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0806 Information Systems; 1503 Business and Management; Big data analytics; Business analytics; Clinical decision support; Health informatics; Information fusion; Translational research
- Full Text
- Reviewed
- Hits: 4125
- Visitors: 3964
- Downloads: 193
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Published version | 281 KB | Adobe Acrobat PDF | View Details Download |