Journal:Big data as a driver for clinical decision support systems: A learning health systems perspective
| Full article title | Big data as a driver for clinical decision support systems: A learning health systems perspective |
|---|---|
| Journal | Frontiers in Digital Humanities |
| Author(s) |
Dagliati, Arianna; Tibolloa, Valentina; Sacchi, Lucia; Malovini, Alberto; Limongelli, Ivan; Gabetta, Matteo; Napolitano, Carlo; Mazzanti, Andrea; De Cata, Pasquale; Chovato, Luca; Priori, Sylvia; Bellazzi, Riccardo |
| Author affiliation(s) | Istituti Clinici Scientifici Maugeri, University of Manchester, Università degli Studi di Pavia, Engenome s.r.l., Biomeris s.r.l. |
| Primary contact | Email: riccardo dot bellazzi at unipv dot it |
| Editors | Cavallo, Pierpaolo |
| Year published | 2018 |
| Volume and issue | 5 |
| Page(s) | 8 |
| DOI | 10.3389/fdigh.2018.00008 |
| ISSN | 2297-2668 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | https://www.frontiersin.org/articles/10.3389/fdigh.2018.00008/full |
| Download | https://www.frontiersin.org/articles/10.3389/fdigh.2018.00008/pdf (PDF) |
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Abstract
Big data technologies are nowadays providing health care with powerful instruments to gather and analyze large volumes of heterogeneous data collected for different purposes, including clinical care, administration, and research. This makes possible to design IT infrastructures that favor the implementation of the so-called “Learning Healthcare System Cycle,” where healthcare practice and research are part of a unique and synergistic process. In this paper we highlight how "big-data-enabled” integrated data collections may support clinical decision-making together with biomedical research. Two effective implementations are reported, concerning decision support in diabetes and in inherited arrhythmogenic diseases.
Keywords: big data, learning health care cycle, data warehouses, data integration, data analytics
Introduction
Following a broadly recognized definition, big data is data “whose scale, diversity, and complexity require new architecture, techniques, algorithms, and analytics to manage it and extract value and hidden knowledge from it.”[1] This definition embraces the multifactorial nature of this kind of data and the technological challenges implied. The integration of different sources of information, from primary and secondary care to administrative data, seems a substantial opportunity that big data provides to healthcare.[2][3][4][5][6] Such integration may allow depicting a novel view of patients' care processes and of single patient's behaviors while taking into account the multifaceted aspects of clinical and chronic care.
References
- ↑ Harper, E. (2014). "Can big data transform electronic health records into learning health systems?". Studies in Health Technology and Informatics 201: 470–5. PMID 24943583.
- ↑ Murdoch, T.B.; Detsky, A.S. (2013). "The inevitable application of big data to health care". JAMA 309 (13): 1351–2. doi:10.1001/jama.2013.393. PMID 23549579.
- ↑ Etheredge, L.M. (2014). "Rapid learning: A breakthrough agenda". Health Affairs 33 (7): 1155-62. doi:10.1377/hlthaff.2014.0043. PMID 25006141.
- ↑ Halamka, J.D. (2014). "Early experiences with big data at an academic medical center". Health Affairs 33 (7): 1132-8. doi:10.1377/hlthaff.2014.0031. PMID 25006138.
- ↑ Krumholz, H.M. (2014). "Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system". Health Affairs 33 (7): 1163-70. doi:10.1377/hlthaff.2014.0053. PMC PMC5459394. PMID 25006142. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5459394.
- ↑ Zillner, S.; Lasierra, N.; Faix, W.; Neururer, S. (2014). "User needs and requirements analysis for big data healthcare applications". Studies in Health Technology and Informatics 205: 657–61. PMID 25160268.
Notes
This presentation is faithful to the original, with only a few minor changes to grammar, spelling, and presentation, including the addition of PMCID and DOI when they were missing from the original reference.
