Difference between revisions of "Journal:Big data as a driver for clinical decision support systems: A learning health systems perspective"

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==Introduction==
==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.”<ref name="HarperCanBig14">{{cite journal |title=Can big data transform electronic health records into learning health systems? |journal=Studies in Health Technology and Informatics |author=Harper, E. |volume=201 |pages=470–5 |year=2014 |pmid=24943583}}</ref> 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.<ref name="MurdochTheInev13">{{cite journal |title=The inevitable application of big data to health care |journal=JAMA |author=Murdoch, T.B.; Detsky, A.S. |volume=309 |issue=13 |pages=1351–2 |year=2013 |doi=10.1001/jama.2013.393 |pmid=23549579}}</ref><ref name="EtheredgeRapid14">{{cite journal |title=Rapid learning: A breakthrough agenda |journal=Health Affairs |author=Etheredge, L.M. |volume=33 |issue=7 |pages=1155-62 |year=2014 |doi=10.1377/hlthaff.2014.0043 |pmid=25006141}}</ref><ref name="HalamkaEarly14">{{cite journal |title=Early experiences with big data at an academic medical center |journal=Health Affairs |author=Halamka, J.D. |volume=33 |issue=7 |pages=1132-8 |year=2014 |doi=10.1377/hlthaff.2014.0031 |pmid=25006138}}</ref><ref name="KrumholzBigData14">{{cite journal |title=Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system |journal=Health Affairs |author=Krumholz, H.M. |volume=33 |issue=7 |pages=1163-70 |year=2014 |doi=10.1377/hlthaff.2014.0053 |pmid=25006142 |pmc=PMC5459394}}</ref><ref name="ZillnerUser14">{{cite journal |title=User needs and requirements analysis for big data healthcare applications |journal=Studies in Health Technology and Informatics |author=Zillner, S.; Lasierra, N.; Faix, W.; Neururer, S. |volume=205 |pages=657–61 |year=2014 |pmid=25160268}}</ref> 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.
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.”<ref name="HarperCanBig14">{{cite journal |title=Can big data transform electronic health records into learning health systems? |journal=Studies in Health Technology and Informatics |author=Harper, E. |volume=201 |pages=470–5 |year=2014 |pmid=24943583}}</ref> 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.<ref name="MurdochTheInev13">{{cite journal |title=The inevitable application of big data to health care |journal=JAMA |author=Murdoch, T.B.; Detsky, A.S. |volume=309 |issue=13 |pages=1351–2 |year=2013 |doi=10.1001/jama.2013.393 |pmid=23549579}}</ref><ref name="EtheredgeRapid14">{{cite journal |title=Rapid learning: A breakthrough agenda |journal=Health Affairs |author=Etheredge, L.M. |volume=33 |issue=7 |pages=1155-62 |year=2014 |doi=10.1377/hlthaff.2014.0043 |pmid=25006141}}</ref><ref name="HalamkaEarly14">{{cite journal |title=Early experiences with big data at an academic medical center |journal=Health Affairs |author=Halamka, J.D. |volume=33 |issue=7 |pages=1132-8 |year=2014 |doi=10.1377/hlthaff.2014.0031 |pmid=25006138}}</ref><ref name="KrumholzBigData14">{{cite journal |title=Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system |journal=Health Affairs |author=Krumholz, H.M. |volume=33 |issue=7 |pages=1163-70 |year=2014 |doi=10.1377/hlthaff.2014.0053 |pmid=25006142 |pmc=PMC5459394}}</ref><ref name="ZillnerUser14">{{cite journal |title=User needs and requirements analysis for big data healthcare applications |journal=Studies in Health Technology and Informatics |author=Zillner, S.; Lasierra, N.; Faix, W.; Neururer, S. |volume=205 |pages=657–61 |year=2014 |pmid=25160268}}</ref> 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.
The interest in the collection of large and heterogeneous healthcare data sources finds a distinctive application in the definition of novel data-driven [[Clinical decision support system|decision support systems]].<ref name="KaltoftEnhancing14">{{cite journal |title=Enhancing informatics competency under uncertainty at the point of decision: A knowing about knowing vision |journal=Studies in Health Technology and Informatics |author=Kaltoft, M.K.; Nielsen, J.B.; Salkeld, G.; Dowie, J. |volume=205 |pages=975–9 |year=2014 |pmid=25160333}}</ref><ref name="KohnIBM14">{{cite journal |title=IBM's Health Analytics and Clinical Decision Support |journal=Yearbook of Medical Informatics |author=Kohn, M.S.; Sun, J.; Knoop, S. et al. |volume=9 |pages=154–62 |year=2014 |doi=10.15265/IY-2014-0002 |pmid=25123736 |pmc=PMC4287097}}</ref><ref name="LupseSupporting14">{{cite journal |title=Supporting diagnosis and treatment in medical care based on Big Data processing |journal=Studies in Health Technology and Informatics |author=Lupse, O.S.; Crisan-Vida, M.; Stoicu-Tivadar, L.; Bernard, E. |volume=197 |pages=65–9 |year=2014 |pmid=24743079}}</ref> Several authors<ref name="KohnIBM14" /><ref name="LupseSupporting14" /><ref name="ZhangApplication16">{{cite journal |title=Application and Exploration of Big Data Mining in Clinical Medicine |journal=Chinese Medical Journal |author=Zhang, Y.; Guo, S.L.; Han, L.N.; Li, T.L. |volume=129 |issue=6 |pages=731-8 |year=2016 |doi=10.4103/0366-6999.178019 |pmid=26960378 |pmc=PMC4804421}}</ref> define two main fields where researchers should address their efforts to produce valuable results in this area: (i) the secondary use of data to create new evidence and glean important insights to make better clinical decision or to reshape health care organizational components; and (ii) the detection of novel correlations from asynchronous events to allow clinicians to promptly identify potential complications, timely adjust treatments, or help analyze similar manifestations in clinical diagnoses. To pledge better renewed decision making, and consequent successful clinical outcomes, big-data-enabled health care systems should effectively integrate advanced computational tools, including novel similarity measures for patients' stratification, and predictive analytics for risk assessment and selection of therapeutic interventions.<ref name="MoghimiApplying13">{{cite journal |title=Applying predictive analytics to develop an intelligent risk detection application for healthcare contexts |journal=Studies in Health Technology and Informatics |author=Moghimi, F.H.; Cheung, M.; Wickramasinghe, N. |volume=192 |pages=926 |year=2013 |pmid=23920700}}</ref><ref name="EarleyBigData14">{{cite journal |title=Big Data and Predictive Analytics: What's New? |journal=IT Professional |author=Earley, S. |volume=16 |issue=1 |pages=13–15 |year=2014 |doi=10.1109/MITP.2014.3}}</ref><ref name="ChenHetero14">{{cite journal |title=Heterogeneous postsurgical data analytics for predictive modeling of mortality risks in intensive care units |journal=Proceedings from the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society |author=Chen, Y.; Yang, H. |volume=2014 |pages=4310–4314 |year=2014 |doi=10.1109/EMBC.2014.6944578}}</ref><ref name="WangTime15">{{cite journal |title=Time-dependent variation of pathways and networks in a 24-hour window after cerebral ischemia-reperfusion injury |journal=BMC Systems Biology |author=Wang, L.Y.; Liu, J.; Li, Y. et al. |volume=9 |pages=11 |year=2015 |doi=10.1186/s12918-015-0152-4 |pmid=25884595 |pmc=PMC4355473}}</ref>




Revision as of 20:47, 1 May 2018

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.

The interest in the collection of large and heterogeneous healthcare data sources finds a distinctive application in the definition of novel data-driven decision support systems.[7][8][9] Several authors[8][9][10] define two main fields where researchers should address their efforts to produce valuable results in this area: (i) the secondary use of data to create new evidence and glean important insights to make better clinical decision or to reshape health care organizational components; and (ii) the detection of novel correlations from asynchronous events to allow clinicians to promptly identify potential complications, timely adjust treatments, or help analyze similar manifestations in clinical diagnoses. To pledge better renewed decision making, and consequent successful clinical outcomes, big-data-enabled health care systems should effectively integrate advanced computational tools, including novel similarity measures for patients' stratification, and predictive analytics for risk assessment and selection of therapeutic interventions.[11][12][13][14]


References

  1. ↑ 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. 
  2. ↑ 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. 
  3. ↑ Etheredge, L.M. (2014). "Rapid learning: A breakthrough agenda". Health Affairs 33 (7): 1155-62. doi:10.1377/hlthaff.2014.0043. PMID 25006141. 
  4. ↑ 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. 
  5. ↑ 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. 
  6. ↑ 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. 
  7. ↑ Kaltoft, M.K.; Nielsen, J.B.; Salkeld, G.; Dowie, J. (2014). "Enhancing informatics competency under uncertainty at the point of decision: A knowing about knowing vision". Studies in Health Technology and Informatics 205: 975–9. PMID 25160333. 
  8. ↑ 8.0 8.1 Kohn, M.S.; Sun, J.; Knoop, S. et al. (2014). "IBM's Health Analytics and Clinical Decision Support". Yearbook of Medical Informatics 9: 154–62. doi:10.15265/IY-2014-0002. PMC PMC4287097. PMID 25123736. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4287097. 
  9. ↑ 9.0 9.1 Lupse, O.S.; Crisan-Vida, M.; Stoicu-Tivadar, L.; Bernard, E. (2014). "Supporting diagnosis and treatment in medical care based on Big Data processing". Studies in Health Technology and Informatics 197: 65–9. PMID 24743079. 
  10. ↑ Zhang, Y.; Guo, S.L.; Han, L.N.; Li, T.L. (2016). "Application and Exploration of Big Data Mining in Clinical Medicine". Chinese Medical Journal 129 (6): 731-8. doi:10.4103/0366-6999.178019. PMC PMC4804421. PMID 26960378. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4804421. 
  11. ↑ Moghimi, F.H.; Cheung, M.; Wickramasinghe, N. (2013). "Applying predictive analytics to develop an intelligent risk detection application for healthcare contexts". Studies in Health Technology and Informatics 192: 926. PMID 23920700. 
  12. ↑ Earley, S. (2014). "Big Data and Predictive Analytics: What's New?". IT Professional 16 (1): 13–15. doi:10.1109/MITP.2014.3. 
  13. ↑ Chen, Y.; Yang, H. (2014). "Heterogeneous postsurgical data analytics for predictive modeling of mortality risks in intensive care units". Proceedings from the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2014: 4310–4314. doi:10.1109/EMBC.2014.6944578. 
  14. ↑ Wang, L.Y.; Liu, J.; Li, Y. et al. (2015). "Time-dependent variation of pathways and networks in a 24-hour window after cerebral ischemia-reperfusion injury". BMC Systems Biology 9: 11. doi:10.1186/s12918-015-0152-4. PMC PMC4355473. PMID 25884595. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4355473. 

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.

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