Journal:Big data as a driver for clinical decision support systems: A learning health systems perspective

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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]

The availability of new data sources is thus leading to the development of a novel model of healthcare, able to fully exploit the potentials of data-driven decision making. The main consequence is that big data will not only be an important enabler for research, but also for the clinical and organizational decision making. We will discuss this perspective within the context of the so-called “Learning Healthcare System Cycle” (LHSC).[15][16][17][18][19][20] We demonstrate the importance of leveraging on LHSC solutions in developing next-generation clinical and organizational decision systems, as follows: (i) we describe a possible formalization for the use of the learning healthcare aystem, proposing a conceptual solution based on state-of-the-art technologies for data production; and (ii) we present two systems implanted upon the LHSC as proof of concept of the validity of the formalized concepts in different clinical scenarios.

Big data and the learning healthcare system cycle

Novel and essential directions in the use of big data for healthcare have been recently redefined within the medical informatics community.[21] Specifically, the well-known conceptual approach of the “data, information, and knowledge” continuum has been reconsidered as the LHSC, where healthcare practice and research should be part of a unique and synergic process. The first main novelty of this approach is to emphasize that clinical practice and research are complementary agents in the generation of data and knowledge.

The role of informatics is to provide the right tools to turn data into information, and information into knowledge, helping to understand deep data relations by retrieving and extracting underlying patterns. Moreover, informatics is crucial for the deployment of the acquired knowledge to support patient care and, ultimately, to guide individual behavior.

Our prospective is that the use of big data in medical informatics will be equally important in the different phases of the LHSC: from research to data driven decision-making. LSHC is indeed based on these two complementary actions, the first one focused on the exploitation of medical generated data for research purposes (care informs research), and the second one focused on the development of novel systems leveraging big data to guide clinical decision making (research informs care).

Care informs research: Research

In clinical practice, data is mostly collected from electronic health records (EHR), through which recent widespread adoption has made available a unique source of clinical information for research. The EHR can be used to extract and interpret clinical data, to automatically support clinical research, and improve quality of care. Specifically, EHR-based phenotyping uses data captured in the delivery of care to identify individuals or cohorts with conditions or events relevant to clinical studies[22][23][24] Some distinguishing aspects of the current literature include: (i) considering the temporal nature of the data—and explicitly including not only clinical information from EHR but also process information from administrative databases—recent methods allow, for example, the extraction of care-flows that highlight frequent patients' trajectories in terms of disease evolution as well as in terms of patterns of care; (ii) allow for computing patients' similarity by resorting to advanced “multimodal” data fusion strategies, including deep learning and tensor factorization; and (iii) fully apply natural language processing pipelines as enablers to integrate in the analytical process data and knowledge hidden in textual reports.

Research informs care: Data-driven decision making

Clinical decision support systems (CDSS) have been traditionally defined as software designed to aid clinical decision making by adapting computerized clinical guidelines and protocols to individual patient characteristics.[25] While it is recognized that developing and deploying CDSSs can be very beneficial in contexts that require complex decision-making, such as chronic disease management, their use in routine clinical practice is currently still limited.[26] Possible causes are related to poor user interfaces, lacking integration with EHRs, and limited analytics capabilities that do not allow data-driven reasoning.

We believe that, in order to provide successful decision support, CDSSs should comply with basic requirements, including: (i) rich contents in terms of knowledge, references, and data evidences; (ii) the capability of processing huge amounts of data with fast response times; and (iii) implementations that are intuitive, appealing, and able to catch users' attention while not delaying clinical actions. These features translate into the fundamental CDSS components: data and knowledge repositories, inference engines, and user interfaces. It is worth noticing that IT infrastructures designed to support research can also be used to assist clinical decision-making. An interesting paradigm is represented by the so-called “sidecar” approach, where the same data warehouse is used to analyze patients' cohorts at a population level and as an instrument to enable “case-based” reasoning in front of a complex clinical case by extracting similar patients and potential treatments.

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. 
  15. ↑ Grossman, C.; Goolsby, W.A.; Olsen, L; McGinnis, J.M. (2011). Engineering a Learning Healthcare System - A Look at the Future: Workshop Summary. National Academies Press. ISBN 09780309120647. https://www.nap.edu/catalog/12213/engineering-a-learning-healthcare-system-a-look-at-the-future. 
  16. ↑ Skiba, D.J. (2011). "Informatics and the learning healthcare system". Nursing Education Perspectives 32 (5): 334-6. PMID 22029247. 
  17. ↑ Deeny, S.R.; Steventon, A. (2015). "Making sense of the shadows: Priorities for creating a learning healthcare system based on routinely collected data". BMJ Quality and Safety 24 (8): 505–15. doi:10.1136/bmjqs-2015-004278. PMC PMC4515981. PMID 26065466. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4515981. 
  18. ↑ Budrionis, A.; Bellika, J.G. (2016). "The Learning Healthcare System: Where are we now? A systematic review". Journal of Biomedical Informatics 64: 87-92. doi:10.1016/j.jbi.2016.09.018. PMID 27693565. 
  19. ↑ Harle, C.A.; Lipori, G.; Hurley, R.W. (2016). "Collecting, Integrating, and Disseminating Patient-Reported Outcomes for Research in a Learning Healthcare System". EGEMS 4 (1): 1240. doi:10.13063/2327-9214.1240. PMC PMC4975567. PMID 27563683. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4975567. 
  20. ↑ Lee, C.H.; Yoon, H.J. (2017). "Medical big data: Promise and challenges". Kidney Research and Clinical Practice 36 (1): 3–11. doi:10.23876/j.krcp.2017.36.1.3. PMC PMC5331970. PMID 28392994. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5331970. 
  21. ↑ Tenenbaum, J.D.; Avillach, P.; Benham-Hutchins, M. et al. (2016). "An informatics research agenda to support precision medicine: Seven key areas". JAMIA 23 (4): 791–5. doi:10.1093/jamia/ocv213. PMC PMC4926738. PMID 27107452. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4926738. 
  22. ↑ Hripcsak, G.; Albers, D.J. (2013). "Next-generation phenotyping of electronic health records". JAMIA 20 (1): 117–21. doi:10.1136/amiajnl-2012-001145. PMC PMC3555337. PMID 22955496. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3555337. 
  23. ↑ Newton, K.M.; Peissig, P.L.; Kho, A.N. et al. (2013). "Validation of electronic medical record-based phenotyping algorithms: Results and lessons learned from the eMERGE network". JAMIA 20 (e1): e147–54. doi:10.1136/amiajnl-2012-000896. PMC PMC3715338. PMID 23531748. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3715338. 
  24. ↑ Richesson, R.L.; Hammond, W.E.; Nahm, M. et al. (2013). "Electronic health records based phenotyping in next-generation clinical trials: A perspective from the NIH Health Care Systems Collaboratory". JAMIA 20 (e2): e226–31. doi:10.1136/amiajnl-2013-001926. PMC PMC3861929. PMID 23956018. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3861929. 
  25. ↑ Sim, I.; Gorman, P.; Greenes, R.A. et al. (2001). "Clinical decision support systems for the practice of evidence-based medicine". JAMIA 8 (6): 527–34. PMC PMC130063. PMID 11687560. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC130063. 
  26. ↑ Belard, A.; Buchman, T.; Forsberg, J. et al. (2017). "Precision diagnosis: A view of the clinical decision support systems (CDSS) landscape through the lens of critical care". Journal of Clinical Monitoring and Computing 31 (2): 261–71. doi:10.1007/s10877-016-9849-1. PMID 26902081. 

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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