Big data analytics in health sector: Theoretical framework,techniques and prospects |
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| Institution: | 1. School of Economics, Business Administration & Legal Studies, International Hellenic University, 14th km Thessaloniki-N. Moudania, Thessaloniki, 57001, Greece;2. Opus College of Business, University of St. Thomas Minneapolis Campus, 1000 LaSalle Avenue, Schulze Hall 435, Minneapolis, MN 55403, USA;1. Indian Institute of Management Rohtak, Haryana, India;2. O.P. Jindal Global University, Sonipat, Haryana, India;1. College of Science, George Mason University (GMU), Fairfax, VA, United States;2. MedStar Georgetown University Hospital (MGUH), Washington, DC, United States;1. Newcastle University Business School, Newcastle University, United Kingdom;2. Rowan University, 201 Mullica Hill Road, Glassboro, NJ 08028, United States;3. Department of Management Systems, University of Waikato, New Zealand;4. Auburn University, 405 W Magnolia Ave, Auburn, AL 36849, United States |
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| Abstract: | Clinicians, healthcare providers-suppliers, policy makers and patients are experiencing exciting opportunities in light of new information deriving from the analysis of big data sets, a capability that has emerged in the last decades. Due to the rapid increase of publications in the healthcare industry, we have conducted a structured review regarding healthcare big data analytics. With reference to the resource-based view theory we focus on how big data resources are utilised to create organization values/capabilities, and through content analysis of the selected publications we discuss: the classification of big data types related to healthcare, the associate analysis techniques, the created value for stakeholders, the platforms and tools for handling big health data and future aspects in the field. We present a number of pragmatic examples to show how the advances in healthcare were made possible. We believe that the findings of this review are stimulating and provide valuable information to practitioners, policy makers and researchers while presenting them with certain paths for future research. |
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| Keywords: | Big data analytics Health-Medicine Decision-making Machine learning Operations research (OR) techniques |
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