Hidden big data analytics issues in the healthcare industry
The goal of the study was to identify big data analysis issues that can impact empirical research in the healthcare industry. To accomplish that the author analyzed big data related keywords from a literature review of peer reviewed journal articles published since 2011. Topics, methods and techniqu...
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| Published in | Health informatics journal Vol. 26; no. 2; pp. 981 - 998 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
London, England
SAGE Publications
01.06.2020
SAGE PUBLICATIONS, INC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1460-4582 1741-2811 1741-2811 |
| DOI | 10.1177/1460458219854603 |
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| Abstract | The goal of the study was to identify big data analysis issues that can impact empirical research in the healthcare industry. To accomplish that the author analyzed big data related keywords from a literature review of peer reviewed journal articles published since 2011. Topics, methods and techniques were summarized along with strengths and weaknesses. A panel of subject matter experts was interviewed to validate the intermediate results and synthesize the key problems that would likely impact researchers conducting quantitative big data analysis in healthcare studies. The systems thinking action research method was applied to identify and describe the hidden issues. The findings were similar to the extant literature but three hidden fatal issues were detected. Methodical and statistical control solutions were proposed to overcome the three fatal healthcare big data analysis issues. |
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| AbstractList | The goal of the study was to identify big data analysis issues that can impact empirical research in the healthcare industry. To accomplish that the author analyzed big data related keywords from a literature review of peer reviewed journal articles published since 2011. Topics, methods and techniques were summarized along with strengths and weaknesses. A panel of subject matter experts was interviewed to validate the intermediate results and synthesize the key problems that would likely impact researchers conducting quantitative big data analysis in healthcare studies. The systems thinking action research method was applied to identify and describe the hidden issues. The findings were similar to the extant literature but three hidden fatal issues were detected. Methodical and statistical control solutions were proposed to overcome the three fatal healthcare big data analysis issues. The goal of the study was to identify big data analysis issues that can impact empirical research in the healthcare industry. To accomplish that the author analyzed big data related keywords from a literature review of peer reviewed journal articles published since 2011. Topics, methods and techniques were summarized along with strengths and weaknesses. A panel of subject matter experts was interviewed to validate the intermediate results and synthesize the key problems that would likely impact researchers conducting quantitative big data analysis in healthcare studies. The systems thinking action research method was applied to identify and describe the hidden issues. The findings were similar to the extant literature but three hidden fatal issues were detected. Methodical and statistical control solutions were proposed to overcome the three fatal healthcare big data analysis issues.The goal of the study was to identify big data analysis issues that can impact empirical research in the healthcare industry. To accomplish that the author analyzed big data related keywords from a literature review of peer reviewed journal articles published since 2011. Topics, methods and techniques were summarized along with strengths and weaknesses. A panel of subject matter experts was interviewed to validate the intermediate results and synthesize the key problems that would likely impact researchers conducting quantitative big data analysis in healthcare studies. The systems thinking action research method was applied to identify and describe the hidden issues. The findings were similar to the extant literature but three hidden fatal issues were detected. Methodical and statistical control solutions were proposed to overcome the three fatal healthcare big data analysis issues. |
| Author | Strang, Kenneth David Sun, Zhaohao |
| Author_xml | – sequence: 1 givenname: Kenneth David orcidid: 0000-0002-4333-4399 surname: Strang fullname: Strang, Kenneth David email: Kenneth.Strang@plattsburgh.edu organization: State University of New York at Plattsburgh, USA – sequence: 2 givenname: Zhaohao surname: Sun fullname: Sun, Zhaohao organization: PNG University of Technology, Papua New Guinea |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31264509$$D View this record in MEDLINE/PubMed |
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| References | Vajjhala, Strang 2017; 13 Ohm 2010; 57 Angiuli, Blitzstein, Waldo 2015; 58 Strang 2013; 2 Al-Janabi, Al-Shourbaji, Shojafar 2016; 18 Terry 2015; 43 Jovanovi, Stimec, Vladusi 2015; 10 Rothstein 2015; 43 Varian 2014; 28 Thorpe, Gray 2015; 130 Strang, Sun 2017; 4 Eastin, Brinson, Doorey 2016; 58 Hogarth, Soyer 2015; 56 Chen, Zhang 2014; 275 Brown 2008; 10 Filkins, Kim, Roberts 2016; 8 Strang 2015; 17 Shull 2014; 31 Wang, Jiang, Kambourakis 2015; 318 Lazer, Kennedy, King 2014; 343 Vaidhyanathan, Bulock 2014; 66 Shen, Zhang 2014; 281 De Zwart, Humphreys, Van Dissel 2014; 37 2015; 9 Strang, Sun 2016; 56 Salleh, Janczewski 2016; 100 Al-Ameen, Liu, Kwak 2012; 36 Burrows, Savage 2014; 12 Ekbia, Mattioli, Kouper 2015; 66 Jungwirth, Haluza 2017; 25 Zhong, Huang, Lan 2015; 165 Strang 2012; 2 Gandomi, Haider 2015; 35 Lusher, McGuire, van Schaik 2014; 19 Hoffman, Podgurski 2013; 41 Ward 2014; 10 Kshetri 2014; 38 Van Loenen, Kulk, Ploeger 2016; 33 Lichtblau, Weilandaug 2016 Van Otterlo 2014; 12 Strang, Alamieyeseigha 2015; 4 Leszczynski 2015; 33 Fan, Han, Liu 2014; 1 Couper 2013; 7 De Montjoye, Pentland 2016 1274; 351 bibr22-1460458219854603 Brown B (bibr16-1460458219854603) 2008; 10 bibr31-1460458219854603 De Zwart M (bibr27-1460458219854603) 2014; 37 ADA (bibr32-1460458219854603) 2015; 9 bibr15-1460458219854603 Hogarth RM (bibr36-1460458219854603) 2015; 56 bibr40-1460458219854603 bibr1-1460458219854603 Ohm P (bibr45-1460458219854603) 2010; 57 IBM (bibr47-1460458219854603) 2013 bibr23-1460458219854603 Checkland P (bibr24-1460458219854603) 1999 bibr49-1460458219854603 Couper MP (bibr48-1460458219854603) 2013; 7 bibr11-1460458219854603 bibr29-1460458219854603 Zikopoulos P (bibr37-1460458219854603) 2011 bibr50-1460458219854603 Burrows R (bibr18-1460458219854603) 2014; 12 bibr21-1460458219854603 bibr39-1460458219854603 bibr26-1460458219854603 bibr42-1460458219854603 bibr13-1460458219854603 bibr55-1460458219854603 Duhigg C (bibr46-1460458219854603) 2014 bibr34-1460458219854603 bibr52-1460458219854603 Hair JF (bibr56-1460458219854603) 2006 bibr44-1460458219854603 Vajjhala NR (bibr10-1460458219854603) 2017; 13 bibr35-1460458219854603 bibr53-1460458219854603 Lichtblau E (bibr30-1460458219854603) 2016 bibr6-1460458219854603 bibr28-1460458219854603 bibr19-1460458219854603 Vajjhala NR (bibr9-1460458219854603) bibr54-1460458219854603 bibr41-1460458219854603 bibr2-1460458219854603 Filkins BL (bibr14-1460458219854603) 2016; 8 bibr20-1460458219854603 bibr33-1460458219854603 bibr4-1460458219854603 Strang KD (bibr8-1460458219854603) 2016; 56 Sun Z (bibr3-1460458219854603); 1 bibr7-1460458219854603 bibr12-1460458219854603 Kessel PV (bibr5-1460458219854603) 2014 bibr43-1460458219854603 bibr25-1460458219854603 bibr17-1460458219854603 bibr51-1460458219854603 bibr38-1460458219854603 |
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| SubjectTerms | Big Data Data analysis Health care industry Research methodology |
| Title | Hidden big data analytics issues in the healthcare industry |
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