Big data, advanced analytics and the future of comparative effectiveness research

Journal of Comparative Effectiveness Research

Volumn 3, Issue 2, 2014, Pages 167-176

  Berger, Marc L ^a   Doban, Vitalii ^a  

^a PFIZER INC   (United States)

Author keywords

advanced analytics; big data; data mining; electronic health records; machine learning

Indexed keywords

ACCESS TO INFORMATION; BIOMEDICINE; COMPARATIVE EFFECTIVENESS; COMPUTER PROGRAM; DATA MINING; ELECTRONIC MEDICAL RECORD; HEALTH INSURANCE; INFORMATION PROCESSING; INFORMATION TECHNOLOGY; INTERNET; MACHINE LEARNING; ONLINE SYSTEM; PRIORITY JOURNAL; REVIEW; SOCIAL NETWORK; FACTUAL DATABASE; HUMAN; INFORMATION RETRIEVAL; PROCEDURES; TRENDS; UNITED STATES;

COMPARATIVE EFFECTIVENESS RESEARCH; DATABASES, FACTUAL; ELECTRONIC HEALTH RECORDS; HUMANS; INFORMATION STORAGE AND RETRIEVAL; UNITED STATES;

EID: 84897906164     PISSN: 20426305     EISSN: 20426313     Source Type: Journal    
DOI: 10.2217/cer.14.2     Document Type: Review

References (33)

1. Olsen LA, Aisner D, McGinnis JM (Eds). The Learning Healthcare System, Institute of Medicine (US) Roundtable on Evidence-Based Medicine. National Academies Press, Washington, DC, USA (2007).
2. DesRoches CM, Campbell EG, Vogeli C et al. Electronic health records' limited successes suggest more targeted uses. Health Aff. 29(4), 639-464 (2010).
3. Kuehn BM. Scientists mine web search data to identify epidemics and adverse events. JAMA 309, 1883-1884 (2013).
4. White RW, Tatonetti NP, Shah NH, Altman RB, Horvitz E. Web-scale pharmacovigilance: listening to signals from the crowd. J. Am. Med. Inform. Assoc. 20(3), 404-408 (2013).
5. Jensen C. Multidimensional Databases and Data Warehousing. Morgan & Claypool Publishers, CA, USA (2010).
6. Provost F, Fawcett T. Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. O'Reilly Media, Inc., MA, USA (2013).
7. Koboldt DC, Steinberg KM, Larson DE et al. The next-generation sequencing revolution and its impact on genomics. Cell 155, 27-38 (2013).
8. Berger ML. Can the pharmaceutical industry embrace comparative effectiveness research? A view from inside. Expert Rev. Pharmacoecon. Outcomes Res. 13(5), 565-568 (2013).
9. Berger ML, Dreyer N, Anderson F et al. Prospective observational studies to assess comparative effectiveness: the ISPOR Good Practices Task Force Report. Value Health 15, 217-230 (2012).
10. Linder JA, Haas JS, Iyer A et al. Secondary use of electronic health record data: spontaneous triggered adverse drug event reporting. Pharmacoepidemiol. Drug Saf. 19(12), 1211-1215 (2010).
11. Weitzman ER, Kaci L, Mandl KD. Sharing medical data for health research: the early personal health record experience. J. Med. Internet Res. 12(2), e14 (2010).
12. Grande D, Mitra N, Shah A, Wan F, Asch DA. Public preferences about secondary uses of electronic health information. JAMA Intern. Med. 173(19), 1798-1806 (2013).
13. Kohane IS. Secondary use of health information: are we asking the right question? JAMA Intern. Med. 173(19), 1806-1807 (2013).
14. Ryan PB, Stang PE, Overhage JM et al. A comparison of the empirical performance methods for a risk identification system. Drug Safety 36, 143-158 (2013).
15. McCarty CA, Chisholm RL, Chute CG et al. The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med. Gen. 4, 13 (2011).
16. Wicks P, Vaughan TE, Massigli MP, Heywood J. Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm. Nat. Biotech. 29, 411-414 (2011).
17. The Official Web Site for the Medicare and Medicaid Electronic Health Records (EHR) Incentive Programs. www.cms.gov/ehrincentiveprograms
18. Mathematica Policy Research and the Harvard School of Public Health. DesRoches CM, Painter MW, Jha AK (Eds). Health Information Technology in the USA: Better Information Systems for Better Care. Princeton, NJ, USA (2013). www.rwjf.org/content/dam/farm/reports/ reports/2013/rwjf406758
19. Explore flu trends - USA. www.google.org/flutrends/us/#US
20. Wikipedia: big data. http://en.wikipedia.org/wiki/Big-data
21. Gartner IT Glossary: big data. www.gartner.com/it-glossary/big-data
22. How big is a petabyte, exabyte, zettabyte or a yottabyte? http://highscalability.com/blog/2012/9/11/ how-big-is-a-petabyte-exabyte- zettabyte-or-a-yottabyte.html
23. Wikipedia: machine learning. http://en.wikipedia.org/wiki/Machine- learning
24. Open Clinical Knowledge Management for Medical Care. Artificial Neural Networks in Healthcare: A Short Review. www.openclinical.org/ neuralnetworksinhealthcare.html
25. Data Warehousing Review. Mailvaganam H. Introduction to OLAP - Slice, Dice and Drill! (2007). www.dwreview.com/OLAP/Introduction- OLAP.html
26. Introduction to Massively Parallel Processing (MPP) database. http://dwarehouse.wordpress. com/2012/12/28/introduction-to-massively-parallel- processing-mpp-database
27. Teradata. www.teradata.com
28. Netezza Data Warehouse. www.ndm.net/datawarehouse/IBM/netezza
29. SAS. www.sas.com
30. The Hadoop Distributed File System. www.aosabook.org/en/hdfs.html
31. Watson in healthcare. www-03.ibm.com/innovation/us/watson/ watson-in-healthcare.shtml.
32. Salford Lung Study. http://data.gov.uk/library/salford-lung-study
33. Health Information Technology for Economic and Clinical Health (HITECH) Act. www.healthit.gov/sites/default/files/hitech- act-excerpt-from-arra-with- index.pdf