Big data science: Opportunities and challenges to address minority health and health disparities in the 21st century

Ethnicity and Disease

Volumn 27, Issue 2, 2017, Pages 95-106

  Zhang, Xinzhi ^a   Pérez Stable, Eliseo J ^a   Bourne, Philip E ^b   Peprah, Emmanuel ^c   Duru, O Kenrik ^d   Breen, Nancy ^a   Berrigan, David ^e   Wood, Fred ^f   Jackson, James S ^g   Wong, David W S ^h   Denny, Joshua ⁱ  

^a NATIONAL INSTITUTE ON MINORITY HEALTH AND HEALTH DISPARITIES   (United States)

^b NATIONAL INSTITUTES OF HEALTH   (United States)

^c NATIONAL HEART LUNG AND BLOOD INSTITUTE   (United States)

^d UCLA MEDICAL CENTER   (United States)

^e NATIONAL CANCER INSTITUTE   (United States)

^f NATIONAL INSTITUTES OF HEALTH   (United States)

^g UNIVERSITY OF MICHIGAN   (United States)

^h GEORGE MASON UNIVERSITY   (United States)

ⁱ VANDERBILT UNIVERSITY MEDICAL CENTER   (United States)

Author keywords

Big data; Health disparities; Health inequities

Indexed keywords

ELECTRONIC HEALTH RECORD; HEALTH CARE DISPARITY; HUMAN; MINORITY HEALTH; PIPELINE; PRIVACY; PUBLIC HEALTH; SCIENTIST; SOCIAL DETERMINANTS OF HEALTH; MINORITY GROUP; PROCEDURES; STATISTICS AND NUMERICAL DATA;

BIG DATA; DATA SCIENCE; HEALTHCARE DISPARITIES; HUMANS; MINORITY GROUPS; MINORITY HEALTH;

EID: 85018895047     PISSN: 1049510X     EISSN: 19450826     Source Type: Journal    
DOI: 10.18865/ed.27.2.95     Document Type: Article

References (86)

1. Ayanian JZ, Landon BE, Newhouse JP, Zaslavsky AM. Racial and ethnic disparities among enrollees in Medicare Advantage plans. N Engl J Med. 2014;371(24):2288-2297. https://doi.org/10.1056/NEJMsa1407273. PMID:25494268.
2. Manrai AK, Funke BH, Rehm HL, et al. Genetic Misdiagnoses and the Potential for Health Disparities. N Engl J Med. 2016;375(7):655-665. https://doi.org/10.1056/NEJMsa1507092. PMID:27532831.
3. Dankwa-Mullan I, Pérez-Stable EJ. Addressing Health Disparities Is a Place-Based Issue. Am J Public Health. 2016;106(4):637-639. https://doi.org/10.2105/AJPH.2016.303077. PMID:26959267.
4. Berman JJ. Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information. Amsterdam, Netherlands : Elsevier, 2013.
5. Hilbert M. Big Data for development: a review of promises and challenges. Dev Policy Rev. 2016;34(1):135-174. https://doi.org/10.1111/dpr.12142.
6. Hoyt RE, Snider D, Thompson C, Mantravadi S. IBM Watson analytics: automating visualization, descriptive, and predictive statistics. JMIR Public Health and Surveillance. 2016;2(2):e157.
7. Chen Y, Elenee Argentinis JD, Weber G. IBM Watson: How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research. Clin Ther. 2016;38(4):688-701. https://doi.org/10.1016/j.clinthera.2015.12.001. PMID:27130797.
8. The White House. Big Data is a Big Deal. U.S. Government; 2012. https://www.whitehouse.gov/blog/2012/03/29/big-data-big-deal. Accessed October 17, 2016.
9. National Institutes of Health. Data Science at NIH. National Institutes of Health; 2012. https://datascience.nih.gov/. Accessed October 17, 2016.
10. Precision Medicine Initiative (PMI) Working Group. The Precision Medicine Initiative Cohort Program - Building a Research Foundation for 21st Century Medicine. National Institutes of Health;2015. https://www.nih.gov/sites/default/files/research-training/initiatives/pmi/pmi-working-group-report-20150917-2.pdf: Accessed October 17, 2016.
11. Sim I. Two ways of knowing: Big Data and evidence-based medicine. Ann Intern Med. 2016;164(8):562-563. https://doi.org/10.7326/M15-2970. PMID:26809201.
12. Pipersburgh J. The push to increase the use of EHR technology by hospitals and physicians in the United States through the HITECH Act and the Medicare incentive program. J Health Care Finance. 2011;38(2):54-78. PMID:22372032.
13. Gottlieb LM, Tirozzi KJ, Manchanda R, Burns AR, Sandel MT. Moving electronic medical records upstream: incorporating social determinants of health. Am J Prev Med. 2015;48(2):215-218. https://doi.org/10.1016/j.amepre.2014.07.009. PMID:25217095.
14. Adler NE, Stead WW. Patients in context - EHR capture of social and behavioral determinants of health. N Engl J Med. 2015;372(8):698-701. https://doi.org/10.1056/NEJMp1413945. PMID:25693009.
15. Office of the National Coordinator for Health Information Technology. Percent of REC Enrolled Providers in an Organization/Site and Area Type Live on an EHR and Demonstrating Meaningful Use. Washington, DC; 2016.
16. Committee on the Recommended Social and Behavioral Domains and Measures for Electronic Health Records; Board on Population Health and Public Health Practice. Capturing Social and Behavioral Domains in Electronic Health Records: Phase 1. Washington, DC: Institute of Medicine. 2014.
17. Committee on the Recommended Social and Behavioral Domains and Measures for Electronic Health Records; Board on Population Health and Public Health Practice. Capturing Social and Behavioral Domains and Measures in Electronic Health Records: Phase 2. Washington, DC: Institute of Medicine. 2015.
18. Henry J, Pylypchuk Y, Searcy T, Patel V. Adoption of Electronic Health Record Systems among U.S. Non-Federal Acute Care Hospitals: 2008-2015. Washington, DC: Office of the National Coordinator for Health Information Technology; 2016.
19. Kruse CS, Kothman K, Anerobi K, Abanaka L. Adoption Factors of the Electronic Health Record: A Systematic Review. JMIR Med Inform. 2016;4(2):e19. https://doi.org/10.2196/medinform.5525. PMID:27251559.
20. Garg A, Boynton-Jarrett R, Dworkin PH. Avoiding the unintended consequences of screening for social determinants of health. JAMA. 2016;316(8):813-814. https://doi.org/10.1001/jama.2016.9282. PMID:27367226.
21. Gottesman O, Kuivaniemi H, Tromp G, et al. The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future. Genetics in Med. 2013;15(10):761-771.
22. Prentice JC, Conlin PR, Gellad WF, Edelman D, Lee TA, Pizer SD. Capitalizing on prescribing pattern variation to compare medications for type 2 diabetes. Value in Health. 2014;17(8):854-862.
23. Hripcsak G, Ryan PB, Duke JD, et al. Characterizing treatment pathways at scale using the OHDSI network. Proc Natl Acad Sci USA. 2016;113(27):7329-7336. https://doi.org/10.1073/pnas.1510502113. PMID:27274072.
24. Chen MS Jr, Lara PN, Dang JHT, Paterniti DA, Kelly K. Twenty years post-NIH Revitalization Act: enhancing minority participation in clinical trials (EMPaCT): laying the groundwork for improving minority clinical trial accrual: renewing the case for enhancing minority participation in cancer clinical trials. Cancer. 2014;120(suppl 7):1091-1096. https://doi.org/10.1002/cncr.28575. PMID:24643646.
25. Hernán MA, Robins JM. Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available. Am J Epidemiol. 2016;183(8):758-764. https://doi.org/10.1093/aje/kwv254. PMID:26994063.
26. Collins B. Big Data and Health Economics: Strengths, Weaknesses, Opportunities and Threats. Pharmacoeconomics. 2016;34(2):101-106. https://doi.org/10.1007/s40273-015-0306-7. PMID:26093888.
27. Obama B. United States Health Care Reform: Progress to Date and Next Steps. JAMA. 2016;316(5):525-532. https://doi.org/10.1001/jama.2016.9797. PMID:27400401.
28. National Center for Health Statistics, Centers for Disease Control and Prevention. Health Insurance Coverage: Early Release of Estimates from the National Health Interview Survey, January-March 2015. http://www.cdc.gov/nchs/data/nhis/earlyrelease/insur201508.pdf. 2016. Accessed Feb 15, 2017.
29. Bergner L, Yerby AS. Low income and barriers to use of health services. N Engl J Med. 1968;278(10):541-546. https://doi.org/10.1056/NEJM196803072781006. PMID:4866348.
30. Ball R, Robb M, Anderson SA, Dal Pan G. The FDA's sentinel initiative - A comprehensive approach to medical product surveillance. Clin Pharmacol Ther. 2016;99(3):265-268. https://doi.org/10.1002/cpt.320. PMID:26667601.
31. Tatonetti NP, Denny JC, Murphy SN, et al. Detecting drug interactions from adverse-event reports: interaction between paroxetine and pravastatin increases blood glucose levels. Clin Pharmacol Ther. 2011;90(1):133-142. https://doi.org/10.1038/clpt.2011.83. PMID:21613990.
32. White RW, Tatonetti NP, Shah NH, Altman RB, Horvitz E. Web-scale pharmacovigilance: listening to signals from the crowd. J Am Med Inform Assoc. 2013;20(3):404-408. https://doi.org/10.1136/amiajnl-2012-001482. PMID:23467469.
33. Wang G, Jung K, Winnenburg R, Shah NH. A method for systematic discovery of adverse drug events from clinical notes. J Am Med Inform Assoc. 2015;22(6):1196-1204. https://doi.org/10.1093/jamia/ocv102. PMID:26232442.
34. Spratt SE, Batch BC, Davis LP, et al. Methods and initial findings from the Durham Diabetes Coalition: integrating geospatial health technology and community interventions to reduce death and disability. J Clin Transl Endocrinol. 2015;2(1):26-36. https://doi.org/10.1016/j.jcte.2014.10.006.
35. Linton SL, Cooper HL, Kelley ME, et al; National HIV Behavioral Surveillance Study Group. Associations of place characteristics with HIV and HCV risk behaviors among racial/ethnic groups of people who inject drugs in the United States. Ann Epidemiol. 2016;26(9):619-630.e2. https://doi.org/10.1016/j.annepidem.2016.07.012. PMID:27576908.
36. Young SD, Rivers C, Lewis B. Methods of using real-time social media technologies for detection and remote monitoring of HIV outcomes. Prev Med. 2014;63:112-115. https://doi.org/10.1016/j.ypmed.2014.01.024. PMID:24513169.
37. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009;457(7232):1012-1014. https://doi.org/10.1038/nature07634. PMID:19020500.
38. Lazer D, Kennedy R, King G, Vespignani A. Big Data. The parable of Google Flu: traps in Big Data analysis. Science. 2014;343(6176):1203-1205.
39. Smith BT, Smith PM, Harper S, Manuel DG, Mustard CA. Reducing social inequalities in health: the role of simulation modelling in chronic disease epidemiology to evaluate the impact of population health interventions. J Epidemiol Community Health. 2014;68(4):384-389. https://doi.org/10.1136/jech-2013-202756. PMID:24363409.
40. Speybroeck N, Van Malderen C, Harper S, Müller B, Devleesschauwer B. Simulation models for socioeconomic inequalities in health: a systematic review. Int J Environ Res Public Health. 2013;10(11):5750-5780. https://doi.org/10.3390/ijerph10115750. PMID:24192788.
41. Gange SJ, Golub ET. From Smallpox to Big Data: The Next 100 Years of Epidemiologic Methods. Am J Epidemiol. 2016;183(5):423-426. https://doi.org/10.1093/aje/kwv150. PMID:26443419.
42. Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff (Millwood). 2014;33(7):1123-1131. https://doi.org/10.1377/hlthaff.2014.0041. PMID:25006137.
43. Rumsfeld JS, Joynt KE, Maddox TM. Big data analytics to improve cardiovascular care: promise and challenges. Nat Rev Cardiol. 2016;13(6):350-359. https://doi.org/10.1038/nrcardio.2016.42. PMID:27009423.
44. Luo G. PredicT-ML: a tool for automating machine learning model building with big clinical data. Health Information Science and Systems. 2016;4:5.
45. Carroll RJ, Eyler AE, Denny JC. Naive Electronic Health Record phenotype identification for Rheumatoid arthritis. AMIA Symposium Proceedings. 2011;2011:189-196.
46. Doshi-Velez F, Ge Y, Kohane I. Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis. Pediatrics. 2014;133(1):e54-e63. https://doi.org/10.1542/peds.2013-0819. PMID:24323995.
47. Peissig PL, Santos Costa V, Caldwell MD, et al. Relational machine learning for electronic health record-driven phenotyping. J Biomed Inform. 2014;52:260-270. https://doi.org/10.1016/j.jbi.2014.07.007. PMID:25048351.
48. Taylor RA, Pare JR, Venkatesh AK, et al. Prediction of in-hospital mortality in emergency department patients with sepsis: a local big data-driven, machine learning approach. Academic Emerg Med. 2016;23(3):269-278.
49. Wu AHB, White MJ, Oh S, Burchard E. The Hawaii clopidogrel lawsuit: the possible effect on clinical laboratory testing. Per Med. 2015;12(3):179-181. https://doi.org/10.2217/pme.15.4.
50. Beermann KJ, Ellis MJ, Sudan DL, Harris MT. Tacrolimus dose requirements in African-American and Caucasian kidney transplant recipients on mycophenolate and prednisone. Clin Transplant. 2014;28(7):762-767. https://doi.org/10.1111/ctr.12376. PMID:24754564.
51. File T, Ryan C. Computer and Internet Use in the United States: 2013. Washington, DC: American Community Survey Reports, ACS-28, U.S. Census Bureau; 2014.
52. Amante DJ, Hogan TP, Pagoto SL, English TM, Lapane KL. Access to care and use of the Internet to search for health information: results from the US National Health Interview Survey. J Med Internet Res. 2015;17(4):e106. https://doi.org/10.2196/jmir.4126. PMID:25925943.
53. Mittelstadt BD, Floridi L. The ethics of big data: current and foreseeable issues in biomedical contexts. Sci Eng Ethics. 2016;22(2):303-341. https://doi.org/10.1007/s11948-015-9652-2. PMID:26002496.
54. Amir Y, Sharon I. Replication research - a must for the scientific advancement of psychology. J Soc Behav Pers. 1990;5(4):51-69.
55. Kho AN, Cashy JP, Jackson KL, et al. Design and implementation of a privacy preserving electronic health record linkage tool in Chicago. J Am Med Inform Assoc. 2015;22(5):1072-1080. https://doi.org/10.1093/jamia/ocv038. PMID:26104741.
56. Loukides G, Denny JC, Malin B. The disclosure of diagnosis codes can breach research participants' privacy. J Am Med Inform Assoc. 2010;17(3):322-327. https://doi.org/10.1136/jamia.2009.002725. PMID:20442151.
57. Gymrek M, McGuire AL, Golan D, Halperin E, Erlich Y. Identifying personal genomes by surname inference. Science. 2013;339(6117):321-324. https://doi.org/10.1126/science.1229566. PMID:23329047.
58. The President's Council of Advisors on Science and Technology. Report to the President: Big Data and Privacy: A Technological Perspective. Washington, DC: White House; 2014.
59. White RM. Unraveling the Tuskegee Study of Untreated Syphilis. Arch Intern Med. 2000;160(5):585-598. https://doi.org/10.1001/archinte.160.5.585. PMID:10724044.
60. Caplan A. NIH finally makes good with Henrietta Lacks' family - and it's about time, ethicist says. NBC News; http://www.nbcnews.com/health/nih-finally-makes-good-henrietta-lacks-family-its-about-time-6C10867941; 2013. Accessed October 17, 2016.
61. Young E. Making Indigenous Peoples Equal Partners in Gene Research. http://www.theatlantic.com/science/archive/2015/10/indigenising-genomics/412096/2015. Accessed October 17, 2016.
62. Wears RL, Williams DJ. Big questions for "Big Data". Ann Emerg Med. 2016;67(2):237-239. https://doi.org/10.1016/j.annemergmed.2015.09.019. PMID:26481264.
63. Joy M, Clement T, Sisti D. The ethics of behavioral health information technology: frequent flyer icons and implicit bias. JAMA. 2016;316(15):1539-1540. https://doi.org/10.1001/jama.2016.12534. PMID:27607056.
64. Cox D. Big Data and precision. Biometrika. 2015;102(3):712-716. https://doi.org/10.1093/biomet/asv033.
65. Filice CE, Joynt KE. Examining race and ethnicity information in Medicare administrative data. Med Care. 2016;Jul:29. PMID:27479593.
66. Kaneshiro B, Geling O, Gellert K, Millar L. The challenges of collecting data on race and ethnicity in a diverse, multiethnic state. Hawaii Med J. 2011;70(8):168-171. PMID:21886309.
67. Kass RE, Caffo BS, Davidian M, Meng XL, Yu B, Reid N. Ten Simple Rules for Effective Statistical Practice. PLOS Comput Biol. 2016;12(6):e1004961. https://doi.org/10.1371/journal.pcbi.1004961. PMID:27281180.
68. Hochster HS, Niedzwiecki D. Big Data, Small Effects. J Clin Oncol. 2016;34(11):1170-1171. https://doi.org/10.1200/JCO.2015.65.8161. PMID:26884573.
69. Srinivasan S, Moser RP, Willis G, et al. Small is essential: importance of subpopulation research in cancer control. Am J Public Health. 2015;105(S3)(suppl 3):S371-S373. https://doi.org/10.2105/AJPH.2014.302267. PMID:25905825.
70. Mardis ER. The challenges of big data. Dis Model Mech. 2016;9(5):483-485. https://doi.org/10.1242/dmm.025585. PMID:27147249.
71. National Institutes of Health. NIH Data Sharing Policy and Implementation Guidance: http://grants.nih.gov/grants/policy/data-sharing/data-sharing-guidance.htm;2003. Accessed October 18, 2016.
72. Luo J, Wu M, Gopukumar D, Zhao Y. Big Data Application in Biomedical Research and Health Care: A Literature Review. Biomed Inform Insights. 2016;8:1-10. https://doi.org/10.4137/BII.S31559. PMID:26843812.
73. Bahga A, Madisetti VK. A cloud-based approach for interoperable electronic health records (EHRs). IEEE J Biomed Health Inform. 2013;17(5):894-906. https://doi.org/10.1109/JBHI.2013.2257818. PMID:25055368.
74. Sobhy D, El-Sonbaty Y, Abou Elnasr M. MedCloud. Health care cloud computing system. 2012 International Conference for Internet Technology and Secured Transactions. 2012:161-166.
75. Lin WM, Dou WC, Zhou ZJ, Liu C. A cloud-based framework for Home-diagnosis service over big medical data. J Syst Softw. 2015;102:192-206. https://doi.org/10.1016/j.jss.2014.05.068.
76. Denny JC. Chapter 13: mining electronic health records in the genomics era. PLOS Comput Biol. 2012;8(12):e1002823. https://doi.org/10.1371/journal.pcbi.1002823. PMID:23300414.
77. Stang PE, Ryan PB, Racoosin JA, et al. Advancing the science for active surveillance: rationale and design for the Observational Medical Outcomes Partnership. Ann Intern Med. 2010;153(9):600-606. https://doi.org/10.7326/0003-4819-153-9-201011020-00010. PMID:21041580.
78. Collins FS, Hudson KL, Briggs JP, Lauer MS. PCORnet: turning a dream into reality. J Am Med Inform Assoc. 2014;21(4):576-577. https://doi.org/10.1136/amiajnl-2014-002864. PMID:24821744.
79. Weber GM, Murphy SN, McMurry AJ, et al. The Shared Health Research Information Network (SHRINE): a prototype federated query tool for clinical data repositories. J Am Med Inform Assoc. 2009;16(5):624-630. https://doi.org/10.1197/jamia.M3191. PMID:19567788.
80. Doshi JA, Hendrick FB, Graff JS, Stuart BC. Data, data everywhere, but access remains a big issue for researchers: a review of access policies for publicly-funded patient-level health care data in the United States. EGEMS (Wash DC). 2016;4(2):1204. https://doi.org/10.13063/2327-9214.1204. PMID:27141517.
81. Warren E. Strengthening Research through Data Sharing. N Engl J Med. 2016;375(5):401-403. https://doi.org/10.1056/NEJMp1607282. PMID:27518656.
82. National Science Foundation NCfSaES. Women, Minorities, and Persons with Disabilities in Science and Engineering. Arlington, VA: Special Report NSF 15-311. https://www.nsf.gov/statistics/2015/nsf15311/digest/;2015. Accessed October 18, 2016.
83. Valantine H A, Collins FS. National Institutes of Health addresses the science of diversity. Proceedings of the National Academy of Sciences. 2015;112(40):12240-12242. https://doi.org/10.1073/pnas.1515612112.
84. Van Horn JD. Opinion: big data biomedicine offers big higher education opportunities. Proc Natl Acad Sci USA. 2016;113(23):6322-6324. https://doi.org/10.1073/pnas.1607582113. PMID:27274038.
85. National Institute of Minority Health and Health Disparities. National Institutes of Health; http://grants.nih.gov/grants/guide/rfa-files/RFA-MD-16-002.html: 2016. Accessed October 18, 2016.
86. McEligot AJ, Behseta S, Cuajungco MP, Van Horn JD, Toga AW. Wrangling Big Data Through Diversity, Research Education and Partnerships. Calif J Health Promot. 2015;13(3):vi-ix. PMID:27257409