Subscribe to RSS
DOI: 10.15265/IY-2014-0007
Big Data: Are Biomedical and Health Informatics Training Programs Ready?
Contribution of the IMIA Working Group for Health and Medical Informatics EducationPublication History
15 August 2014
Publication Date:
05 March 2018 (online)
Summary
Objective: The growing volume and diversity of health and biomedical data indicate that the era of Big Data has arrived for healthcare. This has many implications for informatics, not only in terms of implementing and evaluating information systems, but also for the work and training of informatics researchers and professionals. This article addresses the question: What do biomedical and health informaticians working in analytics and Big Data need to know?
Methods: We hypothesize a set of skills that we hope will be discussed among academic and other informaticians.
Results: The set of skills includes: Programming - especially with data-oriented tools, such as SQL and statistical programming languages; Statistics - working knowledge to apply tools and techniques; Domain knowledge - depending on one’s area of work, bioscience or health care; and Communication - being able to understand needs of people and organizations, and articulate results back to them.
Conclusion: Biomedical and health informatics educational programs must introduce concepts of analytics, Big Data, and the underlying skills to use and apply them into their curricula. The development of new coursework should focus on those who will become experts, with training aiming to provide skills in “deep analytical talent” as well as those who need knowledge to support such individuals.
-
References
- 1 Kayyali B, Knott D, Van Kuiken S. The big-data revolution in US health care: Accelerating value and innovation.. Mc Kinsey: & Company;; 2013
- 2 Smith M, Saunders R, Stuckhardt L, McGinnis J. Best Care at Lower Cost: The Path to Continuously Learning Health Care in. America:: The National Academies Press;; 2013
- 3 Grossman C, McGinnis JM. Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care: Workshop Series Summary:. National Academies Press;; 2011
- 4 Jee K, Kim G-H. Potentiality of big data in the medical sector: Focus on how to reshape the healthcare system.. Healthc Inform Res 2013; 19 (02) 79-85.
- 5 Feldman B, Martin EM, Skotnes T. Big Data in Healthcare Hype and Hope 2012 [cited 2013 28/11/2013].. Available from: http://www.west-info.eu/files/big-data-in-healthcare.pdf.
- 6 Miller AR, Tucker C. Health Information Exchange, System Size and Information Silos.. J Health Econ 2014; 33 (01) 28-42.
- 7 Ward J, Barker A. Undefined By Data: A Survey of Big Data Definitions.. Databases (csDB) [Internet]. 2013 Available from: http://arxiv.org/abs/1309.5821.
- 8 Bourne P. What Big Data means to me.. J Am Med Inform Assoc 2014; Mar 1 21 (02) 194.
- 9 Murdoch TB, Detsky AS. The inevitable application of big data to health care.. JAMA 2013; Apr 3 309 (13) 1351-2.
- 10 Hersh WR, Weiner MG, Embi PJ, Logan JR, Payne PR, Bernstam EV. et al. Caveats for the use of operational electronic health record data in comparative effectiveness research.. Med care 2013; Aug 51 (08) Suppl 03 S30-7.
- 11 Haynes B. Can it work? Does it work? Is it worth it? The testing of healthcare interventions is evolving.. BMJ 1999; Sep 11 319 7211 652-3.
- 12 Rossouw JE, Anderson GL, Prentice RL, LaCroix AZ, Kooperberg C, Stefanick ML. et al. Risks and benefits of estrogen plus progestin in healthy postmenopausal women: principal results From the Women’s Health Initiative randomized controlled trial.. JAMA 2002; Jul 17 288 (03) 321-33. .
- 13 Hersh W, Cimino J, Payne P, Embi P, Logan J, Weiner M. et al. Recommendations for the Use of Operational Electronic Health Record Data in Comparative Effectiveness Research, eGEMs (Generating Evidence & Methods to improve patient outcomes).. 2013 1(1).
- 14 Hersh W. A stimulus to define informatics and health information technology.. BMC Med Inform Decis Mak 2009; 9: 24.
- 15 Greene SM, Reid RJ, Larson EB. Implementing the learning health system: from concept to action.. Ann Intern Med 2012; Aug 7 157 (03) 207-10.
- 16 Brown RS, Peikes D, Peterson G, Schore J, Razafindrakoto CM. Six features of Medicare coordinated care demonstration programs that cut hospital admissions of high-risk patients.. Health Aff 2012; Jun 31 (06) 1156-66.
- 17 Cassel CK, Guest JA. Choosing wisely: helping physicians and patients make smart decisions about their care.. JAMA 2012; May 2 307 (17) 1801-2.
- 18 Feero W. Determining actionability of genetic findings in clinical practice.. ACP Internist.; 2012 (July/August).
- 19 Larson EB. Building trust in the power of “big data” research to serve the public good.. JAMA 2013; Jun 19 309 (23) 2443-4.
- 20 V. D. Data science and prediction.. Communications of the ACM 2013; 56 (12) 64-73.
- 21 Davenport TH, Patil DJ. Data Scientist: The Sexiest Job of the 21st Century 2012.. Available from: http://hbr.org/2012/10/data-scientist-thesexiest-job-of-the-21st-century/.
- 22 Manyika J, Chui M, Brown B, Bughin J, Dobbs R, Roxburgh C. et al. Big data: The next frontier for innovation, competition, and productivity.. May 2011 Available from: http://www.mckinsey.com/insights/business_technology/big_ data_the_next_frontier_for_innovation.
- 23 Big Data Analytics.. An assessment of demand for labour and skills, 2012-2017 2013 [cited 2013 9/12/2013].. Available from: http://www.e-skills.com/Documents/Research/General/BigDataAnalytics_Report_Jan2013.pdf.
- 24 Big data analytics.. Adoption and employment trends, 2012-2017.. 2013 [cited 2013 9/12/2013]. Available from: http://www.e-skills.com/Documents/Research/General/BigDataAnalytics_Report_Nov2013.pdf.
- 25 Fraser H, Jayadewa C, Mooiweer P, Gordon D, Piccone J. Analytics across the ecosystem - A prescription for optimizing healthcare outcomes Somers,. NY 2013
- 26 Balboni F, Finch G, Rodenbeck-Reese C, Shockley R. Analytics: A blueprint for value.. Converting big data and analytics insights into results 2013 [cited 2013 28/11/2013]. Available from: http://www-935.ibm.com/services/us/gbs/thoughtleadership/ninelevers/.
- 27 Solving the talent equation for health IT:. PwC Health Research Institute; 2013. Available from: http://pwchealth.com/cgi-local/hregister.cgi/reg/pwc-hri-healthcare-it-staffingstrategies.pdf.
- 28 Workshop on Enhancing Training for Biomedical Big Data - Big Data to Knowledge (BD2K) Initiative:. National Institutes of Health;; 2013 Available from: http://bd2k.nih.gov/pdf/bd2k_training_workshop_report.pdf.
- 29 ASTD.. Bridging the Skills Gap 2012 [cited 2014 01/24/14]. 52].. Available from: http://nist.gov/mep/upload/Bridging-the-Skills-Gap_2012.pdf.
- 30 CompTIA.. State of the IT Skills Gap 2012 [cited 2014 01/24/2014].. Available from: http://www.wired.com/wiredenterprise/wp-content/uploads/2012/03/Report_-_CompTIA_IT_ Skills_Gap_study_-_Full_Report.sflb_.pdf.
- 31 CompTIA.. Big Data Insights and Opportunities 2013 [cited 2014 01/24/2014].. Available from: http://www.eurolanresearch.com/otherUploadeddocs/2ndBigData.pdf.
- 32 IBM.. IBM Narrows Big Data Skills Gap By Partnering With More Than 1,000 Global Universities 2013.. Available from: http://www-03.ibm.com/press/us/en/pressrelease/41733.wss.
- 33 Mantas J, Ammenwerth E, Demiris G, Hasman A, Haux R, Hersh W. et al. Recommendations of the International Medical Informatics Association (IMIA) on Education in Biomedical and Health Informatics. First Revision.. Methods Inf Med 2010; Jan 7 49 (02) 105-20.
- 34 Dumbill W, Liddy E, Stanton J, Mueller K, Farnham S. Educating the next generation of data scientists.. Big Data 2013; 1 (01) BD21-BD7.