Journal:Challenges and opportunities of big data in health care: A systematic review

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Full article title Challenges and opportunities of big data in health care: A systematic review
Journal JMIR Medical Informatics
Author(s) Kruse, Clemens, S.; Goswamy, Rishi; Raval, Yesha; Marawi, Sarah
Author affiliation(s) School of Health Administration, Texas State University
Primary contact Email: scottkruse [at] txstate.edu; Phone: 1 2103554742
Editors Eysenbach, G.
Year published 2016
Volume and issue 4 (4)
Page(s) e38
DOI 10.2196/medinform.5359
ISSN 2291-9694
Distribution license Creative Commons Attribution 2.0
Website http://medinform.jmir.org/2016/4/e38/
Download http://medinform.jmir.org/2016/4/e38/pdf (PDF)
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Abstract

Background: Big data analytics offers promise in many business sectors, and health care is looking at big data to provide answers to many age-related issues, particularly dementia and chronic disease management.

Objective: The purpose of this review was to summarize the challenges faced by big data analytics and the opportunities that big data opens in health care.

Methods: A total of three searches were performed for publications between January 1, 2010 and January 1, 2016 (PubMed/MEDLINE, CINAHL, and Google Scholar), and an assessment was made on content germane to big data in health care. From the results of the searches in research databases and Google Scholar (N=28), the authors summarized content and identified nine and 14 themes under the categories "Challenges" and "Opportunities," respectively. We rank-ordered and analyzed the themes based on the frequency of occurrence.

Results: The top challenges were issues of data structure, security, data standardization, storage and transfers, and managerial skills such as data governance. The top opportunities revealed were quality improvement, population management and health, early detection of disease, data quality, structure, and accessibility, improved decision making, and cost reduction.

Conclusions: Big data analytics has the potential for positive impact and global implications; however, it must overcome some legitimate obstacles.

Keywords: big data, analytics, health care, human genome, electronic medical record

Introduction

Rationale

Big data analytics offers promise in many business sectors, and health care is looking at big data to provide answers to many age-related issues, particularly dementia and chronic disease management. This systematic review explores the depth of big data analytics since 2010 and identifies both challenges and opportunities associated with big data in health care. The review follows the standard set by Preferred Reporting Items for Systematic Reviews and Meta-analysis (2009).[1]

Big data is commonly defined through the four Vs: volume (scale or quantity of data), velocity (speed and analysis of real-time or near-real-time data), variety (different forms of data, often from disparate data sources), and veracity (quality assurance of the data). The first three Vs are found in most literature[2][3], and the fourth V is a goal.[4]

As of 2012, about 2.5 exabytes of data are created each day; Walmart can collect up to 2.5 petabytes of customer-related data per hour.[2] The industry of health care produces and collects data at a staggering speed, but different electronic health records (EHRs) collect data in different structures: structured, unstructured, and semistructured. This variety can pose difficulty when seeking veracity or quality assurance of the data. The EHRs can provide a rich source of data, ripe for analysis to increase our understanding of disease mechanisms, as well as better and personalized health care, but the data structures pose a problem to standard means of analysis.[5]

There are several large sources for big data in health care: genomics, EHR, medical monitoring devices, wearable video devices, and health-related mobile phone apps. Approximately 483 studies on genomics are registered with the U.S. Department of Health and Human Services; these studies are being conducted in nine countries, and they all use portions of the data from the Human Genome Project.[6] The EHR, being adopted in many countries, offers a source of data the depth of which is almost inconceivable. About 500 petabytes of data was generated by the EHR in 2012, and by 2020, the data will reach 25,000 petabytes.[7] The EHR can collect data from other monitoring devices, but the continuous data streams are not consistently saved in the longitudinal record.

The decrease in the cost of storage has enabled an exponential distribution of data collection, but the ability to analyze this quantity of data is the center of gravity for “big data” in health care. In the United States, financial incentives offered for the “meaningful use” of health information technology has spurred growth in the adoption of the EHR and other enabling health-related technology since 2009.

Health information systems show great potential in improving the efficiency in the delivery of care, a reduction in overall costs to the health care system, as well as a marked improvement in patient outcomes.[8] The U.S. government has allocated billions of dollars to help the country’s health care market realize some of these efficiencies and savings. Specific provisions of the Health Information Technology for Economic and Clinical Health (HITECH) Act, part of the American Recovery and Reinvestment Act, acknowledge the importance of information technology (IT) in the delivery of health care within the United States.[9] The Act allocates approximately US $17.2 billion in incentives for the adoption and meaningful use of health information technology, part of which involves the participation in the electronic exchange of clinical information. In 2010, the Congress passed the Health Information Exchange (HIE) Challenge Grant Program, which contributed about US $547.7 million to state HIE programs.[10]

References

  1. ↑ "PRISMA". The Ottawa Hospital. http://www.prisma-statement.org/. Retrieved 30 July 2015. 
  2. ↑ 2.0 2.1 McAfee, A.; Brynjolfsson, E. (2012). "Big data: The management revolution". Harvard Business Review 90 (10): 60–6. PMID 23074865. 
  3. ↑ Heudecker, N. (31 July 2013). "Hype Cycle for Big Data, 2013". Gartner, Inc. https://www.gartner.com/doc/2574616/hype-cycle-big-data-. Retrieved 08 November 2016. 
  4. ↑ Kayyali, B.; Knott, D.; Van Kuiken, S. (April 2013). "The big-data revolution in US health care: Accelerating value and innovation" (PDF). McKinsey & Company. https://digitalstrategy.nl/wp-content/uploads/E2-2013.04-The-big-data-revolution-in-US-health-care-Accelerating-value-and-innovation.pdf. Retrieved 11 November 2016. 
  5. ↑ Chawla, N.V.; Davis, D.A. (2013). "Bringing big data to personalized healthcare: A patient-centered framework". Journal of General Internal Medicine 28 (Suppl. 3): S660-5. doi:10.1007/s11606-013-2455-8. PMC PMC3744281. PMID 23797912. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3744281. 
  6. ↑ "Summary dbGaP Statistics". Genomic Data Sharing. National Institutes of Health. 23 June 2014. https://gds.nih.gov/17summary_dbGaP_statistics.html. Retrieved 09 November 2016. 
  7. ↑ Feldman, B.; Martin, E.M.; Skotnes, T. (October 2012). "Big Data in Healthcare: Hype and Hope" (PDF). Dr. Bonnie 360º. https://www.ghdonline.org/uploads/big-data-in-healthcare_B_Kaplan_2012.pdf. Retrieved 09 November 2016. 
  8. ↑ Hillestad, R.; Bigelow, J.; Bower, A. et al. (2005). Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. 24. pp. 1103–17. doi:10.1377/hlthaff.24.5.1103. PMID 16162551. 
  9. ↑ Centers for Medicare & Medicaid Services (28 July 2010). "Medicare and Medicaid Programs; Electronic Health Record Incentive Program". Federal Register. https://www.federalregister.gov/documents/2010/07/28/2010-17207/medicare-and-medicaid-programs-electronic-health-record-incentive-program. Retrieved 09 November 2016. 
  10. ↑ "State Health Information Exchange Cooperative Agreement Program". State Health Information Exchange. U.S. Department of Health and Human Services. 14 March 2014. https://www.healthit.gov/policy-researchers-implementers/state-health-information-exchange. Retrieved 09 November 2016. 

Abbreviations

ARRA: American Recover and Reinvestment Act

EHR: electronic health record

HIE: Health Information Exchange

HIPAA: Health Insurance Portability and Accountability Act

HITECH: Health Information Technology for Economic and Clinical Health

MeSH: Medical Subject Headings

PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-analysis

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In several cases the PubMed ID was missing and was added to make the reference more useful.

Per the distribution agreement, the following copyright information is also being added:

©Clemens Scott Kruse, Rishi Goswamy, Yesha Raval, Sarah Marawi. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 21.11.2016.

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