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Review

Challenges and Opportunities of Big Data in Health Care: A
Systematic Review
Clemens Scott Kruse, MBA, MHA, MSIT, PhD; Rishi Goswamy, MHA; Yesha Raval, MHA; Sarah Marawi, MHA
School of Health Administration, Texas State University, San Marcos, TX, United States

Corresponding Author:
Clemens Scott Kruse, MBA, MHA, MSIT, PhD
School of Health Administration
Texas State University
601 University Dr
College of Health Professions
San Marcos, TX, 78666
United States
Phone: 1 2103554742
Fax: 1 5122458712
Email: scottkruse@txstate.edu

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 thatbig data opens in health care.
Methods: A total of 3 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 healthcare. From the results of the searches in research databases and Google Scholar (N=28), the authors summarized content andidentified 9 and 14 themes under the categories Challenges and Opportunities, respectively. We rank-ordered and analyzed thethemes 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.
(JMIR Med Inform 2016;4(4):e38) doi: 10.2196/medinform.5359
KEYWORDS
big data; analytics; health care; human genome; electronic medical record

Introduction
Rationale
Big data analytics offers promise in many business sectors, andhealth 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 andopportunities 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].
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Big data is commonly defined through the 4 Vs: volume (scaleor quantity of data), velocity (speed and analysis of real-timeor near-real-time data), variety (different forms of data, often
from disparate data sources), and veracity (quality assurance ofthe data). The first 3 Vs are found in most literature [2,3], andthe 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 andcollects data at a staggering speed, but different electronic healthrecords (EHRs) collect data in different structures: structured,
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unstructured, and semistructured. This variety can pose difficultywhen seeking veracity or quality assurance of the data. The
EHRs can provide a rich source of data, ripe for analysis toincrease our understanding of disease mechanisms, as well as
better and personalized health care, but the data structures posea problem to standard means of analysis [5].

convert data into knowledge. Health care needs to follow theirlead so that decisions regarding organizational objectives andgoals can be met [4,11,12]. This evolutionary process of data
management is collectively known as big data, and it is essential
to the future of adoption and management of health information
technology [13].

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 US Department
of Health and Human Services; these studies are being
conducted in 9 countries, and they all use portions of the datafrom the Human Genome Project [6]. The EHR, being adopted
in many countries, offers a source of data the depth of which isalmost 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 othermonitoring devices, but the continuous data streams are notconsistently saved in the longitudinal record.

Objectives

The decrease in the cost of storage has enabled an exponential
distribution of data collection, but the ability to analyze thisquantity of data is the center of gravity for “big data” in healthcare. 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 increase in patient
outcomes [8]. The US government has allocated billions ofdollars to help the country’s health care market realize some ofthese efficiencies and savings. Specific provisions of the Health
Information Technology for Economic and Clinical Health(HITECH), part of the American Recovery and Reinvestment
Act, acknowledge the importance of IT in the delivery of healthcare within the United States [9]. The Act allocatesapproximately US $17.2 billion in incentives for the adoption
and meaningful use of health information technology, part ofwhich involves the participation in the electronic exchange ofclinical 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].
With the implementation of this legislation as well as thetechnologies associated with it, it is imperative to effectively
organize and process the ever-increasing quantity of data thatis digitally collected and stored within health care organizations.
Other industries such as astronomy, retail, search engines, andpolitics have developed advanced data-handling capabilities tohttp://medinform.jmir.org/2016/4/e38/

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The purpose of this systematic review is to objectively review
articles and studies published in academic journals in order tocompile a list of challenges and opportunities faced by big data
analytics in health care in the United States. Particular emphasis
was paid to age-related applications of big data.

Methods
Eligibility Criteria
Articles and studies were eligible for analysis if they were
published between 2010 and 2015, published in academic
journals, and published in English. The researchers chose a
range from 2010 to 2015 for two reasons: HITECH was passed
in 2009, and it appeared that a blossom of research and otherarticles seemed to occur in 2010. We focused on academic
journals for their peer-review quality and to decrease the chanceof selecting something about big data published from a
noncredible source.

Information Sources
A combination of key terms from Medical Subject Headings(MeSH) and Boolean operators were combined and used in 2
common research databases, CINAHL and PubMed, andcombined with a general search from Google Scholar (see Figure1) in January 2016.
These terms were chosen not only because they are the focusof the review, but also because they were identified in the initialresearch into the definition of big data.

SearchThe following search string was used in all 3 searches: ((“bigdata” AND healthcare) OR (“big data” AND “health care”)).
This search string was used in CINAHL, PubMed (MEDLINE),
and Google Scholar. In the 2 research databases, our team wasable to restrict the search to academic journals (including othersystematic reviews). MEDLINE was excluded in CINAHL
because it was already captured in PubMed. Google Scholar
creates difficulty for searches because of its severe limit offilters typically associated with academic research. The initial
13,935 results were limited by restricting dates to the last 5
years, limiting results to academic journals and MEDLINE, andin Google Scholar by restricting the keyword search to titles.
The result from the filters ended with 121 articles to review.

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Figure 1. Literature review process with inclusion and exclusion criteria.

Study Selection
Through group research and a series of consensus meetings,
researchers were trained to identify articles germane to thisreview and to recommend elimination of all others. A sharedspreadsheet was used by the research team to parse through thelist of articles. Researchers read all articles in their entirety. A
total of 97 articles were eliminated due to various exclusion
criteria (not germane to big data or health care, editorial only,
not an academic journal, or duplicate from another search), and4 additional articles were identified from the references of the
24 that remained. The group of reviewers made these rejections
or additional recommendations through a series of consensus
meetings where we met to discuss their recommendations andconsensus was reached through discussion. A total of 28 articles
remained in the final review.

Data Collection Process and Identification of Summary
Measures
Each article was reviewed by at least two authors to identify
the relevant points. All reviewers used a spreadsheet template
to summarize their key observations from each article. One teammember combined the spreadsheets into one and shared it once
again. Reviewers held one more consensus meeting to discuss
their findings. From this meeting, trends were identified, andfrom those trends, inferences were made.

Additional Analysis
From the list of observations, reviewers were able to identify
some common threads that emerged as challenges andopportunities in health care that permeated multiple articles.
Separate tables were created to group the threads, and from eachof these tables, common themes were identified. These common
themes only emerged when reviewers combined their

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additional analysis.

Results
Study Selection
As depicted in Figure 1, 935 articles resulted from the initial
search. Filters such as data published (2010-2015), academic
journals, and English language were implemented to reduce therange to what was being studied. Reviewers agreed to eliminate
editorials and focus on those articles that studied big data, as
described in the Introduction section of this manuscript. At theend of the search process, only 28 remained. The articles
reviewed for this study ranged from 2012 to 2015. The majorityof the literature chosen for this paper was published in 2014(15/28, 54%), and a minority was published in 2015 (2/28, 7%);the latter was most likely due to the early part of the year when
the search was conducted.

Synthesis of Results
Multiple reviewers read each article in its entirety. Articles were
included or excluded based on the criteria illustrated in Figure
1. All articles included in the analysis were sorted by date andare listed in Multimedia Appendix 1.
A study catalog number was assigned to each article to simplify
the analysis. Researchers summarized the main points of eacharticle for further analysis.

Additional Analysis
Through the combination of observations, reviewers identified
common threads (challenges and opportunities) and themes
from each thread. Themes were organized into affinity diagrams(Tables 1 and 2), compared, and discussed among researchers.

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Kruse et allack of skill of data analysts, inaccuracies in data, regulatorycompliance, and real-time analytics. Examples for each theme
are provided in Table 1. A total of 60 observations were madefor challenges.

Challenges for Big Data in Health Care
Nine themes emerged under the category of challenges: data
structure, security, data standardization, data storage andtransfers, managerial issues such as governance and ownership,
Table 1. Themes associated with challenges for big data in health care.
ThemesExamplesNumber of articles

Articles themes appeared in(n)Data structure

Fragmented data

% of total articles(N=28)

17

1, 2, 7-9, 12, 14-19, 22, 25-28

61%

14

2, 4, 7-9, 12, 13, 17, 21, 22, 25- 50%
28

114, 5, 7-9, 11, 12, 15, 16, 22, 25 39%

8

1, 4, 7, 12, 22, 26, 28

28%

4

2, 8, 14, 22

14%

Incompatible formats
Heterogeneous data
Raw and unstructured datasets
Large volumes
High variety and velocity
Lack of transparency
Security

Privacy
Confidentiality
Data duplication
Integrity

Data standardization

Limited Interoperability
Data acquisition and cleansing
Global sharing
Terminology
Language barriers

Storage and transfers

Expensive to store
Transfer from one place to other
Store electronic data
Securely extract, transmit, and process

Managerial issues

Governance issues
Ownership issues

Lack of skill

Untrained workers

35, 9, 14

11%

Inaccuracies

Inconsistences

1

9

4%

Lack of precision
Data timeliness
Regulatory compliance

Legal concerns

1

13

4%

Real-time analytics

Real-time analytics

1

9

4%

The 4 Vs appear in multiple places under the Challenges
category. Volume and variety are seen by name under the themeof Data structure. Variety is also implied in the same theme,
but listed as Incompatible formats, as well as Raw andunstructured datasets. Variety can also be inferred from thetheme of Data standardization, listed as Limited interoperability.
Velocity is seen in the theme Real-time analytics. Veracity isseen under the theme of Data Standardization, but listed as Dataacquisition and cleansing, Terminology, and Language barriers.
It is also inferred in the theme Inaccuracies listed as
Inconsistencies and Lack of precision.
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Data Structure Issues
Issues related to data structure were addressed in the majorityof the papers reviewed for this study. It is essential that the key
functions of data processing are supported by the applicationsof big data [13]. Big data applications should be user-friendly,transparent, and menu-driven [13,14]. The majority of data inhealth care is unstructured, such as from natural language
processing [12]. It is often fragmented, dispersed, and rarely
standardized [12,13,15-21]. It is no secret that the EHRs do notshare well across organizational lines, but with unstructured
data, even within the same organization, unstructured data is
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difficult to aggregate and analyze. It is no wonder that 61% of
the articles analyzed listed this as a concern; big data analytics
will need to address this large challenge.
Research data within the health care sector is moreheterogeneous than the research data produced within otherresearch fields [3,5,12]. Data from both research and publichealth is often produced in large volumes [15,22,23]. Anotherstructure-related issue results from the changing health carefee-for-service care model [4]. Finally, big data will need toaddress issues with the transparency of metadata [16,24].
Security Issues
There are considerable privacy concerns regarding the use of
big data analytics, specifically in health care given the enactment
of Health Insurance Portability and Accountability Act (HIPAA)
legislation [15]. Data that is made available on open source isfreely available and, hence, highly vulnerable [12,13,18,20].
Further, due to the sensitivity of health care data, there are
significant concerns related to confidentiality [25,26]. Moreover,this information is centralized, and as such, it is highly
vulnerable to attacks [25]. For these reasons, enabling privacy
and security is very important, as illustrated by a frequency ofmention in 50% of the literature reviewed.
Data Standardization Issues
Although the EHRs share data within the same organization,
intra-organizational, EHR platforms are fragmented, at best.
Data is stored in formats that are not compatible with allapplications and technologies [13,22]. This lack of data
standardization also causes problems in transfer of that data
[5,25]. It complicates data acquisition and cleansing [5,25,26].
About 39% of the literature mentioned this challenge.
Limited interoperability poses a large challenge for big data, as
data is rarely standardized [12,13,16,22]. This leaves big data
to face issues related to the acquisition and cleansing of data
into a standardized format to enable analysis and global sharing[13,17,23,25,27]. With globalization of data, big data will haveto deal with a variety of standards, barriers of language, anddifferent terminologies.
Storage and Transfers
Data generation is inexpensive compared with the storage andtransfer of the same. Once data is generated, the costs associatedwith securing and storing them remain high [25]. Costs are also
incurred with transferring data from one place to another as wellas analyzing it [14,21,22]. Some researchers have been able tocombine the themes of Data structure and Storage and transfers
when they illustrate how structured data can be easily stored,
queried, analyzed, and so forth, but unstructured data is not as
easily manipulated [13]. Cloud-based health information
technology has the additional layer of security associated withthe extraction, transformation, and loading of patient-related
data [27]. The use of big data should address issues related toincreased expenditures as well as the transmittance of secure
or insecure information. About 28% of the literature mentioned
this challenge.

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Managerial Issues
Data governance will need to move up on the priority list oforganizations, and it should be treated as a primary asset insteadof a by-product of the business [15]. Data ownership and data
stewardship should create new roles in business that consider
big data analytics [15], and new partnerships will need to bebrokered when sharing data [23,24,27]. About 14% of theliterature mentioned this point.
Lack of Appropriate Skills
It is important that health care workers are also kept up to datewith the use of constantly changing technology, techniques, anda constantly moving standard of care [5,24]. Due to the constantevolution of technology, there exist populations of individuals
lacking specific skills; as such this is also a significant
continuing barrier to the implementation of big data [12]. About
11% of the literature expressed this challenge.
Inaccuracies (Veracity)
Self-reported data is extensively used in health care, and so it
is crucial that the data collected in this manner be consistent[12]. Keeping information current as well as accurate is anotherchallenge of data collection. Precision of data is also needed toprovide accurate information [12]. Only 4% of the literature
mentioned this challenge.
Regulatory Compliance Issues
Health care organizations should be aware of the various legal
issues that can surface in the process of managing high volumeof sensitive information. Organizations implementing big data
analytics as a part of their information systems will have tocomply with a significant amount of standards and regulatorycompliance issues specific to health care [28]. Only 4% of theliterature mentioned this challenge.
Real-Time Analytics (Velocity)
One of the key requirements in health care is to be able to utilize
big data in real time. Real time is defined by enabling the useof applications such as cloud computing to view said data inreal time. The use of these technologies leads to issues of
security and privacy within patient information [12]. Only 4%of the literature mentioned this challenge. Challenges most often
mentioned or discussed were data structure (17/28, 61%),
security (14/28, 50%), data standardization (11/28, 39%), anddata storage and transfers (8/28, 29%). The other five challenges
comprised less than 15% of the observations.

Opportunities for Big Data in Health Care
Fourteen themes emerged under the category of opportunities:
improve quality of care, managing population health, early
detection of diseases, data quality, structure, and accessibility,
improve decision making, cost reduction, patient-centric care,enhances personalized medicine, globalization, fraud detection,and health-threat detection. Examples of each theme are listed
in Table 2. A total of 113 observations were made foropportunities.

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Table 2. Themes that emerged from the opportunities for big data in health care.
ThemesExamplesNumber of articles

Articles themes appeared in(n)
Improve quality of care

Improve efficiency% of total articles(N=28)

18

2, 4, 5, 6, 8-13, 18-20, 22-25,
27

17

2, 5, 8-10, 12-14, 16, 18-20, 23, 61%
25, 26, 28

172, 4, 5, 7-13, 15, 18-20, 23, 24, 61%28

162, 4, 6, 9, 11, 12, 16, 18, 20- 23, 57%
25-28

11

2,-4, 7, 9, 12, 16, 20, 22, 23, 24 39%

10

1, 3, 4, 7, 9, 11, 12, 14, 16, 18

36%

8

2, 3, 5, 12, 14, 20, 22, 24

29%

Improve outcomes

64%

Reduce waste
Reduce readmissions
Increased productivity and performance
Risk reduction
Process optimization
Managing population
healthManaging population health

Early detection of diseases Predicting epidemics
Disease monitoring
Health tracking
Adopt and track healthier behaviors
Predicting patient vulnerability
Improved treatments
Data quality, structure, and Large volumes
accessibility
Wide variety
Creating transparency
High-velocity capture
Access to primary data
Reusable data
Weed out unwanted data
Open source—free access
Improve decision making

Evidence-based medicine
New treatment guidelines
Accuracy in information

Cost reduction

Inexpensive
Reducing health care spending

Patient-centric health care Empowering patients
Patients making informed decisions
Increased communication
Enhancing personalized
medicine

Targeted approach6

4-6, 24, 25, 28

24%

Globalization

Widely accessible

62, 6-8, 10, 20

24%

Global sharing
Leveraging knowledge and practices
Knowledge dissemination
Fraud detection

Fraud detection

38, 12, 28

11%

Health-threat detection

Health-threat detection

1

7

4%

Despite the challenges that big data needs to overcome, theadvanced analytics that are promised through big data offer
tremendous opportunities for most stakeholders in the health
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care industry (patient, provider, and payer). More than 64% of
the articles analyzed focused on quality improvement and morethan 60% on managing population health and early detection
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of diseases through big data analytics. If even some of theopportunities of big data are realized, they can radically change
patient outcomes and the way decisions are made by providers,and help solve some macro-level issues related to health carewithin countries such as the United States (cost, quality, andaccess).
Improve Quality of CareBig data has the potential and ability to improve the quality andefficiency of care [5,15,23,29-31]. Big data offers an ability to
predict outcomes using the available primary or historical dataand provide proof of benefit that could change established,industry-wide standards of care [25,28]. Leveraging technology
at the patient end can also help with medication adherence[23,25]. This will most certainly play an important role in
improving outcomes [2,13] and improve the health-related
quality of life [20,26,32].
Quality of care will also be improved by reducing waste of
information, which will reduce inefficiencies [13,26]. This will
also assist in analyzing real-time resource utilization productivity[13]. Quality can also be improved by reducing the rates ofreadmissions, increasing operational efficiencies, and improving
performance [5,12,13]. About 64% of the literature mentioned
this opportunity.
Managing Population Health
The management of population health and the early detectionof diseases were topics that the authors thought would havehighly similar results after the analysis. Although there was alarge overlap between the 2 themes, there was also specificvariation between them. So, the researchers chose to keep themseparate. The theme of managing population health focused on
special populations rather than public health.
Big data analytics define populations at a finer level of
granularity than has ever been previously achieved [5,14,15,33].
It can help in managing the overall health of a population as
well as specific individual health [13,26,29]. Big data can enable
population health management from a local or global perspective[31,34]. This capability becomes more salient from the global
perspective when considering the aging of the population andage-related health issues shared by many populations andsubpopulations, many of which are underserved[17,19,21,24,28,32]. About 61% of the literature mentioned thisopportunity.
Early Detection of Diseases
Big data allows for the early detection of diseases, which aids
in clinical objectives related to achieving improved treatments
and higher patient outcomes [12,13,15,22,25]. It is in this areathat the authors found great promise in age-related illness anddisease. Along with early detection, big data analytics can also
help in the prevention of a wide range of deadly illnesses andpersonalized disease management and monitoring[5,19,21,22,29,34]. It enables providers to track healthy
behaviors and helps patients in monitoring their respective
conditions [25,32,33]. This capability holds great potential when
faced with either age-related diseases, or worldwide healthissues such as cardiology [16,22,28,31,34]. About 61% of theliterature mentioned this opportunity.
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Data Quality, Structure, and Accessibility
Literature suggests that big data enables rapid capture of data
and the conversion of primary, raw and unstructured data intomeaningful information [15,17,31,34]. New knowledge canthen be generated from high volumes of effective data, enabling
reuse of the data [15,20,21,32,33]. Open-source technology
increases accessibility to and transparency of the data
[12,25,26,30,35]. Finally, data quality can be maintained using
analytics to get rid of unnecessary information [27]. About 57%
of the literature mentioned this opportunity.
Improve Decision Making
Big data enables appropriate use of evidence-based medicine
and helps health care providers make more informed decisions[12,13,15,22]. This, in turn, improves the quality of careprovided to the patients [16,31,36]. Remote monitoring, patient
profile analytics, and genomic analytics are examples of otherapplications that influence the decision-making process [13,25].
Decision-making process can be highly optimized by theavailability of accurate and up-to-date information, as decision
making is influenced by the generation of new practices andtreatment guidelines within clinical research. Allowing big data
to influence decision making will allow for a faster and simpler
process. This is done by either supporting or replacing humandecision making. About 39% of the literature mentioned thisopportunity.
Cost Reduction
The literature suggests that the decrease in cost of the elementsof computing, such as storage and processing, leads to a decreasein the cost of data-intensive tasks [2,13]. This pass-through ofsavings will be seen across the spectrum of medicine [24,36]
and the health care workforce [25]. Savings will be realized
through more cost-effective treatments and monitoring to
improve medication adherence [25,31] and through the reductionof costly transportation costs, as is experienced in cardiology[12,17,22,34]. About 36% of the literature mentioned thisopportunity.
Patient-Centric Care
Increasing the use of technology is slowly changing the directionof the health care sector from disease-centric care toward
patient-centric care [5]. Big data will play a significant role inthis transformation [37]. It will allow the information to bedelivered to patients directly and empower them to play anactive part in their care [5,15,27]. When patients are provided
with the appropriate information, it will influence their decision
making and allow them to make informed decisions [13,24].
Informed decisions will also be influenced by increased
communication between patients, providers, as well as theircommunities [5,24,32,36]. About 29% of the literature
mentioned this opportunity.
Enhancing Personalized Medicine
With the use of big data, the objectives of personalized medicine
can be translated into clinical practice [5,25,30]. Access to andprocessing of large volumes of data should enable a personalized
patient-specific record of risks of disease [25,29,32]. Big data

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applications aim to make this process more efficient [12]. About
24% of the literature mentioned this opportunity.
Globalization
Big data will actively help in disseminating the knowledge
acquired from the data collected [15,22,30]. Big data plays anactive role in leveraging the practices and knowledge not only
regionally but globally [12,15,29]. By globalizing data, it ismade more widely accessible and providers may access new
information from all regions [22,23,32]. About 24% of theliterature mentioned this opportunity.
Fraud Detection
One of the most significant benefits offered by big data is that
it is instrumental in detecting fraud in an efficient and effective
manner [13,23]. For example, the unauthorized use of specificuser accounts by third parties can be minimized [21]. Only about
11% of the literature mentioned this opportunity.
Health-Threat Detection
Big data offers opportunity for improving capabilities of threat
detection quickly and more accurately. This can be especiallybeneficial for government use [22]. Big data augments thecurrent acquisition of protection against the increasing threatsof foreign countries, criminals, terrorists, and others. Only 3.6%of the literature mentioned this opportunity.
Opportunities most often mentioned or discussed were improve
quality of care (18/28, 64%), managing population health (17/28,61%), early detection of diseases (17/28, 60.7%), data quality
structure and accessibility (16/28, 57%), improve decision
making (11/28, 39.3%), cost reductions (10/28, 36%),
patient-centric health care (8/28, 29%), enhancing personalized
medicine (6/28, 24%), and globalization (6/28, 24%). The othertwo opportunities each comprised less than 15% of theobservations.

Discussion
Summary of Evidence
Although the integration of big data is well underway inindustries such as finance and advertising, it has not yet fully
assimilated into health care. Challenges and opportunities weremade quite clear in the articles analyzed in this review. Threeof the 4 Vs (volume, velocity, and variety) were consistently
adhered to. The fourth V, veracity, was found, but rarely listed
by name. Tables 1 and 2 provide insightful information that is

Kruse et alpreviously unpublished. These tables identify challenges andopportunities and illustrate their frequency of mention in theliterature. This information is helpful to other researchers andinnovators because it provides direction and proper emphasisof research effort. The listed challenges and opportunities areordered by their frequency found in the literature.

Limitations
A big limitation in this review is the low number of articles
used in the analysis. If we were to do this over again, we would
query another database to see whether additional articles wereavailable for analysis.
Selection bias seems to exist in any study. Our control for
selection bias was the initial research up front to agree on adefinitive definition of the concept of big data, and ourconsensus meetings to discuss findings. The consensus meetings
offered great value to the process because they enabled thegroup to hear the focus of an individual and either provide
feedback to confirm the focus or agree that the unique focus
was warranted for all the articles in the review.
Another bias that we discuss regularly is publication bias.
Journals tend to publish results that are statistically significant,which inherently limits the publication of research that may notreach that level. Our control for publication bias was to include
Google Scholar in our search. Our intent was to identify material
in lesser-known journals that might not be indexed in PubMed(MEDLINE) or CINAHL.

Conclusions
Big data and the use of advanced analytics have the potential
to advance the way in which providers leverage technology tomake informed clinical decisions. However, the vast amounts
of information generated annually within health care must beorganized and compartmentalized to enable universal
accessibility and transparency between health care organizations.
Our systematic literature review revealed both challenges andopportunities that big data offers to the health care industry.
The literature mentioned the challenges of data structure andsecurity in at least 50% of the articles reviewed. The literature
also mentioned the opportunities of increased quality, better
management of population health, early detection of disease,and data quality structure and accessibility in at least 50% of
the articles reviewed. These findings identify foci for future
research.

Conflicts of Interest
None declared.

Multimedia Appendix 1
Summary or relevance of cited work.
[PDF File (Adobe PDF File), 33KB-Multimedia Appendix 1]

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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

Edited by G Eysenbach; submitted 19.11.15; peer-reviewed by J Bian, D Maslove, MA Mayer, S Seevanayanagam, L Toldo; comments
to author 03.01.16; revised version received 27.07.16; accepted 28.09.16; published 21.11.16
Please cite as:
Kruse CS, Goswamy R, Raval Y, Marawi S
Challenges and Opportunities of Big Data in Health Care: A Systematic Review
JMIR Med Inform 2016;4(4):e38
URL: http://medinform.jmir.org/2016/4/e38/
doi: 10.2196/medinform.5359
PMID: 27872036©Clemens Scott Kruse, Rishi Goswamy, Yesha Raval, Sarah Marawi. Originally published in JMIR Medical Informatics(http://medinform.jmir.org), 21.11.2016. This is an open-access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction
in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete

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