JMIR MEDICAL INFORMATICS

Wang & Krishnan

Review

Big Data and Clinicians: A Review on the State of the Science
Weiqi Wang, PhD; Eswar Krishnan, MD, MPH
School of Medicine, Stanford University, Palo Alto, CA, United States

Corresponding Author:
Eswar Krishnan, MD, MPH
School of Medicine
Stanford University
1000 Welch Road, Suite 203
Palo Alto, CA, 94304
United States
Phone: 1 650 725 8004
Fax: 1 650 723 9656
Email: e.krishnan@stanford.edu

Abstract
Background: In the past few decades, medically related data collection saw a huge increase, referred to as big data. These hugedatasets bring challenges in storage, processing, and analysis. In clinical medicine, big data is expected to play an important rolein identifying causality of patient symptoms, in predicting hazards of disease incidence or reoccurrence, and in improving
primary-care quality.
Objective: The objective of this review was to provide an overview of the features of clinical big data, describe a few commonly
employed computational algorithms, statistical methods, and software toolkits for data manipulation and analysis, and discuss
the challenges and limitations in this realm.
Methods: We conducted a literature review to identify studies on big data in medicine, especially clinical medicine. We useddifferent combinations of keywords to search PubMed, Science Direct, Web of Knowledge, and Google Scholar for literature ofinterest from the past 10 years.
Results: This paper reviewed studies that analyzed clinical big data and discussed issues related to storage and analysis of thistype of data.
Conclusions: Big data is becoming a common feature of biological and clinical studies. Researchers who use clinical big data
face multiple challenges, and the data itself has limitations. It is imperative that methodologies for data analysis keep pace with
our ability to collect and store data.
(JMIR Med Inform 2014;2(1):e1) doi: 10.2196/medinform.2913
KEYWORDS
big data; database; medical informatics; clinical research; medicine

Introduction
Big data refers to very large datasets with complex structuresthat are difficult to process using traditional methods and tools.
The term process includes, capture, storage, formatting,extraction, curation, integration, analysis, and visualization[1-9]. A popular definition of big data is the “3V” model
proposed by Gartner [10], which attributes three fundamental
features to big data: high volume of data mass, high velocity ofdata flow, and high variety of data types. The notion of big data
can be traced back to the 1970s [11-13] when scientists realizedthat they lacked the tools to analyze datasets of large size. In
those days, big data was merely several to hundreds of
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megabytes [14]; now datasets of terabytes are common [15, 16].
Therefore, the “big” in big data reflects the limits of data storage
and computational power existing at a given point in time.
Table 1 shows the growth of global big data volume andcomputer science papers on big data since 2009. This table
exemplifies that stored data will be in the tens of zettabytes
range by 2020, and research on how to deal with big data willgrow exponentially as well.
Big data is gathered in many disciplines and is made possible
by ubiquitous information-sensing devices and software [19].
An example is web logs: websites such as Google or Facebook
automatically record user information at each visit. OtherJMIR Med Inform 2014 | vol. 2 | iss. 1 | e1 | p. 1
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examples come from the stock market [20], earthquake
surveillance [21], political elections [22], behavioral studies[23], sports [24], pharmaceutical reports [25], health care [26,27], electronic medical records [28], imaging data [29], genome
data [30, 31], and entrepreneur transaction records [32]. Data
collection is sometimes interdisciplinary. As an example, asudden increase in Google search terms such as “flu symptoms”
and “flu treatments” can be used to predict an increase in flu
patients presenting to hospital emergency rooms [33]. This
example also demonstrates that big data has promising predictive
power and return on investment. Return on investment of big
data has also been suggested for clinical big data [34, 35].
Although arguably valuable, big data is difficult to analyze dueto the massive volume of the raw data and its diversity, as shownin Figure 1. Therefore, instead of the raw big data, a large dataset
is often extracted from the raw data to generate a secondary
storage of data for analysis purposes. This data extraction isapplied, for example, when a computer tomography scan is
involved in clinical trials and only the physician diagnosis basedon the scan is included in data analysis. Similarly, a large
volume of descriptive data on various kinds of samplings, tests,
or assays can be extracted with only the key parameters kept.
As a consequence, the data analyzed in clinical medicine isusually from secondary datasets that contain only data ofinterest. The secondary datasets, although still large, are notterabytes in size. Additionally, due to the nature of clinical trials,
a large dataset in clinical medicine usually does not have an
overwhelming number of samples. Kjaergard et al [36] defined
clinical trials with 1000 or more participants as large, and thestudies in clinical medicine titled big/large, data/dataset
generally have thousands of attributes, but only hundreds ofsamples [37-39].
For this paper, we reviewed the literature to determine thefeatures of clinical big data and determine the methods used for
manipulation and analysis of these data. This paper is focusedon clinical medicine rather than general health care issues;
therefore, we mainly reviewed the studies that appeared relevant
to clinicians. We examined the selected studies to extract
information on research interests, goals, and achievements, andthe implemented methodologies. Our intention was not to
conduct an exhaustive systematic review, but instead to enable
a literature-based discussion of how the big data issue has beenaddressed in clinical medicine. Based on our findings, we
discuss the challenges and limitations of analysis of large clinicaldatasets.

Table 1. Global growth of big data and computer science papers on big data.

a

Year

Data volume, ZBa,cConference papers, CSb,cJournal papers, CSc

2009

1.5

12

7

2010

2

26

7

2011

2.5

32

23

2012

3

78

47

2015

8

?

?

2020

44

??

??

Data from oracle [17].

bData from Research Trends [18].

c

CS, computer science; ZB, zettabytes (1 zettabyte = 1000 terabytes = 106 petabytes = 1018 gigabytes, GB).

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Figure 1. A schematic of the issues surrounding storage and use of big data. Clinical big data, as well as big data in other disciplines, have beensurrounded by a number of issues and challenges, including (but not limited to): generation, storage, curation, extraction, integration, analysis, visualization,etc. ANN: artificial neuron network; EMR: electronic medical record; MPP: massively parallel-processing; PCA: principle component analysis; ROI:
return of investment; SVM: support vector machine.

Methods
We conducted a literature review to identify studies on big data
in medicine, especially clinical medicine. We used different
combinations of keywords to search PubMed, Science Direct,
Web of Knowledge, and Google Scholar for literature of interest,
mainly from the last 10 years. The key words were: "big data
medicine", "large dataset medicine", “clinical big data”, “clinicallarge dataset”, “clinical data warehouse”, “clinical database”,“clinical data mining”, “biomedical big data”, “biomedical
database”, “biomedical data warehouse”, “healthcare big data”,“healthcare database”, and “healthcare data warehouse”.

Results
Big Data in Clinical Medicine
Big data plays an important role in medical and clinical researchand has been leveraged in clinically relevant studies. Major
research institute centers and funding agencies have made large
investments in the arena. For example, the National Institutesof Health recently committed US $100 million for the big data
to Knowledge (BD2K) initiative [40]. The BD2K defines“biomedical” big data as large datasets generated by researchgroups or individual investigators and as large datasets generated
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RenderXby aggregation of smaller datasets. The most well-known
examples of medical big data are databases maintained by the
Medicare and Healthcare Cost and Utilization Project (withover 100 million observations). One of the differences between
medical big data and large datasets from other disciplines isthat clinical big data are often collected based on protocols (ie,fixed forms) and therefore are relatively structured, partially
due to the extraction process that simplify raw data as mentioned
above. This feature can be traced back to the Framingham Heart
Study [41], which has followed a cohort in the town of
Framingham, Massachusetts since 1948. Vast amounts of data
have been collected through the Framingham Heart Study, andthe analysis has informed our understanding of heart diseases,
including the effects of diet, exercise, medications, and obesity
on risk [42]. There are many other clinical databases withdifferent scopes, including but not limited to, prevalence andtrend studies, risk factor studies, and genotype-phenotype
studies.

Prevalence and Trend Studies
One of the major uses for clinical big data is in analysis of theprevalence or trends of a disease or phenotype among different
populations. An early big data study evaluated a cohort
consisting of 890,394 US veterans with diabetes followed from2002 through 2006 [43]. Bermejo-Sanchez et al [44] observed
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326 of the birth defect Amelia among 23 million live births,
stillbirths, and fetal anomalies from 23 countries and 4
continents, and found the trend of higher prevalence of Amelia
among younger mothers. Histological features that differ
between chronic idiopathic inflammatory bowel disease andnormality and between Crohn’s disease and ulcerative colitis
were identified in 809 large bowel endoscopic biopsies [45].
Kelly et al [46] studied the prevalence of hip abnormalities of8192 subjects with hemophilia A and B. Siregar et al [47]performed a population-based study on patients after cardiac
surgery in all 16 cardiothoracic surgery centers in the
Netherlands. Elshazly et al [48] examined 1.3 million US adults
for patient-level discordance of non-high-density lipoproteincholesterol and low-density lipoprotein cholesterol. Chan and
McGarey [49] summarize how large datasets can be analyzed
to achieve population-based conclusions, specifically for
determination of secular trends, health disparities, geographic
variation, and evaluation of specific diseases and treatments.
This paper also summarized the strengths and limitations oflarge-sized datasets and addressed issues such as missing data
and bias. These issues will also be discussed in brief below.

Risk Factor Studies
Clinical big data can also be used to determine causality, effect,or association between risk factors and the disease of interest.
Ursum et al [50] examined the relationships between
seroconversion and patient age with inflammatory effects ofautoantibodies in 18,658 rheumatoid arthritis patients andcontrols, and showed that citrullinated proteins and peptides
were more reliable markers for rheumatoid arthritis than was
Immunoglobulin M rheumatoid factor. Ajdacic-Gross et al [51]examined the data on 11,905 Swiss conscripts from 2003 forstuttering and found that there was no single overwhelming risk
factor for stuttering, although premature birth and parental
alcohol abuse appeared influential. Data collected on 14,433
patients from the 155 Veterans Administration medical centersin all 50 US states, Puerto Rico, and the District of Columbia
were used to identify the alcohol dependence of medications[52]. By analysis of 53,177 cases of contrast administration in
35,922 patients from the Radiology and Cardiac Catheterization
Laboratory databases, an increase in contrast nephropathy was
associated with use of sodium bicarbonate [53].
Echocardiography and electrocardiogram-gated single-photon
emission computed tomography traces for the evaluation of left
ventricular ejection fraction were compared in 534 patients [54].
Zhang et al [55] examined clinical data of 16,135 adult patientsand elucidated the relationships between glycemic, blood
glucose level, and intake of insulin with mortality. Mitchel etal [56] studied the effect of 2 types of insulin on 7720 patientsselected from 8 million in UK. Kobayashi et al [57] analyzed
19,070 records on right hemicolectomy from 3500 Japanesehospitals and successfully developed a risk model. It should be
noted that in these studies, the terms of “association” and“causality” must be rigorously distinguished; most of the studies
claimed association, whereas causality was rarely asserted.

Genotype–Phenotype Studies
With the advancement of genotyping technology, an increasing
amount of risk-factor studies have attempted to assess
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Wang & Krishnan
association on the genetic level through evaluation of gene
expression and/or genomic data obtained from patients andcontrols. For example, clinical and genetic data from 5700
patients who had been treated with warfarin were used to create
an algorithm to estimate the appropriate dose [58]. Causalityof autism spectrum disorders has been investigated by analysisof 31,516 clinical cases on copy number variation in patients
versus 13,696 controls [59]. Koefoed et al [60] made efforts toassess the effects of signal transmission and calculated all
combinations of three genotypes from 803 single-nucleotide
polymorphism (SNP) genotypes (2.3 billion combinations) for
1355 controls and 607 patients with bipolar disorder. Thesestudies are similar to risk-factor studies, yet often the big data
is significantly larger in volume due in genetic analyses than in
risk-factor studies.

Method Development Studies
A number of studies have taken advantage of clinical big data
to establish new methods or techniques, or to develop new tools
to enable analysis of data and decision making. In a typicalexample, Hill et al [61] designed an interface to use clinicaldata to evaluate risk ratios for various diseases to aid in
evaluation of treatment options. Liu et al [62, 63] have usedlarge-scale data analysis to optimize diagnosis of breast cancerfrom full-field digital mammography images. Lin et al [64]made efforts to formalize the phenotype variable in the database
Genotypes and Phenotypes. Stephen et al [65] developed analgorithm to categorize pediatric patients presenting withrespiratory distress into different subtypes using clinicalvariables from a clinical data warehouse. Clinical data
warehouses or databases have been created from radiotherapy
clinical trial data [66], gene mutations [67], cancer patient data
[68, 69], kidney disease patient data [70], and gastrointestinal
surgery patient data [71]. Additionally, studies have focused on
personalized big data [72], citizen-centric health care versus
patient-centric health care [72, 73], medication orders [74, 75],
and decision making and information management/retrieval ingeneral [75-80]. The dramatic increase in the number of studies
with large scope in the past few years indicates an increasing
desire of researchers to manipulate clinical big data; “bigdata-assisted clinics” may be expected in the near future.

Discussion
Diversity of Data in Clinical Medicine
The huge body of medical research that has been performed
using large datasets demonstrates the broad spectrum of data
resources used and shows that the structure of the medical
dataset depends on the research question. Data from different
subareas of medical research have broad diversity in terms ofnumbers of entries, types of data stored (or levels),dimensionality, and sample size [81]. Datasets obviously differ
greatly in size: gene expression datasets derived fromhigh-throughput microarray and next-generation sequencing
technologies, such as those that analyze SNPs and copy number
variations, tend to be massive, whereas clinical trial dataset are
not as big. Phan et al [82] suggested that data in medicine be
divided into four different levels: the molecular level (eg,genome data), cellular and tissue level (eg, stem cell
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differentiation data), clinical and patient level (eg, clinical trialdata), and biomedical knowledge base level (ie, a comprehensive
data collection). Additionally, data tend to have different levelsof dimensionality (ie, number of attributes or parameters, p)and sample sizes (ie, number of records/entries, n). Typicaldatasets fall into one of three categories, as summarized by
Sinha et al [83]: large n, small p; small n, large p; and large n,large p. Thanks to advancements in computational technology,
most algorithms can handle low-dimensional data (ie, large n,
small p) without encountering significant difficulty.

in this regard include the use of heat maps [93], gene alignments[94], protein structure visualization [95], scatterplot matrix, tree
visualization, network visualization, parallel coordinates, stacked
graphs, etc. When the big data of interest are scattered or stored
at different resources, data integration [96, 97] and federation[98] is an important phase during data preprocessing.
Approaches such as the Information Manifold [97], which allows
browsing and querying of multiple networked information
sources, can provide solutions to uncertainty problems after
data integration and mapping [99].

Most clinical data, however, is high-dimensional (ie, small n,large p or large n, large p) due to a limited number of patients.
One typical example comes from a study of 69 Broca’s aphasic
patients (ie, n=69) who were tested with nearly 6000 stimulus
sentences (ie, p~6000) [84]. With similar dimensionality,
Mitchell et al [39] studied bipolar disorder where the sample
consisted of only 217 patients. For high-dimensional data, eachpoint, sample, or element is described by many attributes [83]with the involvement of the “curse of dimensionality” [85].
Because high-dimensional data are sparse in dimensions, mostclassification or clustering approaches do not work well because
the increase in problem space reduces the overall density ofdata samples. To solve this problem, compression methods andsignificance testing are usually used to either reduce thedimensionality or select relevant features before data analysis
by some sort of data preprocessing [83].

Statistical Approaches to Data Analysis

Methods for Manipulation of Clinical Big Data
Technologies for Data Storage and Handling
Due to the massiveness and complexity of big data, nonrelationaland distributed databases such as Apache Hadoop [86], Google
BigTable [87], NoSQL [88], and massively parallel-processing
databases are used rather than traditional relational databases
to store data. A large number of biostatistics software packages
have been used to handle large clinical datasets, some of whichenabled the features of cloud-based or distributed computing.
Popular software packages include, but are not limited to, SAS[36, 51-53], Mplus [51], SPSS [36, 39, 45], PP-VLAM [89],Stata [90], and R [91]. These technologies and tools greatly
facilitate the handling of big data.

Methodologies for Data Preprocessing
Clinical raw big data can be highly diverse and uninformative
without preprocessing. Extraction of a diagnosis from raw
computer tomography data is an example of one of the
predominant manners in which clinical big data are
preprocessed. This type of processes relies on a specialist’s
personal expertise and can be a source of bias. Most early
analyses of big data, including that collected by the Framingham
Heart Study adopted some form of preprocessing; therefore,challenges exist in curation [6]. As an alternative to expert
preprocessing, computational algorithms or statistical
approaches, including compression methods, significancetesting, or normalization [92] can be implemented to preprocess
raw big data. This methodology may also introduce bias andcan cause uncertainty problems during data integration.

A number of popular statistical methods have been implemented
in clinical data analysis. The most common include linear
regression and logistic regression [30], latent class analysis[100], principle component analysis [101], and classification
and regression trees [100]. Additionally, logarithmic andsquare-root transformations [58], naive Bayes methods [102],
decision trees [103], neural networks [104], support vector
machines [105], and hidden Markov models [83] are also used
to study problems in medical data.
When a dataset is not overly complicated, a single test (eg, asimple Student’s t test) should be powerful enough to reject anull hypothesis, and single hypothesis testing is the methodology
to adopt [106]. Sometimes one cannot establish the significanceof a hypothesis until different statistical tests have been applied
to the same dataset. Multiple testing is often used to identify
correlations that deserve further investigation [107]. Algorithms
for false discovery rate [108] and family-wise error rate [109]calculation have been implemented for multiple testing in studies
on gene expression data and datasets with similar levels ofcomplexity.

Challenges and Limitations of Use of Clinical Big Data
Overview
Big data itself has many limitations. These limitations include“adequacy, accuracy, completeness, nature of the reporting
sources, and other measures of the quality of the data”, assummarized previously [110]. The consequences of theselimitations are succinctly summarized in the book titled“Models. Behaving. Badly.” [111]. Modeling can often lead toa biased statistical correlation or inference, sometimes known
as a “false discovery”. Clinical big data users face a large
spectrum of challenges, including but not limited to sample size,
selection bias, interpretation problem, missing values,
dependence problems, and data handling methodologies.

Sample Size
One of the counterintuitive challenges in analysis of big dataclinical datasets is that sometimes the sample size is not as big,compared with the number of attributes to allow statisticallysignificant analysis. Population survey methods are sometimes
adopted because these methods can provide larger datasets.
However, the authenticity and accuracy of this type of data arearguably limited; hence, survey methods cannot be reliably used
to produce an adequate description or prediction [39].

In some scenarios, visualization can be a part of data
preprocessing (as well as result exhibition). Typical examples
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Selection Bias
Any dataset is a selection of data rather than the whole dataworld; therefore, selection bias is a very real limitation [112]even if the sample size is big. In that sense, all studies of clinicaldata have this limitation to some degree [39].

Interpretation Problem
Gebregziabher et al [43] stated that the datasets generated
through many translational research projects to answer questionsof public health interest are not self-explanatory due tocomplexity and inadequate description/documentation of thedataset's parameters and associated metadata. The methodologies
for interpreting the data can therefore be subject to all sorts ofphilosophical debate. For example, the data may not be totallynaïve or objective and interpretation may be biased by subjective
assumptions and/or manipulations by individual analysts.

Missing Values
It is common problem that large datasets have missing values,
and in many cases the problem can be significant [44]. A typicalexample is the Framingham Heart Study where data on serum
uric acid are largely missing. Additionally, the covariates (ie,attributes) may not fully capture the degree of risk for patientsand may cause uncertainty in results [53].

Dependence Problems
One issue that has been often neglected is the dependence ofdata. Dependence between either attributes or samples indatasets can cause the degrees of freedom to decrease and/or
some statistical principles to no longer apply. Examples of thisWang & Krishnan
are found when the same patients are evaluated multiple times
through follow-up and when correlations in gene expression
are drawn based on samples from different patients treated withsimilar medications [83]. As many statistical methods do notaccount for dependence, results from these tests may beunreliable if this issue is not properly addressed before the data
analysis.

Data Handling Methodologies
Effective processing of big data has always been a challenge.
One must consider all the aspects of the dataset, includingcollection, curation, extraction, integration, interpretation,
imputation, and selection of appropriate statistical methods,
during processing and analysis. It has been claimed that analysesof large datasets are often suboptimal due to the researcher’slack of knowledge of the available tools and methodologies[83]. On the other hand, algorithms to handle big data are also
underdeveloped to some extent and deserve more attention[113].

Conclusions
This paper reviewed studies that analyzed clinical big data andthat discuss issues related to data storage and analysis. Big data
is becoming a common feature of biological and clinical studies.
Today, a single biophysical researcher can generate terabytes
of data in hours. Over the last decade, clinical datasets havegrown incredibly in size, mostly due to use of modern
technologies for collection and recording of data. Researchers
who use clinical big data face multiple challenges, and the data
itself has limitations. It is imperative that methodologies fordata analysis keep pace with our ability to collect and store data.

Authors' Contributions
WW did the search and primary review of the literature cited in this article and wrote the manuscript. EK guided the researchand critically revised the manuscript.

Conflicts of Interest
None declared.

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Abbreviations
BD2K: Big Data to Knowledge
CS: computer science
SNP: single-nucleotide polymorphism
ZB: zettabytes

Edited by G Eysenbach; submitted 27.08.13; peer-reviewed by L Toldo, J Gao; comments to author 27.10.13; revised version received
25.11.13; accepted 08.12.13; published 17.01.14
Please cite as:
Wang W, Krishnan E
Big Data and Clinicians: A Review on the State of the Science
JMIR Med Inform 2014;2(1):e1
URL: http://www.medinform.jmir.org/2014/1/e1/
doi: 10.2196/medinform.2913
PMID: 25600256©Weiqi Wang, Eswar Krishnan. Originally published in JMIR Research Protocols (http://medinform.jmir.org), 17.01.2014. Thisis 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 Research Protocols, is properly cited. The complete bibliographic information,a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

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