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Current Research in Medicine

Review Articles

Big Data in Medical Applications and Health Care

Lidong Wang and

Cheryl Ann Alexander

Department of Engineering Technology, Mississippi Valley State University, USA

Department of Nursing, University of Phoenix, USA

Article history

Received: 31-3-2015

Revised: 30-4-2015

Accepted: 4-5-2015

Corresponding Author:

Lidong Wang

Department of Engineering

Technology, Mississippi Valley

State University, USA

Email: lwang22@students.tntech.edu

Abstract: Big Data can unify all patient related data to get a 360-degree

view of the patient to analyze and predict outcomes. It can improve clinical

practices, new drug development and health care financing process. It

offers a lot of benefits such as early disease detection, fraud detection and

better healthcare quality and efficiency. This paper introduces the Big Data

concept and characteristics, health care data and some major issues of Big

Data. These issues include Big Data benefits, its applications and

opportunities in medical areas and health care. Methods and technology

progress about Big Data are presented in this study. Big Data challenges in

medical applications and health care are also discussed.

Keywords: Big Data, Health Care, Medicine, Diagnosis, Tele-diagnosis,

Privacy, Monitoring, Clinical Practice, Personalized Patient Care, Fraud

Detection, E-Consultation, Big Data Analytics, Data Mining, Machine

Learning, Hadoop, Smart Health, Information Security

Introduction

Big Data Concept and Characteristics

Big data is the data that exceeds the processing

capacity of conventional database systems. The data is

too big, moves too fast, or doesn’t fit the strictures of

conventional database architectures (Dumbill, 2013). Big

data characteristics can be described by “6Vs”. They are:

Volume, Velocity, Variety, Value, Variability and

Veracity (Russom, 2011; Eaton et al., 2012; O’Reilly

Radar Team, 2012; Zikopoulos et al., 2012; Bellini et al.,

2013; Demchenko et al., 2013; Megahed and Jones-

Farmer, 2013; Minelli et al., 2013; Rajpathak and

Narsingpurkar, 2013):

• Volume: This means data size such as Terabytes

(TB: Approximately 10

bytes), Petabytes (PB:

Approximately 10

bytes) and Zettabytes (ZB:

Approximately 10

bytes), etc

• Velocity: Data is generated at a high speed

• Variety: This represents all types of data such as

structured data from relational tables, semi-

structured data from key-value web clicks and

unstructured data from email messages, articles and

streamed video and audio, etc

• Value: It is defined by the added-value that the

collected data can bring. It refers to the value that

the data adds to creating knowledge. There is some

valuable information somewhere within the data

• Variability: It refers to data changes during

processing and lifecycle. Increasing variety and

variability also increases the attractiveness of data

and the potentiality in providing unexpected, hidden

and valuable information

• Veracity: It includes two aspects: Data consistency

(or certainty) and data trustworthiness. Data can be

in doubt: incompleteness, ambiguities, deception

and uncertainty due to data inconsistency, etc

There is often noisy data or false information in big

data. The focus of Big Data is on correlations, not causality

(Bottles and Begoli, 2014). In addition, the data we consider

big today may not be considered big tomorrow because of

the advances in data processing, storage and other system

capabilities (Zaslavsky et al., 2012).

Health Care Data

Big data in healthcare refers to electronic health data

sets so large and complex that it is difficult to manage with

traditional or common data management methods and

traditional software and/or hardware (Priyanka and

Kulennavar, 2014). Some health care data are characterized

by a need for timeliness; for example, data generated by

wearable or implantable biometric sensors; blood pressure,

or heart rate is often required to be collected and analyzed

in real-time (Helm-Murtagh, 2014).

Data in healthcare can be categorized as follows:

Lidong Wang and Cheryl Ann Alexander / Current Research in Medicine 2015, 6 (1): 1.8

DOI: 10.3844/amjsp.2015.1.8

Genomic Data

It refers to genotyping, gene expression and DNA

sequence (Chen et al., 2012; Priyanka and Kulennavar,

2014).

Clinical Data and Clinical Notes

About 80% of this type data are unstructured

documents, images and clinical or transcribed notes

(Yang et al., 2014):

• Structured data (e.g., laboratory data, structured

EMR/HER)

• Unstructured data (e.g., post-op notes, diagnostic

testing reports, patient discharge summaries,

unstructured EMR/HER and medical images such as

radiological images and X-ray images)

• Semi-structured data (e.g., copy-paste from other

structure source)

Behavior Data and Patient Sentiment Data

• Web and social media data

Search engines, Internet consumer use and

networking sites (Facebook, Twitter, Linkedin,

blog, health plan websites and smartphone, etc.)

(Terry, 2013)

• Mobility sensor data or streamed data (data in

motion, e.g., electroencephalographydata)

They are from regular medical monitoring and

home monitoring, telehealth, sensor-based wireless

and smart devices (Shrestha, 2014)

Health Publication and Clinical Reference Data

Text-based publications (journals articles, clinical

research and medical reference material) and clinical

text-based reference practice guidelines and health

product (e.g., drug information) data (Miller, 2012;

Priyanka and Kulennavar, 2014).

Administrative, Business and External Data

• Insurance claims and related financial data, billing

and scheduling (Terry, 2013)

• Biometric data: Fingerprints, handwriting and iris

scans, etc

Other Important Data

• Device data, adverse events and patient feedback,

etc. (Yang et al., 2014)

• The content from portal or Personal Health Records

(PHR) messaging (such as e-mails) between the

patient and the provider team; the data generated in

the PHR

Benefits of Big Data in Medical Applications

and Health Care

Effective large-scale analysis often requires the

collection of heterogeneous data from multiple sources.

For example, obtaining the 360-degrees health view of a

patient(or a population) benefits from integrating and

analyzing the medical health record along with Internet

available environmental data and then even with

readings from multiple types of meters (for example,

glucose meters, heart meters, accelerometers, among

others) (Jagadish et al., 2014).

Applying advanced analytics to patient profiles,

characteristics and the cost and outcomes of care can

help identify the most clinically and cost effective

treatments, proactively identify individuals who would

benefit from preventative care or lifestyle changes. Big

Data could help reduce waste and inefficiency in the

following three areas (Manyika et al., 2011):

• Clinical operations: Determine more clinically relevant

and cost-effective ways to diagnose and treat patients

• Research & development: (1) lower attrition and

produce a leaner, faster, moretargeted R&D

pipeline in drugs and devices with the help of

predictive modeling; (2) improve clinicaltrial

design, thus reducing trial failures and speeding

new treatments to market; and (3) identify follow-

on indications and discover adverse effects before

products reach the market

• Public health: (1) analyze disease patterns and track

disease outbreaks and transmission to improve

public health surveillance and speed response; (2)

faster develop more accurately targeted vaccines and

(3) turn large amounts of data into actionable

information

Big Data benefits in medical applications and health

care can be summarized as follows (Helm-Murtagh,

2014; Raghupathi and Raghupathi, 2014): (1)

Improvement of health outcomes through more accurate

and precise diagnoses; identification of patients who are

at risk of adverse outcomes; and customization of care at

the level of the individual patient (personalized

medicine). (2) Reduction of costs through earlier

detection of disease; elimination of unnecessary and

duplicate care; reduction in variations in care; and

elimination of erroneous and improper claims

submissions. (3) Predicting and managing obesity and

health risks; detecting health care fraud more quickly and

efficiently (Certain developments or outcomes may be

predicted and/or estimated based on vast amounts of

historical data). (4) Decreasing inappropriate Emergency

Department (ED) utilization by using statistical models to

identify the best ED services or care alternatives that are

more appropriate, more convenient and lower in cost

according to health conditions, prior use of health care

Lidong Wang and Cheryl Ann Alexander / Current Research in Medicine 2015, 6 (1): 1.8

DOI: 10.3844/amjsp.2015.1.8

resources (e.g., having a primary care provider) and

proximity to sites of care. (5) Providing advantages to

Health Informatics. This is fulfilled by allowing for more

tests cases or more features for research, leading to both

quicker validation of studies and the ability to accrue

enough instances for training. Big Data approaches have

been used for the analysis of Health Informatics data

gathered at multiple levels, including the molecular, tissue,

patient and population levels. The amount of data produced

within Health Informatics has grown to be quite vast. Big

Data analytics grants potentially great possibilities to gain

much knowledge in Health Informatics.

Applications and Opportunities of Big Data

in Medical Applications and Health Care

Big Data can provide support across all aspects of

health care. Big Data analytics has gained traction in

genomics, epidemic spread prediction, clinical outcome,

fraud detection, pharmaceutical development and

personalized patient care, etc. There are potential

applications in these areas. The specific applications of

Big Data in the areas are as follows.

Genomics Analytics

Genomic data is becoming critical to the complete

patient record. Combining patient genomic data with

clinical data helps cancer treatment (Chen et al., 2012;

Priyanka and Kulennavar, 2014).

Flu Outbreak Prediction and Control

In public and population health, continuously

aggregating and analyzing public health data helps

detect and manage potential disease out breaks. Big

Data analytics can mine web-based and social media

data topredict flu outbreaks based on consumer

search, social content and query activity (Priyanka

and Kulennavar, 2014).

Clinical Outcome Analytics

Clinical analytics can be performed through unifying

clinical, financial and operation data for efficient clinical

decisions. Blue Cross and Blue Shield of North Carolina,

USA has provided several promising examples of how

Big Data can be used to reduce the cost of care, predict

and manage health risks and improve clinical outcomes

(Helm-Murtagh, 2014).

Fraud Detection and Prevention

Identifying, predicting and minimizing fraud can be

implemented by using advanced analytic systems for

fraud detection and checking the accuracy and

consistency of claims. Big Data predictive modeling can

be used by health care payers for fraud prevention. Fraud

waste and abuse analytics can be performed in analyzing

claims and benefits of Veterans benefits and education

fraud (White, 2014; Raghupathi and Raghupathi, 2014).

Medical Device Design and Manufacturing

Big Data tools enable a broader set of anatomical

configurations, device materials, delivery methods and

tissue interactions to be evaluated. Computational methods

and Big Data can play an important role in medical device

design and manufacturing (Erdman and Keefe, 2013).

Personalized Patient Care

Healthcare is moving from a disease-centered model

towards a patient-centered model. In a disease-centered

model, physicians’ decision making is centered on the

clinical expertise and data from medical evidence and

various tests. In a patient-centered model, patients

actively participate in their own care and receive services

focused on individual needs and preferences. The

patient-centric model creates a personalized disease risk

profile, as well as a disease management plan and

wellness plan for an individual. Personalized healthcare

is a data-driven approach. With the increase in the use of

electronic medical records, Big Data will facilitate to

bring proactive and personalized patient care (Chawla

Davis, 2013). In the near future, new big data-derived

linkages will prompt timely updates of patient triage,

diagnostic assistance and clinical guidelines to allow

more precise and personalized treatment to improve

clinical outcome for patients (Yang et al., 2014).

E-Consultation and Tele-Diagnosis

In the future, the aggregated ECG and images from

hospitals worldwide will become big data, which should

be used to develop an e-consultation program helping

on-site practitioners deliver appropriate treatment.

Real-time tele-consultation and tele-diagnosis of ECG

and images can be practiced via an e-platform for

clinical, research and educational purposes. Big Data

analytics can predict over 50% deaths with fewer false

positives as compared with the traditional ECG

analysis, conductedbased on a smaller segment of ECG

signals (Hsieh et al., 2013).

Pharmaceuticals and Medicine

The ability of pharmaceutical companies to continue

bringing new life-saving/life enhancing medicines to

patients in a timely, yet cost-effective manner will

dependent on their ability to manage big data generated

during all phases of pharmaceutical development.

Integration of clinical, healthcare, patents, safety and

public research data will provide key insights into

decision making for target selection and lead

optimization through Big Data analytics for drug

discovery (Schultz, 2013).

Medical Education

Visual analytics was explored as a tool for finding

ways of representing big data from the medical

curriculum of an undergraduate medical program.

Lidong Wang and Cheryl Ann Alexander / Current Research in Medicine 2015, 6 (1): 1.8

DOI: 10.3844/amjsp.2015.1.8

Table 1. BI&A and Big Data in smart health and well being

Applications

• Human and plant genomics

• Health care decision support

• Patient community analysis

Data

• Genomics and sequence data

• Electronic Health Records (EHR), Personal

Health Records (PHR) Health and patient

social media

Characteristics:

Disparate but highly linked content,

person-specific content, HIPAA, IRB

and ethics issues

Analytics

• Genomics and sequence analysis and

visualization

• EHR association mining and clustering

• Health social media monitoring and

analysis

• Health text analytics

• Health ontologies

• Patient network analysis

• Adverse drug side-effect analysis

• Privacy-preserving data mining Impacts

Improved health care quality, improved

long-term care, patient empowerment

Impacts

Improved healthcare quality, improved

long-term care, patient empowerment.

IRB: Institutional Review Board

HIPAA: Health Insurance Portability and Accountability Act

A possible use of Big Data in the medical education

context (Vaitsis et al., 2014) is to: (1) identify data

connections and the relations between them; (2) determine

data’s roles in the lowest level of a course and in the

overall picture of the medical program; (3) perceive and

analyze the curriculum in terms of identifying whether

knowledge, skills and attitude are constructed through the

alignment of teaching methods and assessment towards

learning outcomes and (4) perform gap analysis by

comparing different states in which data can be found to

identify possible discrepancies.

Smart Health and Wellbeing

Business Intelligence and Analytics (BI&A) and the

related field of Big Data analytics have become

increasingly important in the business communities.

Table 1 (Chen et al., 2012) summarizes some BI&A

features and capabilities in smart health and wellbeing,

including applications, data characteristics, analytics and

potential impacts.

Big Data has brought great opportunities in medical

applications and health care. Big Data applications will

expand to more areas (such as telemedicine and digital

hospitals), further improve medical service and deliver

extensive value-based care. Big Data applications and

opportunities need technology support.

Methods and Technology Progress in Big Data

In healthcare/medical field, large amount of

information about patients’ medical histories,

symptomatology, diagnoses and responses to treatments

and therapies is collected. Data mining techniques can be

implemented to derive knowledge from this data in order

to either identify new interesting patterns in infection

control data or to examine reporting practices. Moreover,

predictive models can be used as detection tools

exploiting Electronic Patient Record (EPR) accumulated

for each person of the area (Bellini et al., 2013).

For Big Data healthcare systems, the Hadoop-

MapReduce framework is uniquely capable of storing a

wide range of healthcare data types including electronic

medical records, genomic data, financial and claims

data etc. and offers high scalability, reliability and

availability than traditional Database Management

Systems (DBMS). In addition, intelligent functional

modules such as specialized machine- learning

algorithms for image analysis and recognition,

diagnosis, surveillance, detection, notification etc., can

be built on it (Ngufor and Wojtusiak, 2013).

Figure 1 shows a general framework of big data and

big data analytics.

In order to create an automatic lesion diagnostic

model, an automatic breast cancer diagnostic model was

created using a pattern recognition algorithm and big data

mining technique. Data mining is the process of

discovering useful correlations hidden in large quantities

data and extracting information which can be used in

decision-making. The Support Vector Machine (SVM)

algorithm that is most frequently used with an elevated

accuracy was also presented in detail. A machine learning

algorithm inputs data into the computer establishes criteria

for categorization and predicts the category of the data

when new data are input (Lee and Lee, 2014).

Visual analytics presents an area of synergistic

research with big data by conceptualizing the output of

complex processes through intuitive graphical means.

Metrics dash boarding, real-time interactive visualization

and Giga-node graph exploration are some examples that

would serve as appropriate visualization solutions to the

big data examples. Unstructured data needs to be

converted into analysis-ready datasets, which include

comprehensive workflows for of big data solutions.

Consideration of the structure of the end-data models is

vital for the visualization process (Schultz, 2013).

Visual analytics combines data analysis and

manipulation techniques, information and knowledge

representation and human cognitive strength to perceive

and recognize visual patterns (Vaitsis et al., 2014).

Visual Analytics supports Big Data by providing

interactive visualizations that allow people to navigate

these datasets. Visual Analytics has been defined as “the

science of analytical reasoning facilitated by interactive

visual interfaces ”(Thomas and Cook, 2006).

Lidong Wang and Cheryl Ann Alexander / Current Research in Medicine 2015, 6 (1): 1.8

DOI: 10.3844/amjsp.2015.1.8

Fig. 1. An applied conceptual architecture of Big Data analytics (Raghupathi and Raghupathi, 2014) (OLAP: Online Analytical

Processing; CSV: Comma Separated Values)

Big Data enabled by cloud technologies could provide

us new insights-clinically, operationally and in research

(Shrestha, 2014). The concept of storage-as-a-service

cloud computing, which provides hospitals with a big data

storage capacity based on their specific demands at a low

cost. In cardiology, cloud computing technology and

mobile teleconsultation should be combined because

mobile teleconsultation requires high speed data delivery

and a big data center where data can be delivered, stored,

retrieved and managed securely (Hsieh et al., 2013).

Besides general cloud infrastructure services (storage,

compute, infrastructure/VM management), the following

services are required to support Big Data (Turk, 2012):

• Cluster services

• Hadoop related services and tools

• Specialist data analytics tools (logs, events, data

mining, etc.)

• Databases/Servers SQL, NoSQL

• MPP (Massively Parallel Processing) databases

• Registries, indexing/search, semantics, namespaces

• Security infrastructure (access control, policy

enforcement, confidentiality, trust, availability,

privacy)

Organizations used various methods of de-

identification (anonymization, pseudonymization,

encryption, key-coding, data sharing) to distance data

from personal identities and preserve individuals’

privacy. De-identification has been viewed as an

important protective measure to be taken under the data

security and accountability principles. Yet, over the past

few years, computer scientists have repeatedly shown

that even anonymized data can typically be re-identified

and associated with specific individuals. De-identified

data, in other words, is a temporary state rather than a

stable category (Tene and Polonetsky, 2013).

Challenges of Big Data in Medical

Applications and Health Care

Large volume, velocity and variety of big data have

brought big challenges in data storage, curation, retrieval,

search and visualization. Variability and veracity of big

data indicate data instability and uncertainty, which often

makes Big Data analytics difficult.

Major challenges of Big Data in medical applications

and healthcare are as follows: (1) the data in many health

care providers, specifically hospitals, are often

segmented orsiloed. Clinical data such as patient history,

vital signs, progress notes and diagnostic test results are

stored in the EHR. Quality and outcomes data such as

surgical site infections, rates of return to surgery and

patient falls are in the quality or risk management

departments. Standards for validating, consolidating and

processing data are needed (White, 2014). (2) It is

difficult to aggregate and analyze unstructured data.

Unstructured data include: Test results, scanned

documents, images and progress notes in the patients’

EHR, etc. (White, 2014). Efficiently handling large

volumes of medical imaging data, extracting potentially

useful information and biomarkers and understanding

unstructured clinical notes in the right context are

Lidong Wang and Cheryl Ann Alexander / Current Research in Medicine 2015, 6 (1): 1.8

DOI: 10.3844/amjsp.2015.1.8

challenges (Priyanka and Kulennavar, 2014). (3)

Analyzing genomic data is a computationally intensive

task; combining with standard clinical data adds

additional layers of complexity (Priyanka and

Kulennavar, 2014). (4) An emerging new data source is

telemetry from patient-owned devices and information

entered by patients. The challenge of Big Data becomes

even greater when telemetry from automated monitoring

devices is included. Such data could include subjective

symptom scores (pain, mood and mobility); patient

reported outcomes; and device telemetry such as weight,

activity, glucose, blood pressure and pulse oximetry

(Halamka, 2014). The capture, indexing and processing

of continuously streaming (and possibly annotated) fine-

grained temporal data is a challenge (Schultz, 2013). (5)

Big Data’s focus on correlations, not causality, is

difficult for physicians biased toward the biomedical

model, where the focus is finding the cause of the

disease in order to effectively treat it. Big data means

more information, but there is often noisy data or false

information (Bottles and Begoli, 2014). (6) Privacy

issues in the Health Insurance Portability and

Accountability Act (HIPAA) are often cited as barriers

to collecting big data (Warner, 2013). In telecardiology

and tele-consultation, data confidentiality in the cloud,

data interoperability among hospitals and network

latency and accessibility are challenges (Hsieh et al.,

2013). (7) Even if the privacy of the patient can be

protected, many health care providers are reluctant to

share data because of market competition. It is difficult

to determine the proper balance between protecting the

patient’s information and maintaining the integrity and

usability of the data. Open access, integration,

standardization of readable and useable data is a

challenge (White, 2014). (8) Data hackers have become

more damaging in big data. Data leakage can be costly.

In March 2012, hackers broke into Utah’s Department of

Health database and downloaded personal data from

780,000 patients (Social Security Numbers were

downloaded for 280,000 patients) (Schmitt et al., 2013).

Biometrics such as a fingerprint helps improve

information security and protect against data leakage.

However, it is almost impossible to guarantee complete

data security. (9) Both providers and payers pointed to

resource shortfalls such as staffing, budget and

infrastructure as the big barriers to the adoption of Big

Data. Lack of infrastructure and policies, standards and

practices that make the most of big data in healthcare

were also cited as a concern (Bulletin Board, 2014). (10)

De-identification is the process by which personally

identifiable information is removed from health

information so that there cannot be any linkage back to

the individual in any way. HIPAA outlines two

procedures for de-identifying the information: Safe

harbor and expert determination. The ability to gather

and analyze de-identified data is essential to driving

down cost and improving quality. Concerns exist that

data cannot really be fully de-identified (Warner, 2013).

Big Data technology challenges such as date

integration, data visualization and information security

will be overcome with the advances of computer science,

scientific computation and other disciplines. Other

challenges such as standards, data privacy and ownership

and data sharing and cross-disciplinary collaboration,

etc. need supports from agencies and governments in

policies. It is important and necessary to consolidate e-

Infrastructures as persistent platforms to ensure

continuity in Big Data.

Conclusion and Future Research

Big Data is based on data obtained from the whole

process of diagnosis and treatment of each case. Big Data

analytics can perform predictive modeling to determine

which patients are most likely to benefit from a care

management plan. It is moving forward quickly in

population health and quality measurement. Big Data offers

a lot of benefits such as disease prevention, reduced medical

errors and the right care at the right time and better medical

outcomes. In addition, Big Data can improve the Research

and Development (R&D) and translation of new therapies.

Big data has great potential to improve medicine, guide

clinicians in delivering value-based care.

Big Data has challenges in medical applications and

healthcare. These challenges include consolidating and

processing segmented or siloed data, aggregating and

analyzing unstructured data, indexing and processing

continuously streaming data, privacy, data leakage,

information security and lack of infrastructure and

unified standards, etc.

Most of the above challenges can be future research

topics. These future research topics can be: Aggregating

and analyzing unstructured health care data, indexing

and processing of continuously stream data, medical data

confidentiality and interoperability, health care data

security and e-Infrastructures as persistent platforms for

health care big data, etc. The authors of the paper will

focus on Big Data in medical sensor data and streaming

data processing, privacy-preserving data mining in health

care, sentiment analysis of medical big data and

personalization and behavioral modeling.

Acknowledgment

This study was supported in part by Technology and

Healthcare Solutions, Inc. in Mississippi, USA. No

conflict of interest to disclose.

Funding Information

The authors have no funding to report.

Author’s Contributions

Lidong Wang and Cheryl Ann Alexander worked on

the manuscript as group efforts at all stages.

Lidong Wang and Cheryl Ann Alexander / Current Research in Medicine 2015, 6 (1): 1.8

DOI: 10.3844/amjsp.2015.1.8

Ethics

This article is original and contains unpublished

material. The corresponding author confirms that both

the authors have read and approved the manuscript and

no ethical issues involved.

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