Healthcare
White Paper
New Horizons, New Hopes
Big Data in Healthcare
About the Author
Viswanathan Ganapathy
Viswanathan has 23 years of industry experience in technology, solutions and
consulting. He has more than a decade of experience in Healthcare technology and
domain focused solutioning for various healthcare customers in the US and Europe.
Viswanathan is part of the Technology Excellence Group of the Healthcare Industry
Solutions Unit at TCS. He conceptualizes strategic solutions and platforms for healthcare
customers including Payers, Integrated Payer-Providers, Specialty Providers and
Pharmacy Benefit Management companies.
Viswanathan has an MBA with a specialization in Healthcare from the Yale School of
Management, Yale University, New Haven.
Healthcare organizations contend with vast volumes of data derived from several relevant
sources - from patient data to medical device sensor data to content from social media. Big
Data has the potential to transform healthcare - it is not just about processing huge datasets,
but about making data hypothesis generating rather than hypothesis driven. Big Datas far
reach can impact several aspects of healthcare - clinical research, care delivery, outcome
improvement, efficiency improvement, health policy and value based strategy
implementation. Through a use-case driven approach, this article emphasizes on the
relevance of Big Data in healthcare, both in the present and in the future.
Contents
1. What do Moon Landing and Tackling Cancer have in common?
5
2. Healthcare Big Data in Simple Terms
5
3. Big Data Characteristics and Challenges
6
4. Key Problems and Trends
7
5. Classified Sample Use Cases for Big Data in Healthcare
8
A. Clinical Research Use Cases
8
B. Care Delivery Use Cases
10
C. Clinical Outcomes
11
D. Fraud, Waste, and Abuse
13
E. Value-based Strategies
13
F. Health Policy and Economics Use Cases
14
6. Healthcare Big Data Systems Integration
14
7. Conclusion
15
What do Moon Landing and Tackling Cancer have in
common?
Indeed there is a connection. Fifty years ago, President John F. Kennedy told the world from Houston that the
United States would go to the moon before the end of the 1960s. JFK s vision was realized in 1969 with the help of
brilliant research in science, engineering and human endeavor.
On Friday, September 21st 2012, Houston s MD Anderson Cancer Center announced its own Moon Shots Program,
aimed at significantly reducing the number of deaths from a handful of cancers by the end of this decade1. But in
the fight against cancer, in addition to the three defining parameters science, engineering and human endeavor
- a fourth tool will also be needed, Big Data.
Dr. Ronald DePinho, president of MD Anderson Cancer Treatment and Research Center, is launching the Moon
Shots project on two parallel tracks.
Track 1: Apply the existing knowledge to make a near-term impact in this decade
Track 2: We do not know everything we need to know to ultimately cure the disease
The program is an unprecedented effort to dramatically accelerate the pace of converting scientific discoveries
into clinical advances that reduce cancer deaths. This is also a typical use case for Big Data where large data sets of
knowledge and information thousands of discoveries, clinical research information, clinical data, patient
experiences, genomics are strung together using just one approach Big Data, and put to use in real-time.

For example, doctors can now analyze the DNA of a patient or a tumor in a matter of hours, spending only a few
hundred dollars; something that took 10 years and cost billions when the first genome was sequenced.

Knowing specifics about a patient's genetics can help doctors determine who will benefit from an existing drug
and who will not, so patients don t waste time and money on a very expensive drug that will not help cure their
cancer.
Healthcare Big Data in Simple Terms
Big Data has a simple foundation. For decades, healthcare organizations have stored vast transactional data in
different databases, formats, and systems. Beyond this critical data, there is a wealth of non-traditional, less
structured data from web blogs, social media interactions, call centers, chat sessions, video chats, email,
[1] CNN, Launching a new war of Cancer(2012), accessed Sep 29, 2012, http://www.cnn.com/2012/09/21/health/cancer-program/index.html?iref=allsearch
[2] Oracle White Paper, Big Data for the Enterprise, January 2012
5
equipment sensors, photographs, and digital images that can be mined for useful information. Reductions in the
cost of both storage and computing power have made collecting this data feasible. As a result, healthcare
organizations are beginning to include non-traditional yet potentially valuable data with traditional enterprise data
2
in their business intelligence analysis.
Big Data in healthcare typically encompasses the following:

Traditional Enterprise Data Transactional data in claims, patient admissions, laboratory health records, CRM
systems, ERP data, online store transactions, and financials

Machine-generated/Sensor Data Includes Call Detail Records (CDR), weblogs, smart meters, Computerized
Tomography (CT) scanners, life support systems, and telemetric data

Social Data Includes customer feedback streams, blogs, micro-blogging sites such as Twitter, and social media
platforms such as Facebook, Myspace, and TuDiabetes.org
Big Data Characteristics and Challenges
Big Data has four typical characteristics
Attribute
Description
Volume
Nano technology and machines generate petabytes of data.
Velocity
Social media constantly generates tons of opinions and relationships. This is valuable
information to track patient experience for hospitals, member experience for payers,
adverse effects for drug stores, and wellness discussions. These can be sourced from Twitter,
Facebook, TuDiabetes.org, blogs, and so on.
Variety
Traditional data formats are described well and change slowly. In contrast, non-traditional
data formats exhibit a dizzying rate of change. As new services and innovations are added,
new sensors deployed or new marketing campaigns executed, new data types are needed
to capture the resultant information.
Value
The economic value of different data varies significantly. The challenge is identifying what is
valuable and then transforming and extracting that data for analysis.
Table 1: Big Data Characteristics
[2] Oracle White Paper, Big Data for the Enterprise, January 2012
6
Key Problems and Trends
The key challenges for healthcare companies, according to Chris Gough, Solutions Architect at Intel and Alan Stein
MD, vice president of Autonomy, a HP company is outlined in the first five items in Table 2.3
#
Trend
Description
1
Fragmented
data

The separation of data among labs, hospital systems, and even
clinical components such as financial IT and electronic health
records is a key issue in healthcare.
Variety
2
Big data is all
about real or
near real-time

Traditional analytics use ETL processes that upload data nightly or
weekly to a data warehouse.
The Big Data trend is moving toward real or near real-time
decision support at the point-of-care.
In traditional analytics, reporting focuses on the past, but with Big
Data, it is more predictive.
Velocity, Value


3
Data is
driving the
processes


Attribute
Traditionally, processes pulled and pushed data whenever needed. Volume, Variety,
Velocity
In Big Data, processes access data to derive meaning from
datasets, create clinical hypothesis, prevent fraud, reduce cost of
care, reduce clinical errors, and improve outcomes.
4
Scale-up is
shifting to
scale-out.

Traditionally, scale-up was the active choice. This led to replacing
existing infrastructure with bigger servers, larger memory and
more processing power In Big Data, multiple nodes are leveraged.
Systems need not be replaced, rather are modernized and
leveraged to exchange and use information.
Value
5
Software as a
service (SaaS),
Infrastructure
as a Service
(IaaS)

The exponential growth of data requires significant supporting
infrastructure and complex software for healthcare companies to
derive insights. Healthcare organizations can adopt new service
delivery models such as Saas and Iaas to fulfill software and
infrastructure needs.
Value
6
Data Privacy
Concern

Privacy of Personal Health Information (PHI) and Individually
Identifiable Personal Information (IIPI) is key to healthcare
companies.
Big Data solutions also need to effectively address data security
concerns to ensure data privacy.
Value

Table 2: Challenges in Healthcare
[3] Healthcare IT News, 5 basics of big data (June 13, 2012), accessed November 15, 2012 http://www.healthcareitnews.com/news/5-basics-big-data
7
Classified Sample Use Cases for Big Data in Healthcare
Area
Clinical
Research
Use case samples




Treatment research
Genomics
Semantic research
New drug indications




Fragmented data
Hypothesis generation
Volume, scale-out
Variety

Fragmented data
Volume, velocity
Real-time or near real-time
Variety
Improving urban healthcare
Balance sheets of countries

Variety, value, volume, velocity


Clinical outcome improvement
Reducing readmissions and inpatient complications
Closing gaps in care
Improving drug safety

Variety
Variety, value
Variety
Value
Fraud, Waste
and Abuse

Reducing fraud, waste, and abuse

Value-based

Accelerating value-based strategies
Prioritizing data investments

Care Delivery




Health Policy


Clinical
Outcomes
and Safety




Chronic care delivery
Care processes
Personalizing care plans
Refining patient monitoring
Challenges addressed






Volume, variety, value
Value
Value
Table 3: Classified sample use cases for Big Data in healthcare
A.Clinical Research Use Cases
1. Treatment Research
4
Biostatistics Chair Victor De Gruttola is working on an Institute of Medicine (IOM) project on HIV care in the US. The
Center for Disease Control and Prevention (CDC) captures diagnostic, demographic, and medical information, but
no data on the use of antiretroviral drugs. Medicaid and Medicare (CMS) track service use through claims data, but
not clinical measurements such as immune function at diagnosis. De Gruttola posits that if researchers could join
these datasets, they would learn of the vulnerable groups of patients that aren t getting the treatment they need.
Simply put, Big Data can transform health sciences from hypothesis driven to hypothesis generating.
[4] HSPH news, http://www.hsph.harvard.edu/news/features/frontlines/spr12-big-data-tb-health-costs/index.html, (July 2012) accessed Aug 6th 2013
8
2. Genomics
The Human Genome project, aimed at the complete mapping and understanding of the human gene, cost $ 3
billion and concluded in 2002, a decade from when it began. Today, it costs $ 1000 and a few days to complete a
5
gene sequence. In 2002, the cost of processing a million DNA bases was $5,292 whereas today it is about $0.19.
Alzheimer s Association and the Brin Wojcicki Foundation have made available massive amounts of new data from
genome sequencing, generated by a Big Data project for Alzheimer s disease. The Alzheimer disease neuroimaging
initiative (ADNI) will be publicly available for research to assist scientists in discovering new treatment and
6
prevention methods for Alzheimers. MD Anderson cancer center in Texas has identified seven types of cancer Triple Negative Breast Cancer, High-grade Serous Ovarian Cancer, Leukemia (AML/MDS), Leukemia (CLL), Lung,
Melanoma and Prostate Cancers for its Moon shots project in which Big Data will play a key role. 7
3. Semantic search
Imagine you re a doctor trying to learn about a new patient or identify who among your patients might benefit
from a new technique. But patient records are scattered across departments, vary in format and, perhaps worst of
all, use the ontology of the department that created the record. Startups like Apixio are trying to fix this by
centralizing records on the cloud and applying semantic analysis to uncover all the information doctors need,
regardless of who wrote it. This uses unstructured data, text mining, and voice mining data analysis.
Figure 2: Use of Office Notes to capture patient information
Source: http://gigaom2.files.wordpress.com/2012/07/mine3-officenotes-semantic-smaller.jpg
[5] National Institute of Mental Health, Director s blog: An emerging era of big data (Feb 2012) accessed Aug 17th 2013, http://www.nimh.nih.gov/about/director/2012/anemerging-era-of-big-data.shtml
[6] Alzheimer s association, Big Data From Alzheimer's Disease Whole Genome Sequencing Will Be Available to Researchers Due to Novel Global Research Database, (July 2013)
accessed Aug 17th 2013, http://www.alz.org/aaic/_releases_2013/fri_400pm_gaain.asp
[7] MD Anderson Cancer Center, Building Genomic medicine capability, accessed Aug 22nd 2013, http://www.genome.gov/Multimedia/Slides/GM4/GM4_24_Futreal.pdf
9
4. Detect New Drug Indications
Petabytes of raw information could provide clues for everything from preventing tuberculosis to shrinking
healthcare costs if we can figure out how to use them. According to researcher Sarah Fortune, Associate
Professor of Immunology and Infectious Diseases at the Harvard School of Public Health, Tuberculosis (TB) bacteria
replicate in random patterns unlike most other bacteria that make carbon copies. TB can thus defy conventional
antibiotics. Using special cameras, Sarah has captured thousands of pictures. Since sifting through these manually
will significantly delay research and inhibit scientific progress, Sarah is looking to use Big Data tools to further TB
research.
5. Healthcare in Developing Countries Malaria Control and Prevention8
Malaria kills one million people a year in Sub-Saharan Africa alone and most of them are children. A group of
researchers from Harvard School of Public Health have tracked Big Data from cell phone usage and the malaria
prevalence maps. The team analyzed the movement of nearly five million Kenyan cell phone subscribers over the
course of a year (from June 2008 to June 2009) and compared it to the instances of malaria found in the country
using a map provided by the Kenya Medical Research Institute and the Malaria Atlas Project. The goal was to
identify both the source and sink points, or where the disease originates and where the disease primarily ends up.
Not surprisingly, they found that one of the primary sources was the area near Lake Victoria, as lakes are prime
breeding grounds for mosquitoes. However, according to the study, a surprisingly large portion of non-native
infections ended up in Nairobi, Kenyas capital.
The researchers, using text and call information, figured out Nairobi was a sink by mapping every journey taken by
each of the nearly 15 million cell phone subscribers. Through that data, it was discovered that many people who
travel to mosquito hotspots such as Lake Victoria or the shore are from Nairobi and end up bringing the disease
back with them.
B. Care Delivery Use Cases
1.Chronic care delivery
In the delivery of healthcare services, management of chronic or long-term conditions is expensive. Use of in-home
monitoring devices to measure vital signs and monitor progress is just one way that sensor data can be used to
improve patient health and reduce both office visits and hospital admittance.
2.Care process enhancement9
Getting ahead of the disease with early diagnosis and without expensive tests was made possible by Seton
Healthcare s Big Data project. Trying to find better ways to detect congestive heart failure early in order to save the
exorbitant costs of treatment as the disease progresses, a team discovered that a distended jugular vein
something that can be spotted during any routine physical exam is a particularly high risk factor.
[8] Datanami, Cellular data bites back at Malaria, (October 31, 2012), accessed Nov 15, 2012,http://www.datanami.com/datanami/2012-1031/cellular_data_bites_back_at_malaria.html
10
3.Care plan personalization
As outlined in the early part of this paper, gene and data-based decisions can be taken at the point-of-care to
deliver the right care for cancer and multiple sclerosis, and to focus on care that will work. This will save time,
improve outcomes, and decrease costs.
10
4.Patient monitoring
New technology and methods of critical care and continuous care can change delivery and methods of delivery
and ultimately reduce the cost of care through remote monitors feeding data in near real-time to electronic
medical record databases. Simply alerting a physician that a congestive heart failure patient is gaining weight
because of water retention can prevent possible emergency hospitalization. In general, the use of data from
remote monitoring systems can reduce patient in-hospital bed days, cut emergency department visits, and
improve the focus of nursing.
C. Clinical Outcomes
1.Clinical outcome improvements and efficiencies
Doctors and staff at Seattle Children s Hospital are using Tableau to analyze and visualize terabytes of data
dispersed across the institution s servers and databases. While data visualization helps in reducing medical errors
and hospital planning, the hospital s focus on data has saved it $3 million in supply chain costs.
Figure 3: Occurrence of disease in patients and cost of healthcare
Reference: http://gigaom2.files.wordpress.com/2012/07/health-costs.jpg
[9] Service Angle, Healthy big data: what the Doctor just ordered, (March 2013) accessed August 17th 2013, http://servicesangle.com/blog/2013/03/05/healthy-big-data-justwhat-the-doctor-ordered/
[10] McKinsey Global Institute, Big data: The next frontier for innovation, competition and productivity (May 2011) accessed Sep 29th 2012,
http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation )
11
Reducing avoidable readmissions and inpatient complications
About 19% - close to two million - of the people covered by the US government s Health Insurance Program,
Medicare, return to the hospital within a month of discharge, costing the US government $17.5 billion annually.11
Center for Medicare and Medicaid Services (CMS) considers readmissions a prime symptom of an overly expensive
and uncoordinated health system. CMS penalized 2,200 hospitals in 2012 with penalties amounting to
approximately $125,000 per hospital and $280 million in total.12
Big Data can help hospitals and the government in improving the co-ordination of care and post care to reduce this
huge bill.13 A hospital undertook a Big Data project for analyzing readmissions of pneumonia patients over a 12month period using patient demographics, procedure and diagnosis codes, and relevant clinical and financial data.
Predictive models found that 21% of the patients who were not readmitted had an endotracheal tube procedure,
whereas the 26% who were readmitted were patients who had refused therapy evaluation at discharge. Using this
result and attaching a risk score to patients prior to discharge, the hospital was able to determine if additional
treatment was needed to reduce readmission chances. This use of predictive analytics led to a 217% improvement
14
over the health system s prior re-admission rate of Medicare patients.
2.Improving drug safety and efficacy.
15
Researchers at New York-based Mount Sinai School of Medicine have developed algorithms to predict reported and
even some unreported drug side effects leveraging large electronic health record datasets. They found 32 severe
side effects stemming from the usage of 53 cancer drugs, using a network built via the algorithm, dubbed
Genes2FANs (functional association networks).16
In a classic example, data registered on the Social media site Patients like me, indicated that using Lithium to treat
Amyotrophic Lateral Sclerosis, (ALS) was futile years before the completion of prospective trials.
Reducing gaps in care
One of the most persistent problems in healthcare is care transitions between sites of care. Clinical information
modeling and continuous data integration and access enabled by Big Data are key to solving this.
[11] Kaiser health news, Medicare revises hospital readmission penalties, (Oct 2012) accessed Aug 17th 2013,
http://www.kaiserhealthnews.org/stories/2012/october/03/medicare-revises-hospitals-readmissions-penalties.aspx
[12] Commonwealth fund blog, The effect of medicare readmission penalties on hospitals efforts to reduce readmissions: perspectives from the field, (Feb 2013) accessed Aug
17th 2013, http://www.commonwealthfund.org/Blog/2013/Feb/The-Effect-of-Medicare-Readmissions-Penalties-on-Hospitals.aspx
[13] American medical news, 2200 hospitals face medicare penalty for readmissions (Aug 2012), accessed Aug 17, 2013,
http://www.amednews.com/article/20120827/government/308279952/6/
[14] Becker s Clinical Quality and infection control, 4 steps leveraging big data to reduce hospital re-admissions (Nov 2012) accessed Aug 17th 2013,
http://www.beckersasc.com/asc-quality-infection-control/4-steps-to-leveraging-qbig-dataq-to-reduce-hospital-readmissions.html
[15] GigaOm, Better medicine brought to you by big data, July 2012, accessed November 2012, http://gigaom.com/cloud/better-medicine-brought-to-you-by-big-data/
[16] FiercehealthIT, Mount Sinai algorithm predicts drug side effects (July 2012) accessed Aug 17, 2013, http://www.fiercehealthit.com/story/mount-sinai-algorithm-predictsdrug-side-effects/2012-07-31
12
D. Fraud, Waste, and Abuse
1.Real-time fraud detection
Fraud and the abuse of healthcare services in the U.S. cost an estimated $125-175 billion annually in healthcare
spending.17
To date, only 3 5% of frauds have actually been detected and usually late in the payment cycle. Additionally,
only a fraction of money that could have been used to provide care is recovered. Some cases are the result of billing
and coding errors - Medicare reimbursements are based on a complex set of diagnosis and procedure codes and
diagnostic related groupings (DRGs). As different codes can produce significantly different reimbursements, there is
a financial incentive to upcode or unbundle for increased reimbursements. With prevention practices in place,
these errors could have been entirely avoided.
Examining healthcare fraud with the application of link analysis, including looking at the social networking patterns
of known bad actors, is a promising new approach. CMS has put together a centralized approach to using
predictive analytics. CMS is building a repository of algorithms to target specific claim and provider types to keep
individuals and companies that intend to defraud out of the system. It also equips CMS with tools to recognize
fraudulent claims and excessive care and eliminate payment errors.
E.Value-based Strategies
1.Accelerate value-based strategies
Value, in simple terms, is the ratio of outcome to expense. Quality of Care, redefined integrated care models and Coproduction and Co-creating of Health are some of the other key value-based strategies in healthcare that can
benefit from Big Data.
2.Prioritize data investments
Watson - IBM has partnered with WellPoint to bring the Jeopardy! Champion question answering system into
doctors offices. Watson could help doctors answer questions posed in natural language by analyzing them against
mountains of medical research data that no individual doctor could possibly read and digest. Watson can sift
through 1 million books or about 200 million pages of medical literature and provide a precise response in less than
three seconds.18
[17] Government healthit, Top 9 fraud and abuse areas big data can target (May 2012) accessed September 29, 2012, http://www.govhealthit.com/news/part-3-9-fraud-andabuse-areas-big-data-can-target
[18] IBM, Wellpoint and IBM announce agreement to put Watson in healthcare (Sep 2011) accessed Aug 17th 2013, http://www03.ibm.com/press/us/en/pressrelease/35402.wss
13
F. Health Policy and Economics Use Cases
1.Improving urban healthcare
The Camden Health Database has seven years of claims data from the local hospitals and emergency departments.
The database holds records of over 600,000 visits and is regularly updated with new data from local hospitals and
emergency rooms. 2 It turns out that 80% of the city s medical costs are attributed to 13% of the patients and the
total cost over five years for hospital and emergency care was $650 million, mostly paid by public funds.
The database serves as a powerful tool in the Camden Coalition s medical care outreach and advocacy efforts,
identifying emerging health hot spots and demonstrating that the majority of the hospital visits were for
19
preventable conditions that could be treated by a primary care provider.
2.Health policy transformation
Environmental scientists are capturing huge quantities of air quality data from polluted areas and attempting to
match it with equally bulky health care datasets for insights into respiratory disease. Epidemiologists are gathering
information on social and sexual networks to better pinpoint the spread of disease and even create early warning
systems. Comparative-effectiveness researchers are combing government and clinical databases for proof of the
best, most cost-effective treatments for hundreds of conditions information that could transform healthcare
policy.
3.Big Data = Balance sheets and budgets of countries
According to McKinsey & Company, with the right tools, Big Data could be worth $9 billion to U.S. public health
surveillance alone and $300 billion to American healthcare in general, the former by improving detection of and
response to infectious disease outbreaks, and the latter largely through reductions in expenditures.
Healthcare Big Data Systems Integration
A Mckinsey report estimates that in about 10 years, Big Data can capture:

more than $300 billion annually in new value

with 2/3rd of that in the form of reductions to national health care expenditure

About 8% of estimated healthcare spending at 2010 levels
Integration opportunities include real-time data integration, systems modernization, infrastructure, multi-node
processing, systems and application rationalization. Data analysis and crunching to facilitate real-time use will
present immense opportunities for system integrators.
This will require new business models such as SaaS, and IaaS, and the creation of new platforms.
[19] Camden Coalition of healthcare providers, An ACO is born in Camden. Can it flourish in Medicaid? (Sep 2011) accessed Aug 17th 2013, http://www.camdenhealth.org/anaco-is-born-in-camden-but-can-it-flourish-in-medicaid/
14
Conclusion
Healthcare organizations have vast amounts of traditional enterprise data. Today, organizations also have access to
even larger amounts of non-traditional data from social media, blogs, images and notes. Big Data has the potential
to string this traditional and non-traditional data together to deliver significant insights that can drive
improvements in wide ranging areas of healthcare from clinical research to care delivery to health policy and
planning. Big Data is proving to be a huge asset in tackling community healthcare issues as demonstrated by
Camden Coalition in New Jersey in efforts to reduce the costs associated with emergency care and make it
prevention-focused. In clinical research and care delivery, Big Data can be leveraged as a powerful tool to find
solutions to Alzheimer s disease and certain types of cancer and also provide a low cost approach to personalized
medicine. In health policy, planning and implementation, initiatives such as using cellphone data to track disease
origination and spread can lead to key insights on where to spend valuable economic resources to control diseases
and epidemics. Healthcare organizations need to evaluate Big Data needs as well as potential uses and take a step
towards moving to a data driven, hypothesis generating approach to forward clinical research frontiers. By
leveraging Big Data, healthcare organizations can create value based outcome-driven efficient care delivery that
benefits all stakeholders.
15
About TCS Healthcare Business Unit
TCS partners with leading health payers, providers and PBMs globally to enable business model transformations
to address healthcare reforms, improve quality of care, increase customer engagement and reduce overheads.
By streamlining and modernizing business processes and systems, TCS helps healthcare organizations realize
operational efficiencies and reduce operating costs. We work closely with healthcare players to empower them
to meet their consumers demands for higher levels of service, quality of care, and new ways of interacting and
engaging. Our advanced data solutions, analytics, and cutting edge digital technologies deliver a higher degree
of customer centricity.
TCS portfolio of services covers the entire payer value chain from Plan Definition, Eligibility and Enrollment,
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and Member Services. For providers, we deliver bespoke services for Provider Management, Claims
Management, Patient Information and Financial Management, Clinical Data Management, Pharmacy Benefit
Management and Revenue Cycle Management.
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