Data Use Cases - Healthcare & Banking

1. Several use cases exhibit how enterprises are effectively
innovating through Big Data
R&D
and
Operations
Treatment and
Clinical
Decision
Making
Customer
Service and
Post
Discharge
Services
Preventive
Medicine
Reducing preventable hospital
readmissions
Predictive Analytics to identify
High Risk patients and Infections
Leveraging Big Data within
Hospital Network
Genome Processing and DNA
Sequencing
Operations & Supply Chain
Management
Optimizing Claims Management
Process
Evidence Based Medicine
Surgical Decision Making Process
Real Time Patient Monitoring
Insights based on voice data from
Contact Centers
Social Media mining for better
customer service
Use of geospatial and public data
sources
 Big Data Analytics
 Predictive
Analytics
 EHR Analytics
 Big Data Analytics
 Predictive Analytics
 Document Automation
 Internet of Things
 Wearable Devices
 Machine Data Analysis
 Big Data Analytics
 Text Analytics
 Natural Language
Processing
 Social Media Analytics
 Need of a Scalable
Solutions
 Address Heterogeneous
Data
 Easy to use solution by
unskilled employees
 Rapid Deployment
 Enhanced accuracy and
reduce processing time
 Aid in Clinical Decision
Making Process
 Leverage machine data to
aid surgeons and
physicians
 Analyze Unstructured Data
 Customer Sentiment
Analysis
Preemptive actions
leveraging multi variate
analysis coupling real
time, historical patient
data and the vast medical
record repository
Permissive actions based
on patient records only
with minimal utilization
of the vast medical data
repository
Voice, Text and Social
Analytics to gain insights
to better understand
patient pain points
Customer service based
on direct client
interaction, medical
history and patient’s
said grievances
Manual recordkeeping,
tedious claim
management, DNA
Sequencing not part of
regular care
Direct linkage with EMRs
to perform automatic data
assessment using NLP,
Text mining to facilitate
inter-operability
Decision based on
patient’s medical
history with periodic
assessment
Analysis of patient’s vitals
through wearables and
machine data with real time
updates to physicians for
effective care
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Key Areas Addressed
Leveraging Mix of
Conventional and Modern
Tech
Modern Age
Conventional Age
Use Cases
Data Usage

2. Global incumbents are already vying to tap into this large
existing opportunity
Mobile NLP, AI, Cognitive Cloud Internet of Things
Service Providers Hi-Tech Enterprises Technology Vendors
Launched e-Health platform, Remote Health
Monitoring System, mFolio to enable real-time
analysis
Launched Watson Health Cloud enabling
Doctors to get real time data across several
mobile and wearable devices
Launched Advance personalized medicine
solution to further improve personalized
medicine and care
Partnered with the likes of Infosys, Sogeti,
General Electric, etc. to launch specific
Healthcare analytics solutions
Introduced Oracle Healthcare Precision
Medicine, a solution to support clinical
research and healthcare
Philips enabling easier patient self
monitoring through IoT and Cloud
Medtronic through its Carelink systems is
utilizing IoT to securely capture and share
the device information between patient and
practitioner
Launched a complete integrated solution
for healthcare “KLOUDKARE”
Cognizant’s HealthActivate solution provides
personalized, targeted patient engagement
services for pre and post-launch trials
GE Healthcare along with Accenture
introduced new claims processing solution
for Healthcare providers
Launched a range of Cloud based solutions
for radiology
CSC launched a Chronic Care
Management(CCM) service providing a simple
and efficient technology based approach for
healthcare providers
Delivery Model or Technology Used
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3. Similarly, Healthcare startup’s are further pushing the
envelope for innovation
Machine
Learning
Artificial
Intelligence
Cognitive
Computing
Scalable /
Mobile
Internet of
Things
Preventive
Medicine
R&D
and
Operations
Treatment and
Clinical Decision
Making
Customer
Service and
Post
Discharge Services
7200+
Startups
developing
Healthcare
Digital
Solutions
(2015)
$15Bn+
Funding
received by
Healthcare
Startups
(2015)
Advanced technology based solutions
launched across the Healthcare Value Chain
complementing Big Data
Illustrative
List
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4. However, there are several challenges that enterprises still
need to address….
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It is estimated a 100,000-plus-person analytic talent shortage at least through 2020, which could
mean 50–60% of data scientist positions may go unfilled
80% of health data is unstructured and stored in hundreds of forms such as lab results, images, and
medical transcripts
Healthcare providers look for easy to deploy solutions which enables integration across different data
sources and can be easily supported by non-expert technical staff along with a short deployment
cycle
Ownership of Big Data initiatives have now spread across different Business Units compared to the
CIO previously running the enterprise wide initiatives
Need of a scalable solution becomes imperative as the data generated by different Business Units
expand and the need of a dynamic and extendable platform becomes important for future
performance requirements as well
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3
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2
5
Harnessing Multi
Channel Data
Deployment
Challenges
Talent
Ownership of Big Data
Initiatives
Need of a Highly
Scalable Solution

5. High need for a solution to be compatible with legacy as well as new age systems:
Several companies use traditional or legacy systems which still acts as a important piece of the
operations. Solutions should be able to combine both legacy and new age systems and enabling
deep analysis of the combined stack. An Open API solution allows organizations to easily connect the
solution with several existing applications
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….These challenges act as a catalyst for an ideal Big Data
solution
5
Mobile & Cloud First Solution:
Solutions must offer high level of scalability due to the volatile nature of data being generated.
Solution should ideally be a cloud solution with mobile capabilities and its scalability aspects should
be automatic, on-demand and elastic
Effectively utilize data generated from multiple disparate sources:
Solutions must be able to collect and analyze varied structured and un-structured data such as
Imaging, Voice, EHR, Lab Reports, Machine Data, Wearables, Social Media, Public Data sources,
Genomic Data, etc.
Rapid rate of response to queries:
Real Time monitoring is a must in Healthcare industry which makes it imperative for the data analysis
to be done in almost real time for effective care services. Solution should be capable of running ideal
queries within seconds or minutes
Solution should be easy to use and simple :
Capability of a solution to run very complex algorithms upon data and effectively provide insights in a
very comprehensible manner. Even, non highly technical organization staff should be able to use it
effectively
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3
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Scalable Solution
Ability to collect and
analyze Heterogeneous
data
Rapid execution of
Predictive &
Prescriptive Algorithms
Easy to use solution
with Effective
reporting mechanism
Interoperability with
traditional & modern
databases and systems
Key Aspects
of an Ideal
Big Data
solution –
Healthcare
Industry