Presentation on Analytics applications in Healthcare, delivered at IIM Bangalore as part of a masterclass on healthcare analytics

1. ANALYTICS FOR
HEALTHCARE PROVIDERS
APPLICATIONS, TRENDS AND FUTURE OF ANALYTICS IN HEALTHCARE
BHUVANEASHWAR SUBRAMANIAN
HEWLETT PACKARD ENTERPRISE

2. HEALTHCARE ANALYTICS THEN & NOW
1854 CHOLERA ENDEMIC, LONDON 2014 EBOLA EPIDEMIC
- Rudimentary cluster mapping
- Manual and inaccurate analysis
- Retrospective
- (Biomosaic tool), CDC Emergency
Response Center
- Sophisticated predictive modeling
- data from mobile phones,
historical epidemiological data
- Multiple data sources
- High computing power

3. A BRIEF STORY ON HOW HPE DEPLOYED
ANALYTICS TO IMPROVE PATIENT ENGAGEMENT
AT LUCILE PACKARD CHILDREN’S HOSPITAL

4. DRIVER 1:
HEALTHCARE TODAY HAS BECOME DATA
CENTRIC
200
0
BIOLOGICAL DATABASES IN 10 YEARS
80
Mb
PATIENT DATA GENERATED PER YEAR
600
Bn
SEQUENCED NUCLEOTIDES PER WEEK ON AN ILLUMINA
HISEQ

5. DRIVER 2: MEDICAL ERRORS LEADING TO
INCREASED CASUALITIES AND COST OF CARE
1:300
1:10,00
0
CHANCE OF MEDICAL
CASUALITY
CHANCE OF AIR TRAVEL
CASUALITY
1.4 Mn
PEOPLE SUFFERING FROM
HOSPITAL INFECTIONS
WORLDWIDE
1.3 Mn
DEATHS CAUSED BY INFECTIONS
THROUGH UNSTERILIZED
INSTRUMENTS

6. DRIVER 3:
HEALTHCARE EVOLUTION TOWARDS EVIDENCE
BASED MEDICINE AND ACCOUNTABLE CARE
DELIVERYEcosystemintegration
Today’s
healthcare
Collaborative
healthcare
Evidence Based
Personalized
Healthcare
LowHigh
Integrated
healthcare
Different
providers will
be at different
stages
• Stand-alone
• Best of breed
• Fragmented systems
• Integrated
EMR, EHR,
PMS, CPOE
• Real-time
alerts
• HIE/improve
d access to
data
• Tight linkage
between
physicians &
hospitals
• Care
collaboration
• Regional, state
and national
RHIOS, NHIN
• Patient access
to data
• Personalized/
evidence-based
clinical decision
support
• Patient
engagement
Quality of Care and OutcomesLow High

7. THE FOUR V’S TOGETHER DEFINE THE
IMPORTANCE OF ANALYTICS FOR
HEALTHCARE
VOLUME VARIETY
VALUE VELOCITY
• 500 petabytes to 25,000
Petabytes by 2020
• Key sources :MRI,CT &
PET Scans
• 1hr to sequence
whole genome of
humans
•50% reduction in time
for genome
sequencing for rare
diseases
Behavioural data,
Environmental data
Medical record data
Vital sign data
Nutritional data
Pharmaocological data
• <50% hospital labour
compensation ratio
• $300 Bn cost savings for
hospitals in US.

8. ANALYTICS APPLICATIONS ACROSS THE
CARE DELIVERY SPECTRUM
CLINICAL
ANALYTICS
BUSINESS
ANALYTIC
S
PATIENT
COMPLIAN
CE
CLINICAL HEALTH
OUTCOMES ANALYTICS
1
RESEARCH
&DEVELOPMENT
ANALYTICS
2
DISEASE
MANAGEMEN
T
TREATMENT
EFFECTIVENES
S
SITE
SELECTION
TARGETED
THERAPEUTIC
S
PATIENT
COHORT
IDENTIFICATIO
N
5
OPERATIONAL
ANALYTICS
FACILITY
UTILIZATION
STAFF
UTILIZATION
PROCESS
QUALITY
CONTROL
COMPLIANCE
REPORTING
3
MARKETING
ANALYTICS
CUSTOMER
SEGMENTATI
ON
SOCIAL
NETWORK
ANALYSIS
PRICING
OPTIMIZATIO
N
CUSTOMER
LIFETIME
VALUE
4
FINANCE AND FRAUD
BILLING
QUALITY
FRAUD
DETECTIO
N
RISK
MANAGEMEN
T

9. THE DATA FOR APPLYING ANALYTICS ACROSS
THE HEALTHCARE SPECTRUM COMES FROM
SEVERAL SOURCES
• Video conferences
• Downloads
• Call notes
• SMS
• Web chat
• Blogs
• Social networks
• Mobile apps
• Sensors
• Survey response
• Emails
• Revenue management
• Claims
• EMRs
• ICD 9-10
• Meaningful use
• Lab/radiology notes
• P4P reporting
• Quality reporting
• Clinical quality measures
• Transcription
• Population health mgmt
Billions of
daily interactions
Millions
of daily
transactions
Enterprise information that comes
from line of business systems that
provide structured database
information that is used to run the
business
Global information that comes
from internal and external
unstructured sources that is used
to gain insight on the business
drivers
&

10. SCENARIOS WHERE HEALTHCARE ANALYTICS
CAN BRING COST SAVINGS
Identifying cost effective ways of
treating patient through
comparative analyses
Analyzing disease patterns
Monitoring disease outbreaks
Aid in vaccine development and
Population safety measures
1
2
Analyse patient data from E.H.R
and several unstructured sources,
Financial data, genomic data
determine risk of disease
recurrence., hospitalization
3
Conducting genomic analysis cost
Effectively and integrating
Genomic information into patient
Diagnosis and treatment
4
HEALTHCA
RE
PROCESSES
Clinical Operations
Public Health
Evidence Based
Medicine
Genomic Analysis

11. APPLYING ANALYTICS IN HEALTHCARE SETTINGS
DELIVERS A DATA-DRIVEN ACTIONABLE
APPROACH TO TREATING DISEASES
A USE CASE ON DEVELOPING A TREATMENT APPROACH TO DIABETES
Obtaining generic
population level data on
diabetes
Localizing
context to
diabetes
patients
visiting a
treatment
center
Identify
diabetic
patients with
a high chance
of
hospitalizatio
n
Organize hospital
resources to
effectively deliver
care management
and avoid
hospitalization
• National
Prevalence for
Diabetes is 8.3%
• Hypertension is a
major co-
morbidity for
diabetes
• 35,000
individuals suffer
from diabetes in
our region
1000 diabetes
patients visit our
center every year
Total cost of
treating patients per
year is $7,000
Cost increased by
15% over last year
Assign patient level
risk scores on
hospital sample to
develop an evidence
based prediction
model to determine
potential admits
next year
Prioritize patients by
risk score and
allocate care
management
resources to
address at risk
patients & take
steps to prevent
hospitalization

12. USE CASE : AN APPROACH FOR PREDICTING
HOSPITAL ADMISSION RISK FOR DIABETIC
PATIENTS
• Local patient data
• Regional and
national data sets
• Device data
• Patient engagement
data
• Genomic,
Environmental data
• Activity based
costing data
Data Warehouse
Workload
1
Workload
2
Workload
3
User Defined
Classification
& Association Rules
Regression
Decision Tree
Clustering
Pattern Discovery
Techniques and Tools
Visualization Output
Plasma
glucose
BMI Readmit
risk
<127.5 <26.5 No risk
<157.5 >26.5 High API enabled
transfer of
clinical
workflowE.H.R
Clinical Apps
Ordering, Supply
Refills
Improved Diagnosis
Care Management
Altered treatment
programs
Clinical and Operational
Outcomes
SQL Querying
HIVE
R Studio

13. EVOLUTION OF ANALYTICS IN A HEALTHCARE
PROVIDER SETTING AND CAPABILITY
PRIORITIES IN ANALYTICS
STAGE 1
Rookie
• Monitor
dashboards
• Receive
patient
data reports
• Visualize
patient
data
STAGE 2
Dabbler
Analyze past
patient
behavior
• Perform ad hoc
data analysis
• Develop 360-
degree
view of patients
0
10
20
30
40
50
60
70
80
90
100
• Build models
Incorporate
machine
learning
techniques
• Identify
patient risks
and
opportunities
• Real time
prescriptive
analytics
• Provide point-of-
care
decision support
STAGE 3
Pros
STAGE 4
Gurus

14. EMERGING TRENDS IN HEALTHCARE
ANALYTICS ADOPTION
INTEGRATING CLINICAL,
FINANCIAL AND QUALITY
DATA TO DELIVER VALUE
BASED CARE
1
IMPROVING QUALITY OF
REMOTE CARE DELIVERY
THROUGH ANALYTICS
2
IMPROVING PATIENT
ENGAGEMENT AND STAFF
RESPONSE
3 DEVELOPING PERSONALIZED
TREATMENTS AND
THERAPEUTICS
4
Kaiser Permanente – sepsis risk
Max Hospitals- Deep Venous Thrombosis
Narayana Health Telemedicine e-Health Cen
Remote Care Analytics Dashboard
Lucile Packard Children’s Hospital
Operating Room Scheduling Dashboard
Moffitt Cancer Center Gene Expression Based
Radiosensitivity Index for Cancer

15. PROMINENT CHALLENGES IN DEPLOYING
ANALYTICS IN HEALTHCARE
Effective integration of
data from multiple
sources for sensemaking
Standardization of clinical
ontologies across clinical
management platforms
Mitigating data security
and privacy concerns
around patient data
Need for healthcare
specific analytics solutions
to improve veracity
HEALTHCARE
ANALYTICS

16. A HOST OF TOOLS FOR HEALTHCARE
PROVIDERS TO MAKE SENSE OF DATA AT
ALL TIMES
16
Healthcare
Analytics
Toolkit
Healthcare Data
Programming
Data Mining
File Distribution,
Processing and
configuration
Infrastructure
Databases
BI Tools & Visualization

17. FUTURE DIRECTIONS FOR ANALYTICS IN
HEALTHCARE
PRESCRIPTIVE ANALYTICS WOULD BECOME
INCREASINGLY PROMINENT IN HOSPITAL
OPERATIONS
Provide “in-cotext”, real time interpretation of
scenarios designed through predictive analytics:
Adjusting resource allocation
STARTUPS ENGAGING WITH HEALTHCARE
ORGANIZATIONS TO DESIGN CUSTOM
PREDICTIVE ANALYTICS SOLUTIONS FOR DISEASE
MANAGEMENT
2
Oncora Medical is working with hospitals to improve
real time treatments for radiation oncology
3
INTEGRATION OF HETEROGENOUS DATA
SOURCES
TO DELIVER EVIDENCE BASED MEDICINE
1
Integration of EMR, genomic data, wearable data,
epidemiological data with social and behavioural data

18. THANK YOU
Contact details:
BHUVANEASHWAR SUBRAMANIAN : bhuvaneashwar.subramanian@hpe.com
eashwarsubramanian@gmail.com

19. APPENDIX

20. HOW CAN HOSPITALS ADOPT AN ANALYTICS APPROACH
Concept
Statement
Proposal Methodology Deployment
Determine the
need by mapping
the situation to the
4 V’s
What is the
problem being
addressed?
Why take an
analytics approach
?
Variable selection
Platform and Tools
Analytical techniques
Association, Results
Expected
Evaluation
Validation Testing
First cut at
establishing the
need for a project
involving analytics
Expand on the
concept note to
highlight the key
questions and
justify the costs
involved in
analytics
implementations
Break down the broad
questions into
actionable objectives
and apply the right
kind of analytical
tools
Break down the broad
questions into
actionable objectives
and map the kind of
tools and platforms to
use

21. EMERGING TRENDS IN ADOPTION OF
HEALTHCARE ANALYTICS
INTEGRATING CLINICAL, FINANCIAL
AND QUALITY DATA TO DELIVER VALUE
BASED CARE
IMPROVING QUALITY OF REMOTE CARE
DELIVERY THROUGH ANALYTICS
DASHBOARDS
EMPLOYING ANALYTICS TO IMPROVE
PATIENT ENGAGEMENT
1 2
Greater than 64% of Hospital Executives
believe that implementing analytics
would improve health outcomes and
support value based care
Best Practice:
• Kaiser Permanente, integrated clinical, E.H.R
data and operational kpis to predict potential
sepsis risk in patients and advance treatment
• Max Hospitals, India deployed analytics to detect
patients with risk for acquired Deep Venous
Thrombosis
Shortage of doctors,particularly in
developing countries is causing hospitals
to depend more on analytics dashboards
to determine availability of paramedical
staff and evaluate treatments
Best Practice :
Narayana Health partnered with Hewlett
Packard to develop the eHealth Center, which used
Analytics dashboards to determine disease spread in
Region and define treatment options based on historical
Data.
3
Analytics is being used to address
chronically ill patients by processing
data streamed from patient wearables to
determine emergency response and
patient alerts/communications
Fact : Critical patients account for 78 percent of
all healthcare spending,81 percent of in-patient
EMPLOYING ANALYTICS TO DEVELOP
PERSONALIZED TREATMENTS AND
THERAPEUTICS
4
Genomic , pharmacological and
conventional diagnostic data are being
integrated to develop personalized
therapeutics and treatment options for
cancerBest Practice :
HPE developed an operating room scheduling
dashboard, that captured data on intensive care
patients from their electronic records and sensors
attached to vital sign monitors at Lucile Packard Hospital
and helped reduce casualities.
Best Practice :
Moffit Cancer Center developed a gene
expression based radio sensitivity Index that
accurately predicts the outcomes of radiation
therapy for various cancers across patient strata.

22. THE FOUR V’S FOR HEALTHCARE TOGETHER
DEFINE THE IMPORTANCE OF ANALYTICS IN
HEALTHCARE
Volume
• The volume of healthcare data is expected to grow 50 fold from 500 petabytes to 25,000
petabytes by 2020
• Primary contributors to the data volume would include high resolution MRI scans, CT
Scans and PET scans
• Data volumes are expected to increase primarily due to government mandates to store
patient data for the longest periods possible
• High resolution healthcare scans are also expected to increase the data volume
Variety
• The variety of data includes text, images, videos
• The primary sources for data are expected to be patient records, patient wearables,
high throughput sequencing data from genomics experiments
Velocity
• Speed at which data is generated from a patient interaction or the rate at which
biomedical data is generated
• Shift from static data like X-Rays, EMRs to real time data from wearable monitoring and
genome sequencers
Value
• Operational efficiencies, to reduce costs, waste, and fraud through more efficient
methods for data integration,
management, analysis, and service delivery.
• Business process enhancements, to find new ways of delivering care while efficiently
allocating services to enable sustainable management of the population health

23. KEY CHALLENGES TO APPLYING ANALYTICS IN
HEALTHCARE
•Analytics solutions
specific to
healthcare will be
necessary to
improve specificity
and veracity of
healthcare
outcomes
•Security threats
challenge the
ability to facilitate
information
exchange and use
open source
software to analyse
proprietary patient
data.
•Currently high
blood pressure can
be expressed in
127 terms.
Integrating data
from genomic
expression studies
with healthcare
records and
standardize
medical ontologies
is a critical
challenge
•Intepreting
structured data and
unstructured data
consistently,as
most of the data
generated is
managed for size
through Electronic
health records and
genomic data
platforms.
Effective
interpretation of
healthcare and
life sciences
data
Standardizing
clinical
ontologies
Lack of
comprehensive
healthcare
specific analytics
solutions .
Threats of data
breaches
and siloed
departmental
data

24. BLOCKCHAIN IN HEALTHCARE TO IMPROVE
EFFECTIVE INTEGRATION OF HEALTHCARE
INFORMATION
Source: Deloitte