HPE and Hortonworks join forces to Deliver Healthcare Transformation

1 © Hortonworks Inc. 2011 – 2016. All Rights Reserved
HPE and Hortonworks join
Forces to Deliver
Healthcare Transformation
Richard Proctor
GM Healthcare, Hortonworks
John Sanson
Alliance Manager, HPE

Agenda
• Introductions
• Healthcare - Current state
• The Problem: Current data architecture
• The Solution: Modern data Architecture with Hadoop
• Healthcare Use Cases
• HPE Hadoop Reference Architecture
• Q&A

Data Driven Organizations-Shifting the Data Paradigm
o Regulatory-centric
o Manual data review/collection
o Repressive data silo’s
o What data to store?
o Expensive storage w/limited options
 All data types
 Organization & Patient centric
 Store everything!
 Inexpensive storage with lots of options
Data as an independent business
process(Silo’s of data)
Reactive reporting
Data as a byproduct of patient care Prospective analysis
Primary audience – Healthcare organization
Secondary audience
Secondary audience
Primary audience – regulatory agencies
Current data process with latent architecture
Modern Data Architecture with Hadoop

The problem: Current data architecture under pressureAPPLICATIONSDATASYSTEM
REPOSITORIES
SOURCES
Existing Sources
ADT, Patient Accounting, GL, Payroll, Physician
entry, Core measures, Patient Sat, AHRQ, External
benchmarks,, Clinical Systems (ED, Radiology, PACS),
Other Sources (Clinics, home health, Ambulatory,
LTAC’s)
RDBMS EDW MPP
Business
Analytics
Custom Applications
Packaged
Applications
OLTP, ERP, CRM Systems
Unstructured documents, emails
Clickstream
Server logs
Sentiment, Web Data
Sensor. Machine Data
Geolocation
Value in New data sources
• Limited Application interaction
• Costly to Scale storage
• Silos of Data
• Inability to manage new data sources
• Schema on Write vs. Read

New Data Paradigm- Winners will be Proactive not Reactive!
2.8 zettabytes
in 2012
44 zettabytes
in 2020
N E W
1 zettabyte (ZB) = 1 million petabytes (PB); Sources: IDC, IDG Enterprise, and AMR Research
Clickstream
PROD, LOE, SHE
Web & social
Geolocation
Internet of Things
Server logs
Files, emails
Transform every industry via
full fidelity of data and analytics
Opportunity
T R A D I T I O N A L
LAGGARDS
LEADERS
Ability to
Consume Data
Enterprise Blind
Spot
90% of all information created by humans originated in the last 2 years

Thank you for the diagram, Robert Wood Johnson
Foundation, 2014
7
Comprehensive Health
Management
80% of healthcare determinants
lie outside
the US healthcare delivery
system
Can healthcare systems expand
into these other areas, and
become true public health
systems?

Imagine if your physician could say this to you…
“I can make a health optimization recommendation to you based on…
 The latest clinical trials
 Your genomic make up and family history
 The local, regional, national & International data about patients just like you
 The real-world health outcomes over time of every patient like you
 the level of your interest and ability to engage in your own care
I can then tell you within a specified range of confidence, which treatment has the
greatest chance of success for you.”
Thank you Dale Sanders at Health Catalyst for the quote

Learning from other industries!
Step 1:
Retail Approach-
• Build a Central Data Repository to store and process all longitudinal patient history “Data Lake”
• Analyze information across multiple touch points and episodes of care within the Healthcare
system or across Providers, Clinics, pharmacies, Home Health, and specialty providers
Step 2:
Predictive Modeling/Analytics-
• 360 degree Patient view used to build Predictive Models around high cost/high quality patients
e.g. long-term chronic disease management
• Data gathered on patients after they leave the care setting enables more timely visibility into
patient behavioral/clinical changes.

Original 24 architects, developers, operators of Hadoop from Yahoo!
ONLY
100open source
Apache Hadoop data platform
% Founded in 2011
HADOOP
1ST
provider to go public
IPO Fall 2014 (NASDAQ: HDP)
subscription
customers800 employees across
740+
countriestechnology partners
1350+ 17TM
Hortonworks Company Profile

Fastest growing Fortune 1000 customer base
Customer Momentum
• More committers to Hadoop than any other distribution
• Non proprietary = FREE with no vendor lock in
• Fastest software company in history to 100M revenue- 4 years
(Barclays)
Largest Cluster in North America
32,000 Nodes
Largest Cluster in Europe
1,000 Nodes
Some notable migrations include many of the early adopters of Hadoop:
© Hortonworks Inc. 2011 – 2014. All Rights Reserved
Experience at Scale
80,000 nodes under contract
Largest Known Cluster in APAC
400 Nodes
• 30+ customers migrated from other distributions

Use Cases - Providers

Historical and Legacy system data offloadHealthcare
Problem
Legacy system licensing when moved to new EMR
 22 years of data for 1.2 million patients ~ 9 million records
 Data on legacy system was not searchable nor retrievable
 Cost to move data to new system not feasible
 Difficulty in combining data from 2 systems
Solution
Unified repository provides data to both researchers & clinicians
 “View only” legacy system retired, saving $500K
 9 million historical records now searchable & retrievable
 Records stored with patient identification for clinical use, same data presented
anonymously to researchers for cohort selection
MPP
SAN
Engineered System
NAS
HADOOP
Cloud Storage
$0 $20,000 $40,000 $60,000 $80,000 $180,000
Fully-loaded Cost Per Raw TB of Data (Min–Max Cost)
Hadoop Enables Scalable Compute & Storage at a
Compelling Cost Structure:
Storage Costs and licensing reduction of
latent systems
$500,000/year
5 x the amount of usable storage, plus 5 x
processing power, for about 30% of the
cost of traditional technologies,”

14 © Hortonworks Inc. 2011 – 2016. All Rights ReservedPage14 © Hortonworks Inc. 2011 – 2015. All Rights Reserved
A Healthcare Data Platform
For a Single View of the Patient
 Mercy
– 35 hospitals and 700+ clinics, 1 million patients annually, Multi State coverage
– One of the earliest and largest EPIC deployments
Objective #1 Improved Documentation- Billing & Coding Accuracy
Objective #2 Improved Clinical Documentation
Objective #3 Real-Time sensor data streaming- Virtual Care Center (VCC)

Mercy, St Louis
Objective #1- Coding & Billing Accuracy
Problem
• Chart queries not being answered in a timely manner which produces problems with
coding and payment accuracy. MDS (Medical Documentation Specialist) wastes
time asking the same query without responses. Escalating problems occur too late
in the billing cycle
• MDS staff use ”gut instincts” to determine which charts to review. There is 1 MDS for
every 3,000 patients
Solution
• Identify the specific patient care paths to most likely include a secondary diagnoses and set
up clinical rules to identify additional care delivery e.g. certain lab orders.
• Identify physicians with the lowest probability of answering follow up queries and create
business rules to rank cases to determine which ones are highest value and most likely need
2nd review.
Benefits
Visibility into undocumented
secondary diagnoses and
billing omissions
Improved staff utilization
and chart review accuracy
More timely and accurate
billing process resulting in
1M + additional annual
revenue from documented
secondary diagnoses and
care
Ability to predict physicians
documentation omission
patterns
Healthcare

Mercy, St Louis
Improved Clinical Documentation & Analytics
Problem
Moving Epic EMR data to Clarity EDW took 24 hours and was “never going to
enable near real time analytics
Difficulty in combining non traditional (unstructured data) with structured data
Solution
Needed to get ahead of the EMR-EDW lag time…
Mercy replicated all of its Epic data in the Hortonworks Hadoop data platform
• Epic data is enriched with data from other internal systems and publicly available
data (Lawson, etc.,)
• NLP and advanced OCR applications deployed to drive data exploration and
discovery
Benefits
One query on 19,000 individuals
took two weeks on the current
data architecture, but it ran in 0.5
days on HDP
Hyperlinks built to bring visibility
to updates pushed back into
EPIC
Current lag time of 3-5 minutes
for new clinical entries
Mercy searches through terabytes
of free-text lab notes, speeding
clinical based insights from “never”
to “seconds”
Healthcare

Near Real Time Analytics with Epic
EPIC W P
Oracle
Clarity
W P
Batch
Governance
&Integration
Security
Operations
Data Access
Data Management
Near Real-time
APPLICATIONS
Business
Analytics
Custom Applications
Packaged
Applications
Sqoop
The Process:
Initial bulk load via sqoop from
Oracle/Clarity EDW to HDP. They then
capture deltas every 3-5 minutes from
cache into HDP.

Monitor Patient Vitals in Real-Time with
Sensor data
Problem
Managing The Volumes of System Sensor Data
 In a typical hospital setting, nurses do rounds and manually monitor patient vital signs. They may
visit each bed every few hours to measure and record vital signs but the patient’s condition may
decline between the time of scheduled visits.
 This means that caregivers often respond to problems reactively, in situations where arriving earlier
may have made a huge difference in the patient’s wellbeing.
Solution
Hadoop Empowers Healthcare by Converting High Volumes of Sensor Data into a Manageable
Set of Data
 New wireless sensors can capture and transmit patient vitals at much higher frequencies, and these
measurements can stream into a Hadoop cluster.
 Caregivers can use these signals for real-time alerts to respond more promptly to unexpected
changes.
 Over time, this data can go into algorithms that proactively predict the likelihood of an emergency
even before that could be detected with a bedside visit.
Benefits
Proactively Predict Events rather
than reactively
Real-time Alerts
Capture & Transmit Patient
Vitals at Much Higher
Frequencies
Improve Patient Satisfaction
Improved response times
Reduce adverse drug response
times
Healthcare

Real Time Predictive
care
Mercy’s Journey- The Virtual Care Center (VCC)
Clinical
Docs
Vital Sign
Monitoring
Single
Patient
Record
Lab Notes
Archive
Privacy
Database
Medical
Decision
Support
Device
Data
Ingest
Preventive
Care
The path to Real Time Care
• First of its kind in the World!
• 24/7/365 monitoring
• $54M Investment with 330 specialized
medical professionals
• 36 Medical Centers/33 ED’s
• 478 Critical Care beds/28 Critical Care
units
• 2,431 Acute Care beds
• 15% reduction in LOS

Yield, Quality & Process Optimization at Merck
Problem
Merck was seeing higher-than-usual discard rates on certain vaccines. The team was
looking into the causes of the low vaccine yield rates, but the usual investigative approach
involved time-consuming spreadsheet-based analyses of data collected throughout the
manufacturing process.
Key Challenges
• Data Silos
• High Cost of Data Retention
• High Cost of Testing Hypothesis in the Real World
The Solution
 Hadoop enables the pharmaceutical company to crunch huge amounts of data resulting in the ability to develop
and bring to vaccines to market faster and at lower cost.
 Through 15 billion calculations and more than 5.5 million batch-to-batch comparisons, Merck discovered that
certain characteristics in the fermentation phase of vaccine production were closely tied to yield in a final
purification step.
Plans for the Future
• Analyze Streaming Machine Sensor Data in Real time
• Proactively minimize Yield Variability
• Predictive Equipment Maintenance

Other common use cases with Hadoop
• Low cost & searchable centralized storage of genomic data, PDF’s, Images, etc.
• Storage of ICU device data
• Storage of large data not persisted in another location or repository
• RTLS – Real Time Location System Patient, doctor, and hospital staff real time location tracking within
the hospitals
• EPIC/Cerner Integration
• Medication Safety- Error reduction through use of data
• Clinical Trial Cohort Identification
• Breach/Threat Security Assessment
• Store medical research forever
• Theft & Abuse- “Drug Diversion”
• 360 View of drug safety
• Clinical Text note search (Google type search)
• Population Health initiatives (re-admissions)
• Wearable's Monitoring Analytics
• Payment Integrity/Fraud Streaming

Join the Hortonworks Healthcare
User Group!
• Monthly 1-hour calls covering thought
leadership topics
• Past speakers: Mercy, UC Irvine, Mayo
Clinic, Cardinal Health, Zirmed,
Intermountain Health, CHOLA…
• 125+ members!
"If I haven’t said it loud enough, this is an awesome
forum!” Paul Vee, CHOLA
“This is one of the best uses of my time.” Lonny Northrup,
Intermountain Healthcare

Lessons Learned…
23
1. Like it or not: your business and success runs at the speed of software so IT
leadership needs to lead on Hadoop
2. No Question: The Hadoop ecosystem is revolutionizing data management and
analytics and evolving faster than any one company possibly can.
3. Adoption Curve: Start learning and adopting now; be ready for full adoption
soon to stay competitive
4. There is only one Hadoop licensed by the Apache software foundation…choose
your distribution carefully to avoid vendor lock from proprietary solutions as
you scale out!
5. Our best customers are our most involved customers!

HPE Hadoop Architecture

HPE’s Hadoop Architecture is the difference!!!
Rapid design and
deployment
Multiple Solution
Options
Built and tested for
Hortonworks
Reduce cost, risk,
and errors
$

HPE offers three (3) Deployment Options
− Hortonworks tested
− Traditional, symmetric
− Standard Rack Co-location
− Entry Big Data system
− Small deployments (~20 nodes)
− Purpose-built, symmetric
− Mid-size to large deployments
− Workload/Storage optimization
− High density, lower power
− Asymmetric architecture
− Mid-size to large deployments
− Multiple/Dynamic workloads
− High density, lowest power
HPE ProLiant DL Server HPE Apollo Storage
Server
Moonshot Server and
Apollo Storage

Apollo 4530 System Hortonworks Reference Architecture
Purpose-built for Hadoop and Big Data analytics
Analytics
At scale
Versatile
performance
Unleash the full value of Big Data with Hadoop
42U rack comparison
Apollo 4530
- 1 management
node
- 2 head nodes
- 27 worker nodes
- 1.8 PB raw storage
2U rack mount server
- 1 management node
- 2 head nodes
- 18 worker nodes
- 960 TB raw storage
Apollo 4530 advantage
33% more compute
2X storage capacity

New approach to address Big Data demands
Two Socket, 2U Servers
YARN Apps,
HDFS, ORC
Files, Parquet,
Hbase,
Cassandra
Compute Optimized Servers
Storage Optimized Servers
YARN Apps
HDFS, ORC
Files, Parquet,
Hbase,
Cassandra
− Separate processing /storage tiers connected
by Ethernet networking
− Standard Hadoop installed asymmetrically
• storage components on storage servers
• yarn apps on processing servers
− HPE Moonshot, HPE Apollo 4200; HPE
Apollo 2000
HPE Big Data Reference Architecture
− Processing / storage always collocated
− All identical servers
− Data partitioned across servers on direct-
attached storage (DAS)
Traditional Big Data Approach

Unique ValueHPE Apollo & Moonshot Combined Solution
HPE Big Data Reference Architecture for Hadoop
Innovation delivering unique value to customers and the open source
community
29
Data Consolidation
− Shared storage pool for multiple Big Data
environments
Maximum Elasticity
− Dynamic cluster provisioning from compute
pools without repartitioning data
Flexible Scalability
− Scale compute and storage independently
Breakthrough Economics
− Workload optimized components for better
density, cost and power
Ethernet (RoCE)
HPE Apollo 4000
HPE Moonshot or HPE Apollo 2000

Advantages* of HPE Big Data Reference Architecture
A new standard for Big Data delivery at scale
30
HPE Big Data Reference
Architecture
* Normalized on performance
Traditional
Architecture
Traditional
Architecture
BDRA
Hadoop
performance
Equivalent
Density >2x more dense
Network
bandwidth
40Gbit versus
10Gbit
HDFS Storage
performance
2x greater
Power (watts) Half the power

Healthcare Customer Stories
Visit us at:
 http://hortonworks.com/industry/healthcare/

Thank You!

HPE and Hortonworks join forces to Deliver Healthcare Transformation

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HPE and Hortonworks join forces to Deliver Healthcare Transformation