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Using Advanced Analytics for
Value-based Healthcare Delivery
Copyright © Prime Dimensions 2013 All rights reserved.

2.
We are a DC-based consulting firm that provides advanced
analytics capabilities and implementation services
 Prime Dimensions offers expertise in the processes, tools and techniques
associated with:
 Data Management
 Business Intelligence
 Advanced Analytics
 We focus on operational aspects and emphasis on Big Data strategy and
technology
 We assist organizations in transforming data into actionable insights to improve
performance, make informed decisions and achieve measurable results.
 We partner closely with clients to ensure cost-effective implementations.
Big Data requires a new generation of scalable
technologies designed to extract insights from very large
volumes of disparate, multi-structured data by enabling
high velocity capture, discovery, and analysis.
Copyright © Prime Dimensions 2013 All rights reserved.
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3.
Agenda
 Challenges & Opportunities
 Payment Reform
 Big Data Enabling Technologies
 Big Data Roadmap
Copyright © Prime Dimensions 2013 All rights reserved.
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4.
Typical Client Challenges
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5.
Healthcare in the 21st Century
“The transformational force that has brought affordability and accessibility to
other industries is disruptive innovation. Today's health-care industry screams for
disruption. Politicians are consumed with how we can afford health care. But
disruption solves the more fundamental question: How do we make health care
affordable?”
Clayton M. Christensen, The Innovator's Prescription: A Disruptive Solution for
Health Care
National Challenges






Escalating Costs
Dwindling Budgets
More Oversight and Scrutiny
Expectation of Transparency
and Accountability
New Laws, Regulations and
Polices
Focus on Performance
Transformational Opportunities




Radical innovation
Go beyond measuring
outcomes to changing them
Aggregate and/or discover data
in ways that were not possible
until recently
Visualize problems in humanfriendly formats
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6.
Current U.S. Healthcare Situation
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7.
Medicare Accounts for Over 20% of Total U.S.
Healthcare Spending in 2010
Note: Other health insurance
programs includes DoD and VA
Source: Medicare Payment Advisory Committee, June 2012 Data Book
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8.
Healthcare Spending as a Percentage of GDP
In 2010, total U.S.
healthcare spending was
$2.6
trillion, representing
18% of Gross Domestic
Product, doubling over
the past 30 years. If this
trend
continues, estimates are
that healthcare
spending will reach a
staggering $5 trillion, or
20% of GDP, by 2021.
Moreover, patients’ outof-pocket costs have
doubled over the past 10
years.
Source: Medicare Payment Advisory Committee, June 2012 Data Book
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9.
Transforming the Healthcare Ecosystem
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10.
Agenda
 Challenges & Opportunities
 Payment Reform
 Big Data Enabling Technologies
 Big Data Roadmap
Copyright © Prime Dimensions 2013 All rights reserved.
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11.
The DNA of Healthcare Data
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12.
Unsustainable Fee-for-Service (FFS)
Payment Model
 Current FFS payment structure results in redundant testing, medical
errors, and over-utilization of the system




Maximizes providers’ fees and reimbursements based on volume
Incentivizes multiple tests and procedures, regardless of necessity
Limited coordination of care across provider network
Medical errors and failed procedures result in higher fees for providers
 Data overload: vital health information assets are not being leveraged
 96% of Medicare dollars account for treating chronic illnesses, with
only 3-5% for prevention (Source: CMS, Chronic Conditions Chartbook, 2012)
Research indicates that payment reform would reduce spending by
$1.33 Trillion by 2023 and significantly improve quality and outcomes.
(Source: The Commonwealth Fund Commission on a High Performance Health System, 1/13)
Copyright © Prime Dimensions 2013 All rights reserved.
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13.
Four Major Payment Reform Models
Global
Payments
• A single
PMPM
payment is
made for all
services
delivered to
a
patient, with
payment
adjustments
based on
measured
performanc
e and
patient risk.
Bundled
Payments
Patientcentered
Medical
Home
• A single
• A physician
“bundled”
practice or
payment, w
other
hich may
provider is
include
eligible to
multiple
receive
providers in
additional
multiple
payments if
care
medical
settings, is
home
made for
criteria are
services
met, based
delivered
on quality
during an
and cost
Copyright Prime
episode ©of Dimensions 2013 All rights reserved.
performanc
Accountab
le Care
Organizati
ons
• Groups of
providers
that
voluntarily
assume
responsibilit
y for the
care of a
population
of patients
share payer
savings if
they meet
quality and
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cost

14.
ACO Implementation Process
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Solution
 We selected three CMS programs for analytics: Accountable Care
Organizations (ACO), Bundled Payments, and Patient Centered
Medical Home.
 The focus of Phase 1 of the prototype is on ACOs.
Sources:
Bundled Payments for Care Improvement (BPCI) Initiative: General Information, http://innovation.cms.gov/initiatives/bundled-payments/
Against this backdrop, CMS has launched new delivery and payment initiatives aimed to improve patient outcomes and quality while containing costs. We have
selected the following programs for this business case: Bundled Payments, Accountable Care Organizations, and Patient Centered Medical Home.
Bundled Payments for Care Improvement (BPCI) Initiative: General Information, http://innovation.cms.gov/initiatives/bundled-payments/
Copyright © Prime Dimensions 2013 All rights reserved.
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Payment Reform Solution
 Helps healthcare payers find the optimal cost, revenue, and provider
incentive models to improve health outcomes and contain patient cost
 Embeds business rules and algorithms of the Medicare Shared Savings
Program (MSSP) Accountable Care Organization (ACO).
 Includes the following features and capabilities:
 ACO benchmarks and budget based on historical cost baseline, trend
estimates and risk adjustments
 Performance monitoring of key measures and metrics related to cost,
utilization and quality
 Predictive modeling to determine the proper mix of inputs to
maximize payment incentives
 Dynamic dashboards and visualizations to perform trade-off analysis
and scenario planning
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Methodology and Scope
 The team acquired and loaded public datasets from
CMS and related sources
 Phase 1 scope:
In-patient Medicare data for 2010
Sampling of 5 metropolitan areas representing a range
of regions and population sizes (CA, FL, MA, OH, GA)
 Diabetes, hypertension, coronary disease, and heart
failure measures
 Readmissions and ER utilization


 Created scenarios for comparative analysis and
benchmarking

Population, cost, clinical and utilization data
 Analytical models and predictive modeling
Seeking input from SMEs
 Applying industry best practices
 Assessing technical solutions

Data Sources
1. Data Entrepreneurs’ Synthetic Public Use
File
2. Institutional Provider & Beneficiary
Summary
3. Data for ACO Applicant Share Calculations
4. CMS Statistics reference booklet for the
Medicare and Medicaid health insurance
programs
5. National Health Expenditure Accounts
(health care goods and services, public
health activities, government
administration, the net cost of health
insurance, and investment related to health
care)
6. Premier Hospital Quality Incentive
Demonstration (expanding the information
available about quality of care and through
direct incentives to reward the delivery of
superior quality care)
7. Hospital Compare (including 27 quality
measures of clinical process of care and
outcomes)
8. Assorted files from the CMS Download
Database
9. The Dartmouth Atlas of Health Care
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18.
Agenda
 Challenges & Opportunities
 Payment Reform
 Big Data Enabling Technologies
 Big Data Roadmap
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Big Data Enabling Technologies
 New systems that handle a wide variety of data from
sensor data to web and social media data
Big Data requires a new
 Improved analytical capabilities (sometimes called
generation of scalable
advanced analytics) including event, predictive and technologies designed to
extract meaning from very
text analytics
large volumes of
 Operational business intelligence that improves disparate, multi-structured
data by enabling high
business agility by enabling automated real-time
velocity
actions and better decision making
capture, discovery, and
analysis.
 Faster hardware ranging from faster multi-core
processors and large memory spaces, to solid-state  Massively Parallel
Processing
drives and virtual data storage
 Hadoop and MapReduce
 Cloud computing including on-demand software-as- NoSQL Databases

a-service (SaaS) and platform-as-a-service (PaaS)
 In-Memory Technology
analytical solutions in public and private clouds
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Massively Parallel Processing
 Massively parallel processing (MPP) is the coordinated processing of a program by
multiple processors that work on different parts of the program, with each processor
using its own operating system and memory
 Known as “loosely coupled” or “share nothing” systems
 Enables “analytic offload”
 Permits parallelized data loading, i.e. E-L-T vs. E-T-L
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21.
The Hadoop Ecosystem
Compute Cluster
Multi-structured
Source Data
Master Node
NameNode
DataNode
Slave Node
JobTracker
TaskTracker
DataNode
TaskTracker
Slave Node
DataNode
TaskTracker
Slave Node
MapReduce
Engine
DataNode
Slave Node
TaskTracker
YARN
DataNode
TaskTracker
Slave Node
DataNode
TaskTracker
DATA LAYER
WORKLOAD MANAGEMENT LAYER
APPLICATION LAYER
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22.
The Hadoop Ecosystem
Compute Cluster
ApplicationMaster
Multi-structured
Source Data
ResourceMaster
Master Node
NameNode
DataNode
Slave Node
NodeMaster
DataNode
JobTracker
TaskTracker
TaskTracker
Slave Node
NodeMaster
DataNode
TaskTracker
Slave Node
NodeMaster
DataNode
Slave Node
NodeMaster
DataNode
MapReduce
Engine
TaskTracker
YARN
MapReduce 2.0
TaskTracker
Slave Node
NodeMaster
DataNode
TaskTracker
WORKLOAD MANAGEMENT LAYER
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23.
NoSQL (Not only SQL) Databases
 Like HDFS, NoSQL databases have distributed, schema-less data structure
and high scale out across low cost commodity hardware
 NoSQL databases are not reliant on a relational, fixed data model and
Structured Query Language (SQL)
 NoSQL databases are not ACID-compliant and lack the strict data integrity of
relational databases
 NoSQL databases have BASE properties – Basically Available, Soft
state, Eventual consistency
 NoSQL databases are queried via REST APIs.
Key-Value
Columnar
Document/Object
Graph
Increasing Data Complexity
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24.
In-memory Database


An in-memory database stores (IMDS) data entirely in main memory using NVDIMMs, as opposed to
disk-based storage
Storing and retrieving data in memory is much faster than writing to and reading from disc



Reduce data access and latency caused by I/O operations and significantly speeding query and response
times.
This trend toward in-memory is made possible by low memory prices and multi-core 64-bit
processing capabilities.
Data stored entirely in-memory is immediately available for analytic processing
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25.
Database Options
Data Velocity
Scale Up
Analytic
Databases
Relational
Databases
NoSQL
Databases
Scale Out
Data Variety
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26.
Agenda
 Challenges & Opportunities
 Payment Reform
 Solution Prototype
 Big Data Enabling Technologies
 Big Data Roadmap
Copyright © Prime Dimensions 2013 All rights reserved.
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27.
Our roadmap reduces risk while progressing towards a
unified analytics environment
Continuous improvement & innovation
Changes to
laws
(e.g., ACA)
Permanent
budget
pressure
Information
Management Health
Assessor
Design Thinking & Prototyping
Mission
Oriented
Analytics
Framework
Unified Analytics
Environment
Evolving On-Demand Infrastructure
Commodity IT
Open source tools
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Smarter, mor
e demanding
data
consumers &
partners

28.
Your Brain on Big Data
Query & Reporting
Discovery & Engagement
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Growth of Data Exploding
 Eric Schmidt: “Every 2 days we create as much
information as we did up to 2003.”
 Relational databases can not effectively ingest, store and
query the increased volume, variety and velocity of Big
Data
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30.
Begin with the End in Mind
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Cycles of Design Thinking and Prototyping
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Mission Oriented Analytics Framework
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33.
Evolving On-Demand Infrastructure for Big Data
Advanced Analytics
Analytic Offload
Scale-up/Scale-out
YARN
Storm
NoSQL Database
Tez
Analytic
Applications
REST/JSON
APIs
Dashboards &
Visualizations
Analytic
Database
E-L-T
In-memory
Columnar
HCatalog
Data Warehouse Augmentation
OLAP
Data
Warehouse
E-T-L
Multi-structured
And Stream
Source Data
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Data
Discovery
Structured Source Data

34.
Unified Analytics Environment
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35.
Data Management | Business Intelligence | Advanced Analytics
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Questions?
Our Contact Information
Michael Joseph
Managing Partner
Direct: 703.861.9897
Email: mjoseph@primedimensions.com
Richard Rowan
Managing Director
Direct: 703.201.2641
Email: rrowan@primedimensions.com
Prime Dimensions, LLC
www.primedimensions.com
Data Management | Business Intelligence | Advanced Analytics
Follow us @primedimensions
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