This document discusses the importance of data integration for new care models like patient-centered medical homes and accountable care organizations. It notes that electronic health records on their own are not sufficient, and that successful models require integrating data across settings to power analytics, clinical decision support, and population health management. The document outlines strategies for organizations, including developing strong data governance, clinical analytics capabilities, and health information exchange infrastructure to share information and coordinate care.

1. Data Integration – The Key to Successfully
Utilizing Information for Point of Care and for
Population Health
Charles DeShazer, MD
VP, Quality, Medical Informatics &
Transformation
Dean Health System

2. Overview
 Implications of New Emerging Care Models
 Implications of Medical Care Evolution
 Implications of Government Interventions
 Government Vision of HIT
 Critical Success Factor – Data Integration &
Distribution
 “Big Data” Analytics
 Key Strategies & Tactics

3. Overview
 Implications of New Emerging Care Models
 Implications of Medical Care Evolution
 Implications of Government Interventions
 Government Vision of HIT
 Critical Success Factor – Data Integration &
Distribution
 “Big Data” Analytics
 Key Strategies & Tactics

4. New Care Models
 Patient-Centered Medical Home
 Health care model that aims to provide structured,
proactive and coordinated care for patients.
 Accountable Care Organizations
 Group of health care providers (e.g. primary care
physicians, specialists and hospitals) that have entered
into a formal arrangement to assume collective
responsibility for the cost and quality of care of a specific
group of patients and that receive financial incentives to
improve the quality and efficiency of health care.
 Payment Driven Models
 Bundled payments
 Pay for Performance
 Case rates
 Capitation

5. ACO model represents a shift of COST RISK to
Providers through payment mechanisms…

6. Population vs. Costs vs. Interventions
Example of 100,000 People in a Population
% of
% of Cost
Population
Complex Case Management
1% 1000
25%
Lives
Disease/Demand
14% 14,000 Lives Management 50%
15% 15,000 Lives Health 15%
Mgmt
70% 70,000 Lives 10%
Approximately 75% of costs are due to chronic co

7. Healthcare Information Technology (HIT)
Requirements
PCMH ACOs
 Care Coordination  Cross Continuum
 Chronic Disease Medical Management
Management &  Member Engagement
Complex Case  Clinical Information
Management Exchange
 Population Health  Quality & Performance
Management (esp Reporting
registries)  Predictive Modeling &
 Patient Engagement & Analytics
Activation
 Administrative and
 Evidence-based Financial Risk
Medical Practice Management systems
 Real-time connectivity

8. EHR is necessary but not
sufficient
 In "Associations Between Structural Capabilities of Primary Care
Practices and Performance on Selected Quality Measures," Mark
Friedberg MD, and colleagues examine how a range of primary
care practice attributes, including having an EMR, may impact
physician performance on quality metrics.
 The research profiled 307 practices in Massachusetts across
2007.
 Across the practice characteristics and HEDIS metrics, the
attributes correlated to a practice's higher-quality performance on
diabetes and prevention metrics included: having an EMR,
frequent meetings to discuss practice quality performance, and
physician awareness of patient experience. EMRs were
specifically associated with higher performance on two diabetes
metrics (eye exams and nephropathy monitoring) and three
prevention metrics (breast cancer, colorectal cancer, and
chlamydia).
 Key insight: The key transformative aspect of the EMR's role
in the practice was shown to be providing information to
support decision-making--not just serving as a repository
Friedberg, M., et al, Annals of Internal Medicine, 2009; 151:456-463
for data.

9. Overview
 Implications of New Emerging Care Models
 Implications of Medical Care Evolution
 Implications of Government Interventions
 Government Vision of HIT
 Critical Success Factor – Data Integration &
Distribution
 “Big Data” Analytics
 Key Strategies & Tactics

10. Continued Evolution of the Medical
Care …
 Genomics
 New Technology
 Aging Population

11. Overview
 Implications of New Emerging Care Models
 Implications of Medical Care Evolution
 Implications of Government Interventions
 Government Vision of HIT
 Critical Success Factor – Data Integration &
Distribution
 “Big Data” Analytics
 Key Strategies & Tactics

12. Meaningful Use & ICD-10
 Meaningful Use
 Driving increased adoption of EHRs in-patient and ambulatory
 Penalty starts if not “meaningful user” by 2015
 Infrastructure for ONC vision and robust Clinical Decision Support
(CDS)
 ICD-10
 One of the most comprehensive regulatory changes in the history of
healthcare in the US
 Unlike MU, it is an unfunded regulatory event
 Replaces 30 year old ICD-9-CM, which is outdated and lacks clinical
granularity
 Provides granularity to diagnostic information that should
greatly enhance predictive models
 Improved ability to specify and measure healthcare services
 Enable better integration of predictive modeling and clinical
decision support
 Richer data structures for research

13. ICD-10 Asthma Codes More granular clinical
information will
enhance predictive
models as well as
enable real-time
program referrals
especially when
followed serially and
combined with other
data.

14. Overview
 Implications of New Emerging Care Models
 Implications of Medical Care Evolution
 Implications of Government Interventions
 Government Vision of HIT
 Critical Success Factor – Data Integration &
Distribution
 “Big Data” Analytics
 Key Strategies & Tactics

16. Overview
 Implications of New Emerging Care Models
 Implications of Medical Care Evolution
 Implications of Government Interventions
 Government Vision of HIT
 Critical Success Factor – Data Integration &
Distribution
 “Big Data” Analytics
 Key Strategies & Tactics

17. Data Management Lifecycle
Data Collection (QA) Data Extraction (ETL)
•Avoid GIGO •Critical Integration Step
• Collection workflows •Data Governance &
• Clarity of where to enter Master Data Management
data to be reportable •Data warehouse & marts
• Coding consistency and • Single source of truth
conventions (ICD-10) • Create Predictive &
Analytic Models
•Establish accountability •Leverage Analytics for
and feedback mechanisms Insights that drive
• Link with Lean efforts decisions and processes
• Identify gaps and new •Testing and Validation
requirements • Formatting for ACTION
•Learn from reports & • Visualization of data
change collection process • Delivery medium incl. CDS
Improvement Initiatives Information Delivery

18. Key Technical Infrastructure to support the
PCMH
EHR is necessary but not sufficient. The next level of quality management will
require an INTEGRATED Health Information Technology (HIT)
“ecosystem” especially a robust analytic infrastructure. Standalone EHR
may not be able to provide all of these functions.
Focus Area Key Technical Infrastructure
Care Coordination HIE, Workflow Management, Shared Care Plan,
Referral tracking
Chronic Condition Management CRM, Workflow Management, Shared Care
& Complex Care Management Plan, Predictive Modeling, CDS, Telehealth,
Registries
Population Health Management CRM, HRA, Predictive Modeling, Workflow
Management, CDS, Population analytics,
Registries
Patient Engagement & Activation CRM, Shared Decision Making, Telehealth, PHR
Evidence-Based Medicine CDS, Workflow Management, Population
Practice analytics
Real-Time Connectivity HIE, Telehealth, mobile technology, unified
messaging

19. CSC ACO Maturity Stages

20. Overview
 Implications of New Emerging Care Models
 Implications of Medical Care Evolution
 Implications of Government Interventions
 Government Vision of HIT
 Critical Success Factor – Data Integration &
Distribution
 “Big Data” Analytics
 Key Strategies & Tactics

21. “Big Data” Analytics
 Recent IDC research on digital data indicates that in 2010, the amount of digital
information in the world reached beyond a zettabyte in size. That's one trillion gigabytes
of information. To put that in perspective, a blogger at Cisco Systems noted that a
zettabyte is roughly the size of 125 billion 8GB iPods fully loaded.
 The increasing velocity, variety and complexity of data is overwhelming traditional
datawarehouse tools, techniques and infrastructure.
 Healthcare has a particular need to manage data well as EHRs become common, use of
devices increase, integration of multimedia and imaging becomes important, integration
with social networking resources becomes useful and genomics data becomes essential
for decision-making.
 New high performance hardware, software and techniques are emerging to address this
issue called “Big Data” Analytics.
 Gartner contends that terms like "big data," "real-time data" and "linked data" signal a
new era in which the economics of data (not the economics of applications, software or
hardware) will drive competitive advantage.
 What does this mean? It doesn't matter which EHR you have from a competitive
standpoint. Competitive advantage will come from (1) the quality of the data you
collect, (2) how you integrate the data and provide analytics to drive insights and
(3) how well you use these insights to drive customer experience/relationships and
business results.

22. Overview
 Implications of New Emerging Care Models
 Implications of Medical Care Evolution
 Implications of Government Interventions
 Government Vision of HIT
 Critical Success Factor – Data Integration &
Distribution
 “Big Data” Analytics
 Key Strategies & Tactics

23. Leverage Meaningful Use as a Springboard
Criteria Opportunity
Problem List Define system-wide standards and policies,
improve accuracy of documentation, infrastructure
for CDS, use for shared care plan
AVS & PHR Enhance quality, consistency and usefulness of
content (esp. for chronic condition management),
fully operationalize PHR, enhance patient
engagement, use for shared care plan, leverage to
engage family & caregivers
Medication List Improve medication reconciliation and
management of transitions of care.
Patient Lists & Enhance analytics, create robust registries and
Structured Data dashboards, infrastructure for CDS
Clinical Decision Create governance structure, establish standards,
Support focus them on key areas of improvement
opportunity, avoid alert fatigue
Quality Measures Expect to be held accountable for results, create

24. Key Tactics & Strategies
 Implement & Optimize your EHR
 Consider "Big Data" Analytics
 Develop an effective data governance and master data
management strategy
 Develop your Clinical Analytics unit
 Enhance your CDS infrastructure
 Decide on and commit the organization to an improvement
methodology, this is the cultural change tool
 Invest in workflow optimization (good use for Lean
techniques)
 Docs should manage by exception
 Consider how you will create a “Shared Care Plan” (SCP)
to ensure all providers and the patient are on the same
page
 Create enterprise and community infrastructure for health
information exchange and CRM at the ACO level 24
 Develop approaches to activate consumer/patient & family