Implementation, Change Management and the Application of Healthcare Analytics

Implementation, Change
Management and the Application of
Healthcare Analytics
The Fellowship of the Ring of Analytics
Copyright 2015, Healthcare Center of Excellence

Let’s Go on a Journey…
• Imagine you are on a journey to master your
data and bring a healthcare analytics program
to your organization
– Who will you need on this journey with you?
– What tools will you need?
– What organizational changes will be needed?
– How long will it take?

Don’t Really Have to Imagine, Do You?
• You’re already
living this dream
• Or is it a
nightmare?

Today’s Journey
• Background on getting started with analytics
– Healthcare Transformation Model
• Implementing Healthcare Analytics
– Readiness Assessment
– Critical Success Factors
– Overcoming Challenges
• Competing on Healthcare Analytics
– What is it?
– How is it accomplished?

Your Narrator for Today
• Professor Bryan Bennett
– Northwestern University
• School of Professional Studies
• Teach Predictive Analytics and Data Management Courses
– West Virginia University
• Integrated Marketing Communications Graduate Program
• Teach “Audience Insight” Core Course
– Study of consumer behavior through research, analysis and
Ethnography
– Judson University
• School of Leadership and MBA Programs
• Teach Marketing, Leadership and Information Technology
Courses

Professor Bryan Bennett – Data Scientist
• Focus on healthcare transformation and analytics
• National Speaker on healthcare topics
• Lean Six Sigma Green Belt, Certified Public
Accountant & Project Management Professional
• Career highlights include:
– Design of Microsoft’s Global Customer Analytics Group
Structure
– CRM implementation vision recognized by the Gartner
Group
– Analysis leading to Rogaine for Women Introduction

Professor Bryan Bennett - Author
• Contributed to Adaptive Health Management
Information Systems book with:
– Authored Chapter 10 – “Data Stewardship: Foundation for
Health Management Information System Design,
Implementation, and Evaluation”
• HIMSS Future Care technology and big data
blogger
• Executive Director and lead researcher for
Healthcare Center of Excellence
www.healthcarecoe.org

Professor Bryan Bennett - Author
• Competing on Healthcare
Analytics
– Guide to succeeding in today’s
new healthcare industry
• Using People, Process and
Technology Powered by Leadership
• Expected publication: August
2015

The Fellowship of the Ring of Analytics

But First – The Prequel
Getting Started with Predictive Analytics
Healthcare Analytics Symposium 2014 Workshop
Background Material

What is Predictive Analytics?
• Predictive analytics is the practice of extracting
information from existing data sets in order to
determine patterns and predict future outcomes
and trends.
• It does not tell you what will happen in the
future.
– It forecasts what might happen in the future with an
acceptable level of reliability, and includes what-if
scenarios and risk assessment.

Gartner Even Goes Further
• In addition to predicting what might happen, they
add:
– Analysis measured in hours or days (real-time or near
real-time).
– The emphasis on the business relevance of the
resulting insights, like understanding the relationship
between x and y.
– An emphasis on ease of use, thus making the tools
accessible to business users.
Source: www.gartner.com

Gartner Analytic Ascendancy Model

Healthcare Transformation Model
The continuums and steps required for a healthcare organization to
transform from a paper-based organization to one that is data-
enabled.

Healthcare Transformation Change Model
Paper-Based
Healthcare
Organization
Data-Enabled
Healthcare
Organization
DATA / TECHNOLOGY
ORGANIZATIONAL
/ PEOPLE
PROCESS /
WORKFLOWS

Paper-Based
Healthcare
Organization
Data-Enabled
Healthcare
Organization
Resistance
To Change
Isolated
Acceptance
Growing
Importance Embraced
Throughout
Organization
DATA / TECHNOLOGY
ORGANIZATIONAL
/ PEOPLE
PROCESS /
WORKFLOWS
Real-Time
Decision
SupportEHR
Implementation
Analysis &
Modeling
Minimal
Data
Capture
Network-Wide
And Outside
Data Capture
Data Integration,
Discovery &
Interconnectivity
Integrated
Mgmt & Support
Stage 1 Stage 2 Stage 3 Stage 4
Integration of Clinical
& Non-Clinical
Data Sources

Implementation Stumbling Blocks
• Data / Technology
– Difficult integration of other data sources
– Lack of mainstream analytical and modeling tools
– Lack of data standardization
• Organizational / People
– Resistance to change by providers
• Process / Workflows
– Accurate and timely data capture
– Process redesigns for efficiency
– Interconnectivity

Transformation Stumbling Blocks
Paper-Based
Healthcare
Organization
Data-Enabled
Healthcare
Organization
Resistance
To Change
Isolated
Acceptance
Growing
Importance Embraced
Throughout
Organization
DATA / TECHNOLOGY
ORGANIZATIONAL / PEOPLE
PROCESS / WORKFLOWS
& Non-Clinical
Data Sources
Real-Time
Decision
SupportEHR
Implementation
Analysis &
Modeling
Minimal
Data
Capture
Network-Wide
And Outside
Data Capture
Data Integration,
Discovery &
Interconnectivity
Integrated
Mgmt & Support
Stage 1 Stage 2 Stage 3 Stage 4

Healthcare Transformation State of Analytics
• Most organizations can do some form of
Description Analytics from their current legacy or
EHR systems
• With some additional data management, many
are capable of performing some Diagnostic
Analytics
• Predictive Analytics are being implemented by
many advanced healthcare organizations
• Prescriptive Analytics is only being implemented
by the most elite organizations

Predictive
Prescriptive
Diagnostic
Descriptive
DATA / TECHNOLOGY
ORGANIZATIONAL
/ PEOPLE
PROCESS /
WORKFLOWS
Paper-Based
Healthcare
Organization
Data-Enabled
Healthcare
Organization
Resistance
To Change
Isolated
Acceptance
Growing
Importance Embraced
Throughout
Organization
Minimal
Data
Capture
Network-Wide
And Outside
Data Capture
Data Integration,
Discovery &
Interconnectivity
Integrated
Mgmt & Support
EHR
Implementation
Analysis &
Modeling
Integration of
Data Sources
Real-Time
Decision
Support
2014
2015

Continuums Key Takeaways
• Organizations are not as far down the
continuums as they might think
• Getting buy-in from the providers is a hurdle but
can be overcome with a well conceived game
plan that also addresses processes
• Training and communication is key to the success
of your transformation
• A transformational viewpoint is necessary for a
predictive analytics program to succeed

Process Study Conclusions
• Organizations might be overemphasizing technology to
the detriment of good processes
• Continuum misalignment can be illustrated by an
inverted triangle with the technology receiving the most
emphasis and process receiving the least
Improperly Aligned Continuums Properly Aligned Continuums
Technology
People
Process
Technology
People
Process

Failure is Not an Option
• The choice and deployment of technology is just as
important as training personnel which is just as
important as the workflow design or redesign
• Any attempt to do otherwise will result in failure
• This failure could come in the form
– Poor physician acceptance
– Missed diagnosis information
– Unrecorded diagnosis information
– Leading to incorrect or incomplete business intelligence

Don’t Fail Before Getting Started
• Organization transformation must start from
the beginning with communication and
personnel training throughout the process to
increase acceptance and advocacy among the
staff
• If training and information on the software is
offered just before the software ‘goes live’ you
may have already failed

Predictive Analytics Implementation
• Needs executive support
• Needs a well-defined business challenge or
query
• Needs lots of data & good data management
• Need the right team
• Needs to be an integral part of the
organization’s operations
• Need to track results and update models

Healthcare Transformation Model
Leadership
Paper-Based
Healthcare
Organization
Data-Enabled
Healthcare
Organization
Resistance
To Change
Isolated
Acceptance
Growing
Importance Embraced
Throughout
Organization
DATA / TECHNOLOGY
ORGANIZATIONAL
/ PEOPLE
PROCESS /
WORKFLOWS
Real-Time
Decision
SupportEHR
Implementation
Analysis &
Modeling
Minimal
Data
Capture
Network-Wide
And Outside
Data Capture
Data Integration,
Discovery &
Interconnectivity
Integrated
Mgmt & Support
& Non-Clinical
Data Sources

Executive Leadership
• Not a continuum
• Must be present in each stage of each continuum
– Consistent throughout transformation
• Most important critical success factor in any
change management situation
– Also known at Executive Sponsorship
– Lean Six Sigma
– Project Management

Importance of Executive Support
• Success is not all about the tools utilized or best
analyst
– Management support for analytics throughout the
organization has proven to be a critical success factor,
including:
• Top down mandates for analytics, sponsors and champions
– Drive cultural changes
• Being open to change and new ideas
• Having unified analytics-driven focus on the patient’s health
• Identifying and addressing operational threats to patient
care

Healthcare Leadership Challenge
• Healthcare Challenge
– Executives have to manage organization’s staff to get
their cooperation and buy-in
• Particular challenge working with providers who might
believe someone is trying to tell them how to practice
medicine
• Possible Solution
– Be involved!
– Get staff involved as early as possible
– Find advocates and utilize them to their fullest
– Focus on the benefits the transformation will offer

Well-Defined Business Problem
– Business challenges are everywhere. The real
problem is prioritizing which one to address first
– Find the challenge(s) that have the most potential
of showing quick results and improving care
• Picking ‘low hanging fruit’ is always good
• People lose interest in longer term projects if results
aren’t delivered soon

Data Needs
• A variety of data is needed for an effective
predictive analytics program
– Transactional and descriptive
• In many cases, the data is usually kept in
multiple silos across the organization
• A data warehouse is typically needed to
efficiently access all this data

The Problem With Data Silos
• Data silos are a repository of data stored and
used by a single or few departments in an
organization
• Usually does not exchange data with other
groups or departments
– Data may not be updated
• Impacts data integrity
• Executive sponsor is needed to “open” these silos

The Analytics Project Team
– The challenge will be finding qualified people from
an already scarce resource pool and getting them
to accept the lower wage healthcare may pay
– The entire team must be paid market wages
– Outsourcing might need to be an option
• Bottom Line: GET HELP!
– Especially when first starting

Integral Part of the Organization
– Everyone must buy-in to the results of the
analytics program including clinical, finance and
operational staff.
– Focus on the benefits of your analytics program
– Show real results to providers
• Avoid the impression that the analytics will tell them
how to practice medicine

Results Tracking and Model Updates
– With the right team in place this should not be an
issue
– Have the right team but also the right process to
manage and update the models
– Part of Analytics Program Lifecycle

Areas Showing Benefits Now
• Improved Patient Flow
• Disease Outbreak Prediction
• Emergency Room Risks
• Reduced Readmissions

Now Back to Our Story…

Implementing a Healthcare Predictive
Analytics Program
Competing on Healthcare Analytics

Where Do You To Start?
• According to a recent study:
– 41% of respondents identified data analytics and
business intelligence is a priority for 2015
• 81% have questions about where to start
– How much data to collect?
– What type of data to collect?
– How to turn the data into valuable insight?
– Still in very early stages for the industry
- Stoltenberg Consulting, 2015

Are You Ready?
• The organization needs to assess its analytics
readiness capabilities along 5 dimensions:
– Leadership
– Organizational
– Personnel
– Technology
– Data

Readiness Capability Assessment
Analytics
Readiness
Data
Readiness
Technology
Readiness
Personnel
Readiness
Organizational
Readiness
Leadership
Readiness
Experience driving
organization change?
Possess exceptional
critical thinking skills?
Good relationships with
key department leaders?
Is all needed data included
in a warehouse?
Infrastructure and
security in place?
Have you identified the right
tools and technology?
Is there familiarity with
using 3rd party data?
What are the data
quality risks?
Are good data capture
processes in place?
Is the organization
ready for analytics?
Have you identified
sources of ‘quick wins?
Have you identified
your analytical needs?
Does the organization
have the right talent?
Are the staff and
analytics team trained?
Is there a personnel
retention plan in place?

Leadership Readiness
• Does your leader has experience driving
organization-wide change?
• Does your leader have good relationships with
key department leaders?
• Does your leader possess exceptional critical
thinking skills?
• Is your leader adept at articulating his vision?

Organizational Readiness
• Is the organization ready for analytics?
• Have you identified your analytical needs?
– Defining metrics and what ‘good looks like’
• Have you identified sources of “quick wins”?

Personnel Readiness
• Does the organization have, or can acquire,
the right talent?
• Are the staff and analytics team trained in the
purpose of the program and how it will impact
them?
• Is there a personnel retention plan in place?

Technology Readiness
• Is all the data needed included in the data
warehouse?
• Are infrastructure and security procedures in
place to protect the data?
• Have you identified the right tools and
technology needed?

Data Readiness
• Are good data capture processes in place?
• What are the data quality risks?
• Is there familiarity with using 3rd party data?

Healthcare Executives Roundtable
• Invitation only group of healthcare executives from
around the country
– Discuss various healthcare transformation issues
• Meet by phone
– Participants vary each month
Titles Included Type of Organizations
Director, Informatics
Chief Medical Informatics Officer
Chief Analytics Officer
Chief Medical Officer
Senior VP for Medical Affairs
Non-profit health systems
University health care
Government entities

Roundtable Call on Population Health
• Building a population health COE is a multi-year journey
– Warrants cultural shift, alignment across organization and
continuous review
• Enterprise wide education is needed
• Not just a clinical / data / operational problem
• Successful organizations are patient centric and focus on
quality
– Most currently are hospital focused
– Clinicians must think as part of a larger, holistic care team
• Cleveland Clinic
• Leadership and personnel gaps were considered critical
success factors

Critical Success Factors
• Leadership Readiness
• Talent Gap
• Data Management
• Define Metrics

Leadership Readiness - CSF
• Many CEOs are former practicing physicians
• May not have the broad business experience
required to manage a total organization
transformation
• May be physician focused to the detriment of
entire transformation
– ‘Don’t have to use EHR’

Leadership Readiness - CSF
• Enterprise-wide solutions needs enterprise-
wide support
– Cross departmental silos
• Need sufficient resources
– Right people on the team
– Commitment from other departments
• Most important of all attributes
– Makes the others happen

Leadership Readiness Solutions
• Making sure you have the needed resources to
complete the project
– Financial and personnel
• Support staff needs bigger than expected
• Having a vision for where you’re going
• Building credibility with your team members
• Raising your team members to their potential
– High visibility
– Social leadership

Leadership and Data Analysis
• New area for most executives
• Requires skills not previously needed
– Know what looks good
• Must quickly understand what makes sense
• Data scientist would know data is wrong
– Measurement creates the illusion of insight
– People tend to believe charts and graphics even though it
doesn’t make sense or doesn’t answer the question at hand

Leadership and Data Analysis
• Requires skills not previously needed
– Identify where analytics adds value
• Data collection is not divorced from analytics
– Consider what business problem trying to solve before
collecting data
– Lead with confidence
• Know enough to feel it’s OK to ask questions

Talent Gap - CSF
• Competition for scarce I.T. analyst, data
scientists and statisticians is intense
– Hospitals cannot match more lucrative offers
elsewhere
• Data scientist with healthcare and / or
strategic focus are rare
• Supplement with diverse team

Personnel Readiness Challenge
$0
$20,000
$40,000
$60,000
$80,000
$100,000
$120,000
$140,000
$160,000
$180,000
$200,000
Senior Mgmt Assoc Staff Dept Heads Management
Healthcare I.T. Salary Comparison
Health IT Salaries Other Industries
Source: Healthcare Information and Management Systems Society

Projected Health I.T. Job Growth
0% 5% 10% 15% 20% 25% 30% 35% 40%
Database Administrators
Network Architects
Research Scientists
Software Developers
Systems Analyst
Info Security Analysts
15%
15%
15%
22%
25%
37%
Projected Growth Rate 2012 to 2022
Projected Growth Rate 2012 to 2022
Source: Bureau of Labor Statistics

Talent Gap Solutions
• Healthcare will not win a bidding war with most other
industries
– Forced some hospitals to put projects on hold
– Smaller hospitals will struggle to compete even more
• New approaches to filling positions must be considered
– Can compete by offering greater freedom in providing
solutions
– Appeal to desire to help people through better healthcare
– Train current personnel on technology

Need Strong Leader to Fight for Team
• Your leader needs to be ready take on all
challenges
• Need to be flexible
– Mission changed over time
• Have to utilize all skills to bring team to
success

Your Challenge
• You need a team of diverse skills
• You need a leader to guide team and provide
organization wide support

• Executive – to insure access to the needed data
and the right people
• Champion – ultimate owner of the analytics
project whose problem or query we seek to
answer. Could be department head or CMO /
CMIO
• Data Scientist – responsible for translating the
business or strategic needs to the modelers
and/or analyst for execution and reviewing
results for accuracy

• Project Manager – needed to manage the day-to-
day needs of the project and to make sure the
analysts have the data and support they need
• Data Analyst –builds or gathers the data into a
format or file the modeler will use
• Modeler – a statistician who will actually build
the models using various modeling tools
• Clinician – needed to help the team make sense
of the results / numbers from a healthcare point
of view

• Accounting / Finance Manager – needed to
review the accuracy of the financial data used
• Operations Manager – needed to verify
impact of analysis on operations

Data Management - CSF
• Need to integrate unfamiliar data types
• Essential for highest data quality
• Data capture processes

Data Management Solutions
• Build a comprehensive data warehouse
• Redesign processes to achieve highest data
capture
– At the point of interaction

Define Metrics - CSF
• Identify predictive variables
– May not always be clinical or health data
• Incorporating the program into the data
collection and transformation process
• Present data that is meaningful and correct
– Coke versus New Coke

Define Metrics Solutions
• Continuous process improvement and analysis
• Understand the connection between data
collection and analytics
• Train everyone of their role in the data
collection process
– Human input error cause of over 70% data errors

Challenges Exercise
• What are / were some of your challenges
• List top 5 challenges
– Share in a group and develop 3 solutions
– Share group’s solutions with workshop
• Take picture and send copy for research
compilation
– Email image to: bryan@healthcarecoe.org
– HAS Challenges in subject line
• Free 30 minute phone consultation
• Just add “Want Consultation” in subject line

…You Have a Journey
• Dropping the Ring in a pit of fire is only the
beginning for you
• There is the need for constant improvement
and refinement
• The more information that is captured the
more effective your analytics program will
become

Analytics Lifecycle

Implementation Roadmap
• Takes all the pieces and parts into one plan
– Transformation Model
– Readiness Assessment
• 3 continuums and leadership
– People, Process and Technology (includes data)
• Many tasks occur concurrently

Healthcare Transformation Roadmap
Leadership
Paper-Based
Healthcare
Organization
Data-Enabled
Healthcare
Organization
Resistance
To Change
Isolated
Acceptance
Growing
Importance Embraced
Throughout
Organization
DATA / TECHNOLOGY
ORGANIZATIONAL
/ PEOPLE
PROCESS /
WORKFLOWS
Real-Time
Decision
SupportEHR
Implementation
Analysis &
Modeling
Minimal
Data
Capture
Network-Wide
And Outside
Data Capture
Data Integration,
Discovery &
Interconnectivity
Integrated
Mgmt & Support
& Non-Clinical
Data Sources

Implementation Roadmap
Effective
Roadmap
ProcessTechnology
OrganizationalLeadership

Leadership
• Develop and articulate a vision to drive organization change
– Focus on the benefits the transformation will offer
• Manage organization staff to get their cooperation and buy-in
– Find advocates and utilize them to their fullest
– Top down mandates for analytics, sponsors and champions
• Have unified analytics-driven focus on the patient’s health
– Identify and address operational threats to patient care
• Develop / improve exceptional critical thinking skills

Organization / People
• Identify sources of resistance to change
• Prepare organization for analytics
– Prepare the staff through constant messaging
• Determine your analytical needs
• Identify sources of “quick wins”

Technology / Data
• Determine data needs for an effective
predictive analytics program
– Have executive “open” silos as needed
• Build data warehouse to efficiently access all
data
– Difficult integration of other data sources
– Standardize data as needed
• Choose analytical and modeling tools

Process
• Redesign processes to maximize efficiency
• Plan for accurate and timely data capture
• Prepare for interconnectivity of data

Implementation Plan Template
Section Description
Priority Area: Enter here the top issues selected as priorities to address at this
time.
Goal: Write a broad statement of what you hope to accomplish
related to this priority area.
Performance
Measures:
Demonstrate in this section how you will know you are making
progress. State specifically what you will measure to determine
whether changes have occurred.
Short Term (1-2 years)
Long Term (3-5 years)
Objectives: Describe the specific measurable end-products of your analysis.
Objectives should be SMART: specific, measurable, achievable,
realistic, and time-framed.

Implementation Plan Template
Section Description
Activity: Outline the steps you will take to achieve each objective. The
activities are the “how” portion of the action plan.
Timeline: State the projected start and end date for each activity.
Resources
Required:
Include all resources needed for this action step.
Lead Person/
Organization:
Identify by name the key person who will initiate the activity,
provide direction for the work, and monitor progress.
Anticipated
Result:
Describe the direct, tangible and measurable results of the
activity.
Progress Notes: Track progress of completion of activities.
Also note any unexpected outcomes, both positive and negative.
Alignment: Show the alignment between your priority area to organizational
priorities.

• Using advanced analytics to create an
organization that provides better care at a lower
cost on a profitable basis
• Achieved in many industries for years
– Just coming to healthcare
• Electronic data to analyze
• Healthcare can use many of the same principles
to not only be more competitive but also more
efficient

• Most analytically sophisticated and successful
firms have four common key characteristics
1. Support a strategic, distinctive capability
2. Enterprise-wide approach and management
3. Senior management commitment
4. Company made strategic bet on analytics-based
competition

Support a Strategic, Distinctive Capability
• What sets the organization apart from its
competitors
• What makes it distinctive in the marketplace
– Netflix – predicting movie preferences
– Amazon – suggesting add-on or new products
– LinkedIn & Facebook – new connections

Distinctive Capability In Healthcare
• Represented by patient-focused care
– Personalized
– Improved quality of care
• Identifying health trends
– Using demographic and geospatial data analysis
• Identifying costliest patients
– Segmentations
– Interventions

Enterprise-Wide Approach and Management
• Analytical activities are not relegated to just
one group within the company or a collection
of disparate employees across the
organization
• Manage analytics as an organization or
enterprise
• Results made available throughout the
organization

Enterprise-Wide Approach In Healthcare
• Better sharing of data internally
– Interconnectivity preparedness
• Better coordination of care
• Protocol-based treatment
– Based on best practices

Senior Management Commitment
• Broad analytical approach requires changes in
culture, process, behavior and skills for
multiple employees
– “Unlock the silos”
– Share information
• Usually led by CEO
– Without push from the top, the cultural changes
necessary may not take place

Senior Management In Healthcare
• Capability to drive change through
organization
– Need broad business experience
• Visionary and Communicator
– WVU President
• Strong relationship management skills

Strategic Bet on Analytics-Based Competition
• Companies bet their future success on
analytics-based strategies
• Measured in cost savings, revenues, profits,
market share and customer loyalty
– If no impact on critical measures then company
not really competing on analytics.

Strategic Bet on Analytics In Healthcare
• Improved operational efficiencies
– Using scorecards and dashboards
• Drive sustainable and profitable growth
• Increased transparency
– Cost data
– Quality information

To Sustain a Competitive Advantage
Analytical Capabilities Must Be
• Hard to duplicate
– Not easily copied by a competitor
• Personalized models
– Wall Street Journal test
• Unique
– Based on organization strategy or position
• Adaptable to many situations
– From providers to patients to management

To Sustain a Competitive Advantage
Analytical Capabilities Must Be
• Better than the competition
– Enabling smarter decisions
– Name Competitors: Amazon, LinkedIn, Facebook
– Top 4 healthcare systems
• Renewable
– Continued improvement and reinvestment
– Journey not a destination

Stages of Analytical Competition Model
Stage 5
Analytical
Competitors
Stage 4
Analytical
Companies
Stage 3
Analytical Aspirations
Stage 2
Localized Analytics
Stage 1
Analytically Impaired

Competing On Healthcare Analytics
• These topics and others
included in upcoming book
• Due to be published in August
2015
• Guide to succeeding in today’s
new healthcare industry
– Using People, Process and
Technology Powered by
Leadership
• Send e-mail to:
HASdiscount@healthcarecoe.org
– Will receive 20% off book when
published

In Conclusion
• For analytics success:
– Lead you team of halfings like Frodo through
perilous journeys to reach your goal and not let
your team down
– Be prepared to shoot down the attacks from your
enemies quickly and precisely like Legolas the elf
– Wield your mighty ax wherever it’s needed
regardless of your size or power like Gimli the
dwarf

In Conclusion
• For analytics success:
– Lead your team with quiet assurance and protect
them at all costs like Aragorn
– Fight the battles for your organization and make
sure they have the resources they need regardless
of the peril to yourself like Gandalf the Wizard

Questions and Answers
• Contact Information:
– Professor J. Bryan Bennett
• Data Scientist and Author
– Healthcare Center of Excellence
• Executive Director
– E-mail: bryan@healthcarecoe.org
– Website / Blogs
• www.healthcarecoe.org
• www.himssfuturecare/blog/1266
– Twitter: @healthcarecoe
– Facebook: facebook.com/healthcarecoe

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