About the presentation. Based on Chapter 3 of my book "Healthcare Analytics for Quality and Performance Improvement", this presentation describes the key components of a strategic analytics framework that can enable your healthcare organization to leverage data from source-systems to achieve its quality, safety, and performance improvement goals. What is an analytics strategy? Analytics is currently a very “trendy” topic. The internet is scattered with many buzzwords, marketing angles, white papers, and opinions on the topic of healthcare analytics. With all this “noise”, it is easy to get distracted from what is actually required, from an analytics perspective, by your organization. An analytics strategy helps cut through the noise and keep focus on what is important for the organization. Regardless of what the latest “buzz” is, your analytics strategy will enable your organization to Invest now for what is required now, and invest later for what is required in the future. An analytics strategy helps ensure that analytics development and capabilities are in alignment with enterprise quality and performance goals and helps avoids the “all dashboard, no improvement” syndrome. Furthermore, a well formed strategy document helps to achieve optimal use of analytics within a healthcare organization and can mean the difference between a “collection of reports” versus a high-value information resource. An analytics strategy can rarely stand on its own. In general, the analytics strategy should use as input an organization’s Quality Improvement (QI) strategy and should be used to inform an organization’s Business Intelligence (BI) or Information Technology (IT) strategy. The analytics strategy is an important input to technical strategies because analytics, after all, can involve a sophisticated use of data and technology. Requirements for analytics may trigger a cascade of enhancements throughout other components of IT and BI (i.e., reporting, data storage, ETL, etc) The document is intended to accompany Chapter 3, “Developing an Analytics Strategy to Drive Change”, so please refer to the chapter for further information about developing an analytics strategy.

1. Developing a Strategic Analytics
Framework that Drives Healthcare
Transformation
Trevor Strome, MSc, PMP
Blog: http://HealthcareAnalytics.info
Twitter: @tstrome
Presentation Content Based on Chapter 3 of:
Healthcare Analytics for Quality and Performance Improvement

2. Healthcare Analytics and the Information Value Chain
Business
Processes
Data
Improvement
Approach
System Insight
Developing an Analytics Strategy Framework that Drives Healthcare Transformation Copyright © 2014 Trevor Strome
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Performance
Objectives
Quality Goals
Analytics
What DID
Happen
What IS
Happening
What Will
Happen
Decisions &
Actions
Outcomes Evaluation
Healthcare analytics is the system of tools, techniques, and people
required to consistently and reliably generate the accurate, validated,
and trustworthy business and clinical insight needed to take appropriate
actions and achieve measurable, desired outcomes.

3. Problem Domain
• Healthcare organizations (HCOs) are facing increasing quality,
financial, and regulatory pressures, and must transform to achieve
sustainability.
• The three fundamental information needs of healthcare
improvement are to identify:
– What quality/performance/safety aspects need to improve?
– What processes must change to result in improvement?
– What change (if any) has occurred?
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4. Going beyond reporting…
• Healthcare organizations require better insight into their
operations and accountability for their performance.
• Healthcare organizations must allow for creative use of
available data and analytic tools to foster decision
making – in real time and near the point of care.
• To keep up with pace of change, analytics development
needs to adopt an agile approach which values
innovation and experimentation.
Developing an Analytics Strategy Framework that Drives Healthcare Transformation Copyright © 2014 Trevor Strome
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5. Analytics - Attaining the right insight at the right time
Past Present Future
What Happened?
(Reports)
What’s Happening
Now?
(Alerts)
What Will Happen?
(Extrapolation)
How and Why Did
It Happen?
(Modeling)
What’s the next
best action?
(Recommendation)
What’s the
best/worst that can
happen?
(Prediction,
Simulation)
Information
Insight
Notes:
Adapted from: Davenport TH, Harris JG, & Morison R. Analytics at Work. Boston:
Harvard Business School Publishing Corporation, 2010.
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6. What is an Analytics Strategy?
• A strategy that ensures analytics development and capabilities are
in alignment with enterprise quality and performance goals
– avoids the “all dashboard, no improvement” syndrome
• Helps to achieve optimal use of analytics
– can mean the difference between a “collection of reports” versus
a high-value information resource
• Analytics Strategy should align with other relevant strategies
including:
– Business Intelligence (BI) strategy
– Information Technology (IT) strategy
– Quality Improvement (QI) strategy
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7. Building and Executing a Successful Framework
• Understand requirements
– Review strategy components with stakeholders
– Identify how analytics are currently used
– Determine what capabilities will be needed (short & long term)
• Identify gaps and mitigate risks
– List known/potential gaps and their mitigation approaches
– Prioritize gap mitigation based on impact, effort, & cost
• Execute plan
– Assign task owners and target implementation deadlines
– Monitor progress and apply mid-course corrections
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8. Strategic Analytics System Framework

9. Business &
Quality
Context
Analytics
System
Stakeholders
& Users
Processes &
Data
Tools &
Techniques
Analytics System Framework
Technology
&
Infrastructure
Team &
Training
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An effective analytics system is more than simply a reporting/BI tool
layered on top of a data source.

10. Adding SWOT to Strategy
• Traditional “SWOT” analysis can be layered onto the components
(and sub-components) of analytics strategy.
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Strengths Weaknesses Opportunities Threats
Business &
Quality Context
Stakeholders &
Users
Data &
Processes
Tools &
Techniques
Team &
Training
Technology &
Infrastructure

11. Business & Quality Context
Business &
Quality
Context
Analytics
Strategy
Stakeholders
& Users
Processes &
Data
Tools &
Techniques
Technology &
Infrastructure
Team &
Training

12. Business Context: Enterprise Goals, Objectives, and Strategy
• Goals:
– Are what the organization is aiming to achieve.
– Define the performance and quality targets of the organization
– Answer “why” the organization is (or should be) engaging in
certain activities
• Strategy
– Outlines how the organization expects to achieve its goals
• Analytics must provide insight into past, current, and anticipated
future progress towards meeting the enterprise goals.
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13. Aligning Strategic and Tactical Quality Objectives
• Analytics is the “glue” which ties strategic objectives and tactical
activities together.
• Objectives of unit- or department-based improvement initiatives
should, where possible, align with the quality objectives of the
organization as a whole.
– Prevents misdirected/wasted activity
– Enables the HCO to monitor progress and evaluate outcomes
Strategic Level Strategic Objectives
Analytics Metrics Indicators Targets
Tactical Level Tactical Objectives
A reminder that the customer (“the
patient”) is the ultimate reason for
the work we’re doing.
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Voice of the customer

14. Quality Strategy / Improvement Approach
• Quality Strategy outlines the steps and approach the organization is
going to be taking to achieve quality goals/objectives.
• Which QI approaches are utilized (i.e., Lean, Six Sigma) will impact
what data is required, how it is analyzed, and how it is
communicated.
• Analytics development teams and quality improvement teams must
work closely together
– to ensure that information requirements of users and the delivery
by via analytics are in sync.
• When executing the analytics strategy, always ask “are we
taking appropriate and necessary steps towards achieving the
organization’s quality and performance goals?”
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15. Stakeholders & Users
Business &
Quality
Context
Analytics
Strategy
Stakeholders
& Users
Processes &
Data
Tools &
Techniques
Technology &
Infrastructure
Team &
Training

16. Stakeholder Analysis
• A stakeholder is a person (or group of persons) that are:
– impacted by, users of, or otherwise have a concern (or interest
in) the development and deployment of analytical solutions
throughout the healthcare organization.
• When developing an analytics strategy, it is important to understand
what each of the likely analytics stakeholders will require, and
develop approaches to ensure they are getting what they need.
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17. HCO Stakeholder Types
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Stakeholder Description
Patient The person whose health an healthcare
experience we’re trying to improve with the use
of analytics
Sponsor The person who supports and provides financial
resources for the development and
implementation of the analytics infrastructure
Influencer A person who may not be directly involved in the
development or use of analytics, but who holders
considerable influence over support of analytics
initiatives.
Customer / User A person in the HCO who accesses analytical
tools, or uses the output of analytical tools, to
support decision making and to drive action.

18. Analytics Use Cases
• A use case is a brief description of how analytics will be used by a
stakeholder. Analytics use cases can help to:
– identify any gaps in analytics capabilities, and
– reduce the likelihood that critical analytics needs will be missed.
• Analytics use cases help identify:
– what data elements are most important and what indicators will
be necessary to calculate, and
– what types of usability and presentation factors (such as
dashboards, alerts, and mobile access) need to be considered.
• TIP: Develop high-level use cases when outlining the analytics
strategy, and drill down in more detail as new analytical applications
are designed and built.
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19. Example Analytics Use Cases
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Customer /
user
Sample use case(s)
Physician Uses personalized
performance report to adjust
care practices.
Unit manager Determine which patients are
likely to exceed length of
stay targets.
QI team
leader
Identify bottlenecks in patient
flow.
Evaluate outcomes of QI
initiatives.
Healthcare
executive
Evaluate and monitor overall
performance of the
organization.

20. Processes & Data
Business &
Quality
Context
Analytics
Strategy
Stakeholders
& Users
Processes &
Data
Tools &
Techniques
Technology &
Infrastructure
Team &
Training

21. Data considerations
• Data is the “raw material” of analytics.
• Modern computerized clinical systems (such as electronic medical
records) contain dozens if not hundreds of individual data elements.
– The potential exists for thousands of possible data items from
which to choose for analytics.
• An analytics strategy must consider:
– how to determine which data is necessary for quality and
performance improvement
– how the data is managed and its quality assured
– how data links back to business processes for necessary
context.
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22. Data Considerations for Analytics Strategy
Data Issue Example
Data Sources • What are the sources of data?
• What data is necessary to address key
business issues?
Data Quality • How good is the quality of available data?
• Is the data “good enough” for analytics?
• What gaps in data exist?
• Does metadata exist?
Data governance • Who is responsible for data management,
governance, and stewardship?
• What policies and procedures exist?
Business Processes • What business processes and procedures align
with important quality issues?
• What data is available for measuring
processes? Are proxy measures available?
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23. Business Processes
• Business processes provide essential context to the data.
• Most quality improvement methodologies monitor progress and
evaluate performance and outcomes using indicators based on
process data.
– This requires a strong alignment between key business
processes and the data that measures those processes.
• As part of the analytics strategy, you should consider:
– if and how current business processes are documented, and
– how data items are mapped to these documented business
processes.
• TIP: stacks of Visio charts becomes unmanageable very quickly!
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24. Big Data and Analytics
• Big data “describes large volumes of high velocity, complex, and
variable data that require advanced techniques and technologies to
enable the capture, storage, distribution, management, and analysis
of the information.”1
• Big Data represents big opportunity
– U.S. health care data alone reached 150 exabytes in 2011.
– Big data for U.S. health care will soon reach zettabyte (1021
gigabytes) scale and even yottabytes (1024gigabytes) not long
after.
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1. Hartzband, D. D. (2011). Using Ultra-Large Data Sets in Health Care. 2011 Sessions (p. 3). e-healthpolicy.org.

25. Where Does Big Data Come From?
• Web and social media data: Clickstream and interaction data from
social media such as Facebook, Twitter, Linkedin, and blogs.
• Machine-to-machine data: Readings from sensors, meters, and
other devices.
• Transaction data: Health care claims and other billing records.
• Biometric data: Fingerprints, genetics, handwriting, blood pressure,
medical images, retinal scans, and similar types of data.
• Human-generated data: Unstructured and semi-structured data
such as electronic medical records (EMRs), physicians’ notes,
email, and paper documents.
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SOURCE: Institute for Health Technology Transformation.

26. Using Appropriate Indicators
• Using appropriate indicators that align between tactical and strategic
levels are necessary.
– Tactical-level sub-indicators should align with strategic indicators
– Some tactical-level-specific indicators might be necessary for
initiatives that are important at a program, department, or unit
level, but don’t directly align with strategic goals.
Indicator
Sub-
Indicator 1
Sub-
Indicator 2
Sub-
Indicator 3
Strategic
Level
Tactical
Level
Tactical
Indicator 1
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27. Example Strategic and Tactical Indicator Alignment
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95% of patients admitted from ED
achieve EDLOS < 8hrs
Time to
physician
assessment
Time to
consult
answered
Time to
consult
decision
Strategic
Level
Tactical
Level
Time to
inpatient bed
assigned
Time to
patient left ED

28. Analytics Tools and Techniques
Business &
Quality
Context
Analytics
Strategy
Stakeholders
& Users
Processes &
Data
Tools &
Techniques
Technology &
Infrastructure
Team &
Training

29. Common Analytical Applications
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Analytical Application Description
Statistical • Used for deeper statistical analysis not
available in “standard” business intelligence or
reporting packages
Visualization • Used for developing interactive, dynamic data
visualizations that aid with analysis
Data Profiling • Helps to understand and improve the quality of
an HCO’s data.
Data Mining • Analysis of large data sets to uncover unknown
or unsuspected relationships.
Text Mining • Analysis of unstructured, text-based data to
extract high-quality information.
Online Analytical
Processing
• Allows analysts to interactively explore data by
drilling-down, rolling up, or “slicing and dicing”
data.

30. Inventory of Existing Analytical Tools
• Analytical tools must meet the requirements of analysts building
analytics solutions/applications, and the end-users who will rely on
the resultant information and insight.
• Conduct an inventory of existing analytics tools to determine if:
– Capability is missing that will be required
– Existing capability exists that may not be widely known
• Identify viable best-of-breed vendor solutions that meet
requirements; custom-build from scratch if necessary or if
participating in research.
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31. Team and Training
Business &
Quality
Context
Analytics
Strategy
Stakeholders
& Users
Processes &
Data
Tools &
Techniques
Technology &
Infrastructure
Team &
Training

32. Team Development Considerations
• PEOPLE are a critical consideration when developing or expanding
an analytics capability within a healthcare organization
• Although having the best tools are nice, having the best (and right)
people is critical to achieving the goals and objectives of the HCO
• An analytics strategy must consider:
– What kinds of people (and the skills they bring) are necessary
– The optimal size and composition of the team
– Roles and degree of specialization
– What gaps in skills exist, and what training is required
– How to attract the best analytical talent
– How to retain the analytic talent within your HCO
– Optimal organizational structure
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33. Organizational Considerations
• Different resource management models exist for analytics teams:
– “centralized” analytics office
– “distributed” analytics resources
– “virtual” center of excellence / competency center (combines
best aspects of centralized and distributed models)
Senior
Management
Decision Support
Services
(Analytics)
Central (“Core”)
Analytics Analysts
Surgery Program
Program Analytics
Resource
Medicine
Program
Program Analytics
Resource
Emergency
Program
Program Analytics
Resource
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Virtual Business Intelligence / Analytics Competency Centre

34. Technology and Infrastructure
Business &
Quality
Context
Analytics
Strategy
Stakeholders
& Users
Processes &
Data
Tools &
Techniques
Technology &
Infrastructure
Team &
Training

35. Healthcare BI and Analytics Technology and Infrastructure
Reporting and analytics are
the “tip of the iceberg”
regarding the business
intelligence technology stack.
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Source:
Evelson, B. It's Time to Reinvent your BI Strategy.
Forrester Research, Inc.

36. • An abstracted BI stack helps maintain focus on key components of
analytics required to address business and clinical goals.
Analytics Stack
Presentation
Focus on the Business
Visualization Dashboards Reports
Alerts Mobile Geospatial
Quality & Performance Management
Processes Indicators Targets
Improvement strategy Evaluation strategy
Analytics
Tools Techniques Team
Stakeholders Requirements
Deployment Management
Data
Quality Management Integration
Infrastructure Storage
Business Context
Objectives Goals Voice of patient
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37. Technology & Infrastructure
• Analytics and reporting are the tip of the iceberg in the business
intelligence stack.
• The current, near-term, and long-term analytics needs of the HCO
must drive how analytics-related technological capabilities are
acquired. The exact complement of tools will depend on the overall
needs of the HCO.
• The analytics strategy is an important input to IT hardware and
infrastructure strategies and planning as hardware and other system
upgrades are considered.
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38. Strategy Execution

39. Strategy Execution Summary
• It is important to implement and adhere to the analytics strategy
• Plan for and schedule activities to address identified gaps
– Establish a selection criteria to determine what projects will get
emphasis in light of needs of the business and analytics strategy
– Prioritize activities and desired capabilities to balance resources
as new (possibly conflicting) work arises
• Monitor progress towards achieving goals of the analytics strategy
• Ensure that the strategy is a living document that serves as a
roadmap for guiding action and doesn’t become “shelfware”
Developing an Analytics Strategy Framework that Drives Healthcare Transformation Copyright © 2014 Trevor Strome 39

40. Gap Analysis
• Identify important gaps between current and future state, what the
corrective action(s) will be, who owns the actions, and what the due
date for corrective actions is.
Category Current State Target State Corrective
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http://www.mindtools.com/pages/article/gap-analysis.htm
Action
Priority Owner Due Date
Business & Quality
Context
Stakeholders &
Users
Data & Processes
Tools &
Techniques
Team & Training
Technology &
Infrastructure

41. Prioritizing Gap Corrective Actions
• Use the Impact / Effort matrix to help quantitatively determine priority
for addressing analytics gaps.
High impact, Low effort
High impact, High effort
Q2 Q3
Q4
“Immediate”
Low impact, Low effort
“Consider”
“Evaluate”
Low impact, High effort
“Avoid”
Developing an Analytics Strategy Framework that Drives Healthcare Transformation Copyright © 2014 Trevor Strome
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Q1
Impact (increasing)
Effort/Resources Required (increasing)

42. For More Information
Author: Trevor Strome
Email: trevor@HealthcareAnalytics.info
Twitter: @tstrome
Blog: http://HealthcareAnalytics.info
Book: Healthcare Analytics for Quality and Performance Improvement
http://HealthcareAnalyticsBook.com
(Published by John Wiley & Sons, Inc, and available on Amazon.com)
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