1. © 2014 Health Catalyst
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© 2014 Health Catalyst
www.healthcatalyst.com
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
7 Essential Practices for Data
Governance in Healthcare
By Dale Sanders

2. © 2014 Health Catalyst
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The Value of Healthcare Data
2
As healthcare has become a more analytically
driven industry, data is now one of the most valuable
assets outliving facilities, devices and people.

3. © 2014 Health Catalyst
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Governance of Healthcare Data
3
Data governance describes the concept of
managing and influencing the collection and
utilization of data in an organization.
Demand for data governance growing due to
increased data demand for ACO and population
health
Tendency to operate in extremes, either too much
or too little governance

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Healthcare Analytic Adoption Model
In the Healthcare Analytic Adoption Model, a robust data governance
function is required in order to achieve the conditions of Level 5 maturity.
4
Level 8
Level 7
Level 6
Level 5
Level 4
Level 3
Level 2
Level 1
Level 0
Personalized Medicine
& Prescriptive Analytics
Clinical Risk Intervention
& Predictive Analytics
Population Health Management
& Suggestive Analytics
Waste & Care Variability Reduction
Automated External Reporting
Automated Internal Reporting
Standardized Vocabulary
& Patient Registries
Enterprise Data Warehouse
Fragmented Point Solutions
Tailoring patient care based on population outcomes and
generic data. Fee-for-quality rewards health maintenance.
Organizational processes for intervention are supported
with predictive risk models. Fee-for-quality includes fixed
per capita payment.
Tailoring patient care based on population metrics. Fee-
for-quality includes bundled per case payment.
Reducing variability in care processes. Focusing on
internal optimization and waste reduction.
Efficient, consistent production of reports & adaptability
to changing requirements.
Efficient, consistent production of reports & widespread
availability in the organization.
Relating and organizing the core data content.
Collecting and integrating the core data content.
Inefficient, inconsistent versions of the truth.
Cumbersome internal and external reporting.

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Balanced, Lean Governance
5
Start with a broad vision
and framework but limited
application.
Expand governance
function only as needed
Govern to the least extent required for the
common good.
Essentials of
DATA
GOVERNANCE
1
Enhancing data quality
Increasing data content in
the EDW
Encouraging more, not
less data access
Resolving analytic
priorities
Campaigning for
data literacy
Establishing standards
for master reference data
Base your committee charter on…
Data governance committee needs data steward SMEs
When in doubt, govern less, not more. Keep it lean.

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Data Quality
6
Essentials of
DATA
GOVERNANCE
2
Data Quality = Completeness x Validity x Timeliness of Data. .
Data quality is probably the
single most important
function of data governance.
Low data quality negatively
impacts decision accuracy or
timeliness

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Data Access
7
Essentials of
DATA
GOVERNANCE
3
Increasing access to data, across
all members of the enterprise –
external stakeholders, community
members and especially patients,
is a critical Committee function.
The Committee bridges internal
stakeholders to streamline decision
making and departmental reconciliation.

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Data Literacy
8
Essentials of
DATA
GOVERNANCE
4
Data literacy can be increased by:
1. Education – good data from bad data
2. Data analysis tools
3. Data driven process improvement
4. Applying statistical techniques to improve
decision making process
5. Deliberate collection and dissemination of
metadata
Data serves no purpose if intended
beneficiaries cannot interpret or use
the data.

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Data Content
9
Essentials of
DATA
GOVERNANCE
5
For example, activity-based-costing data, genetic and familial
data, bedside devices data, and patient reported observations
and outcomes data are all critically important to the evolution
of analytics in the industry.
Building and acquiring the systems to collect this data is the
first step in the analytic journey and can take as long as five
years to complete.
The Data Governance Committee should plot a multi-
year strategy for data provisioning and acquisition

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Analytic Prioritization
10
Essentials of
DATA
GOVERNANCE
6 The Data Governance Committee should play a major
role in developing and implementing the strategic
analytic plan for the C-level suite
Analytic resource allocation
should use 60/40 mix to balance
top-down corporate priorities with
bottom-up requests from clinical
and business units.
Top-down: 60%
Bottom-up: 40%

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Master Data Management
11
Essentials of
DATA
GOVERNANCE
7 Over time, the Data Governance Committee
defines, encourage use, and resolves
conflicts in master data management.
Local data standards (facility codes,
department codes, etc.);
Regional and industry standards (CPT, ICD,
SNOMED, LOINC, etc.).
Defining algorithms such as readmission
criteria, or attributing patients to providers in
accountable care arrangements.

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Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
A Landmark, 12-Point Review of Population Health Management
Companies (Also by Dale Sanders)
Prior to his healthcare experience, Dale Sanders worked for
fourteen years in the military, national-intelligence, and
manufacturing sectors, specializing in analytics and decision
support. In addition to his role at Health Catalyst, he continues to
serve as the senior technology advisor and CIO for the National
Health System in the Cayman Islands. Previously, he was CIO with
the Northwestern University Medical Center, and regional director of Medical
Informatics at Intermountain Healthcare where he served in a number of
capacities, including chief architect of Intermountain’s enterprise data
warehouse. Dale is a founder of the Healthcare Data Warehousing Association.
He holds Bachelor of Science degrees in Chemistry and in Biology from Ft.
Lewis College, Durango Colorado, and is a graduate of the U.S. Air Force
Information Systems Engineering program.