Demystifying Healthcare Data Governance

Demystifying Healthcare Data Governance
— Dale Sanders

Data Governance in Healthcare
Data is the new oil!”
— Andreas Weigend
Former Amazon Scientist
© 2014 Health Catalyst
www.healthcatalyst.com
As the age of analytics
emerges in healthcare,
health system executives
are increasingly challenged
to define a data governance
strategy that maximizes
healthcare data’s value to
the mission of their
organizations
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
2

A Sampling of My Up & Down Journey
TOO MUCH DATA
GOVERNANCE
(1987)
MMICS
TOO LITTLE DATA
GOVERNANCE
WWMCCS: Worldwide Military Command & Control System
MMICS: Maintenance Management Information Collection System
NSA: National Security Agency
IMDB: Integrated Minuteman Data Base
PIRS: Peacekeeper Information Retrieval System
EDW: Enterprise Data Warehouse
(1986)
WWMCCS
(1992)
NSA Threat
Reporting
(1995)
IMDB
& PIRS
(1996)
Intel
Logistics
EDW
(1998)
Intermountain
Healthcare
(2005)
Northwestern
EDW
(2009)
Cayman
Islands HSA
1983
2014
3
Dale
Sanders

The Sanders Philosophy of
Data Governance
4

Data Governance Cultures
HIGHLY
CENTRALIZED
GOVERNMENT
Centralized EDW;
monolithic early
binding data model
BALANCED
GOVERNMENT
HIGHLY
DECENTRALIZED
GOVERNMENT
Centralized EDW;
distributed late
binding data model
No EDW; multiple,
distributed analytic
systems
5

 Elements of centralized decision making
shared values, rules, and laws; then abide by them
and act accordingly
Characteristics of Democracy
● Elected or appointed, centralized representatives
● Majority rules
 Elements of decentralized action
● Direct voting and participation, locally
● Everyone is expected to participate in developing
6

● Inconsistent analytic results from different sources,
● Poor data quality, e.g., duplicate patient records rate
● When data quality problems are surfaced, there is no
formal body nor process for fixing those problems
● Inability to respond to new analytic use cases and
What’s It Look Like?
 Not enough data governance
● Completely decentralized, uncoordinated data
analysis resources-- human and technology
attempting to answer the same question
is > 10% in the master patient index
requirements… like accountable care
7

 Unhappy data analysts… and their customers
What’s It Look Like?
 Too much data governance
● Everything takes too long
– Loading new data
– Changes data models to support new analytic use cases
– Getting access to data
– Resolving data quality problems
– Developing new reports and analyses
8

The Triple Aim of Data Governance
● Pushing the data-driven agenda for cost reduction,
1. Ensuring Data Quality
● Data Quality = Completeness x Validity
2. Building Data Literacy in the organization
● Hiring and training to become a data driven
company
3. Maximizing Data Exploitation for the
organization’s benefit
quality improvement, and risk reduction
9

– Setting the tone of “data driven” for the culture
Keys to Analytic Success
The Data Governance Committee should be a
driving force in all three…
– Actively building and recruiting for data
literacy among employees
– Choosing the right kind of tools to support
analytics and data governance
Mindset
Skillset
Toolset
10

The Data Governance Layers
Happy Data
Analyst
11

We need a longitudinal analytic view across the
ACO of a patient’s treatment and costs, as well
as all similar patients in the population we serve.”
The Different Roles in Each Layer
Executive & Board Leadership
12

We need an enterprise data warehouse
that contains all of the clinical data and
financial data in the ACO, as well as a
master patient identifier.”
We need a data analysis team, as well as
the IT skills to manage a data warehouse.”
The following roles in the organization
should have the following types of access
to the EDW.”
Data Governance Committee
13

Data Stewards
I’m responsible for patient
registration. I can help.”
I’m responsible for clinical
documentation in Epic. I can help.”
I’m responsible for revenue cycle
and cost accounting. I can help.”
14

Data Architects & Programmers
We will extract and organize the data from the
registration, EMR, rev cycle, and cost
accounting and load it into the EDW.”
“Data stewards, can we sit down with you and
talk about the data content in your areas?”
“DBAs and Sys Admins, here are the roles
and access control procedures for this data.”
15

DBAs & System Administrators
Here is the access control list and
procedures for approving access to this
data. Let’s build the data base roles and
audit trails to support these.”
16

Data access & control system
When this person logs in, they have the
following rights to create, read, update,
and delete this data in the EDW.”
17

Data Analysts
I’ll log into the EDW and build a query
against the data in the EDW that should be
able to answer these types of questions.”
“Data Stewards, can I cross check my
results with you to make sure I’m pulling
the data properly?”
“Data architects, I’ll let you know if I have
any trouble with the way the data is
organized or modeled.”
18

The clinical data owners
The financial and supply
chain data owner
Representing the
researchers’ data needs
Who Is On The Data Governance
Committee?
Representing the
analytics customers
The data technologist
Chief Analytics Officer
CIO
CMO & CNO
CFO
CRO
19

Data Governance Committee Failure Modes
Wandering data governance committees do so because
they lack something tangible to govern, and lack the
experience to recognize their wandering. To succeed they
must develop data management and awareness skills.
20

Technical overkill is very common when a well-intended
and overly passionate CIO chairs the data governance
committee. A lack of experience with data management and
systems is a recipe for agendas that tend to drive inflated or
unrealistic design.
21

Politics and political infighting can manifest as passive-aggressive
participation in the data governance process.
Members pretend to be data-driven and selfless during
committee meetings but fall back into territorial or
defensive behaviors when returning to their department.
22

Red tape is common within authoritarian forms of data
governance. It is the inherent nature of bureaucracy.
Committee members behave like bureaucrats of the data,
rather than governors and stewards of the data, trying to
maximize the data’s value to the organization.
23

 Data Governance Committee: Constantly pulling for
broader data access and more data transparency
 Information Security Committee: Constantly pulling
for narrower data access and more data protection
 Ideally, there is overlapping membership that helps
with the balance
Data Governance & Data Security
24

Data Quality = Validity x Completeness
To achieve the Triple Aim of Data
Governance, the governance
committee needs reports that
exposes data quality.
Data stewards use these reports
in their efforts to close the gaps
in data quality for the systems of
their responsibility.
Tools for Data Governance
Data quality reports
25

The data governance committee
will also need reports for
understanding how the data
warehouse is being used.
• Who’s using the data?
• When is the data being used?
• Why acquire the data?
CRM tools for the data warehouse
26

For capturing and filling-in
computable data missing from
source systems.
Sometimes this white space data
is manually abstracted and
manually integrated on desktop
computers using Excel or Access.
These tools replace spreadsheets
and databases by providing an
easy-to-use data entry tool that is
tightly coupled with the EDW.
“White Space” data management tools
27

The metadata repository serves
as the “Yellow Pages” for the
EDW. It is the tool used to browse
the EDW data and attributes.
– What’s in the data warehouse?
– Are there any data quality problems?
– Who’s the data steward?
– How much data is available and over
Metadata Repository
what period of time?
– What’s the source of the data?
28

Healthcare Analytics Adoption Model
29
Modeled after the HIMSS Analytics
EMR Adoption Model, the
Healthcare Analytics Adoption
Model provides a framework for
evaluating an organization’s
adoption of analytics.
It also provides a roadmap for
developing analytics strategies,
both for vendors and for internal
use by healthcare delivery
organizations.

Healthcare Analytics Adoption Model
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.
© Sanders, Protti, Burton, 2013
30

The progressive patterns at each level
– Adding new sources of data to expand our understanding of care
delivery and the patient
Data timeliness increases
– To support faster decision cycles and lower “Mean Time To
Improvement”
Complexity of data binding and algorithms increases
Progression in the Model
Data content expands
– From descriptive to prescriptive analytics
– From “What happened?” to “What should we do?”
Data governance and literacy expands
– Advocating greater data access, utilization, and quality
31

2-4 years
1-2 years
Six Phases of Data Governance
You need to move through
these phases in no more
than two years
Level 8
Level 1
32
3-12 months
– Phase 6: Acquisition of Data
– Phase 5: Utilization of Data
– Phase 4: Quality of Data
– Phase 3: Stewardship of Data
– Phase 2: Access to Data
– Phase 1: Cultural Tone of “Data Driven”
Personalized Medicine
& Prescriptive Analytics
Enterprise Data Warehouse

What Data Are We Governing?
33

Master Data Management
Master data management is comprised of
processes, governance, policies, standards, and
tools that consistently define and manage the
critical data of an organization to provide a single
point of reference.
The data that is mastered includes:
- Wikipedia
– Reference data - the dimensions for analysis
– Analytical rules – supports consistent data binding
34

Data Binding & Data Governance
“systolic &
diastolic
blood pressure”
Pieces of
meaningless
Analytics
Software
Programming
data
115
60
Binds
data to
Vocabulary
Rules
“normal”
35

Why Is This Binding Concept
Important?
Comprehensive
Agreement
Persistent
Agreement
Data Governance needs to look for and facilitate both
36
Knowing when to bind data, and how
tightly, to vocabularies and rules is
CRITICAL to analytic success and agility
Is the rule or vocabulary widely
accepted as true and accurate in
the organization or industry?
Is the rule or vocabulary stable
and rarely change?

Vocabulary: Where Do We Start?
Charge code
CPT code
Date & Time
DRG code
Drug code
Employee ID
Employer ID
Encounter ID
Gender
ICD diagnosis code
ICD procedure code
Department ID
Facility ID
Lab code
Patient type
Patient/member ID
Payer/carrier ID
Postal code
Provider ID
In today’s environment, about 20 data elements
represent 80-90% of analytic use cases. This
will grow over time, but right now, it’s fairly simple.
Source data
vocabulary Z
(e.g., EMR)
Source data
vocabulary Y
(e.g., Claims)
Source data
vocabulary X
(e.g., Rx)
37

Where Do We Start, Clinically?
We see consistent opportunities, across the industry,
in the following areas:
• CAUTI
• CLABSI
• Pregnancy management,
elective induction
• Discharge medications
adherence for MI/CHF
• Prophylactic pre-surgical
antibiotics
• Materials management,
supply chain
• Glucose management in
the ICU
• Knee and hip replacement
• Gastroenterology patient
management
• Spine surgery patient
management
• Heart failure and ischemic
patient management
38

Start Within Your Scope of Influence
We are still learning how to manage outpatient populations
39

In Conclusion
Practice democratic data governance
– Find the balance between central and decentralized
governance
– Federal vs. States’ rights is a good metaphor
The Triple Aim of Data Governance
– Data Quality, Data Literacy, and Data Exploitation
Analytics gives data governance something to govern
– Start within your current scope of influence and data, then
grow from there
40

Link to original article for a more in-depth discussion.
Demystifying Healthcare Data Governance
More about this topic
Becoming the Change Agent Your Healthcare System Needs
Dr. John Haughom, Senior Advisor
3 Phases of Healthcare Data Governance in Analytics
Mike Doyle, Vice President of Sales
Data Governance: 7 Essential Practices
Dale Sanders, Senior Vice President of Strategy
How Accountable Care Organizations Will Drive Demand for Data Analytics
Dr. David Burton, Former CEO and Executive Chairman
Discovering Patterns in the Data to Improve Patient Care
Dr. John Haughom, Senior Advisor

– John Haughom, MD, Senior Advisor, Health Catalyst
For more information:
Download Healthcare: A Better Way.
The New Era of Opportunity
“This is a knowledge source for clinical and
operational leaders, as well as front-line
caregivers, who are involved in improving
processes, reducing harm, designing and
implementing new care delivery models, and
undertaking the difficult task of leading
meaningful change on behalf of the patients
they serve.”

Other Clinical Quality Improvement Resources
Dale Sanders has been one of the most influential leaders in healthcare analytics and
data warehousing since his earliest days in the industry, starting at Intermountain
Healthcare from 1997-2005, where he was the chief architect for the enterprise data
warehouse (EDW) and regional director of medical informatics at LDS Hospital. In
2001, he founded the Healthcare Data Warehousing Association. From 2005-2009, he
was the CIO for Northwestern University’s physicians’ group and the chief architect of
From 2009-2012, he served as the CIO for the national health system of the Cayman Islands where
he helped lead the implementation of new care delivery processes that are now associated with
accountable care in the US. Prior to his healthcare experience, Dale had a diverse 14-year career
that included duties as a CIO on Looking Glass airborne command posts in the US Air Force; IT
support for the Reagan/Gorbachev summits; nuclear threat assessment for the National Security
Agency and START Treaty; chief architect for the Intel Corp’s Integrated Logistics Data Warehouse;
and co-founder of Information Technology International. As a systems engineer at TRW, Dale and
his team developed the largest Oracle data warehouse in the world at that time (1995), using an
innovative design principle now known as a late binding architecture. He holds a BS degree in
chemistry and minor in biology from Ft. Lewis College, Durango Colorado, and is a graduate of the
US Air Force Information Systems Engineering program.
Click to read additional information at www.healthcatalyst.com
the Northwestern Medical EDW.

Demystifying Healthcare Data Governance

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