Transcript Demystifying Healthcare Data Governance

Demystifying Healthcare Data
Governance
Dales Sanders – May 7, 2014
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Today’s Agenda
 General concepts in data governance
 Unique aspects of data governance in
healthcare
 The layers and roles in data governance
 Constant theme: Data governance as it relates
to analytics and data warehousing
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A Sampling of My Up & Down Journey
TOO MUCH DATA
GOVERNANCE
(2005)
Northwestern
EDW
(1995)
IMDB
& PIRS
(1996)
Intel
Logistics
EDW
(1987)
MMICS
2014
1983
(1986)
WWMCCS
(1992)
NSA Threat
Reporting
(1998)
Intermountain
Healthcare
(2009)
Cayman
Islands HSA
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
TOO LITTLE DATA
GOVERNANCE
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The Sanders Philosophy of
Data Governance
The best data governance governs
to the least extent necessary to
achieve the greatest common good.”
Govern no data until its time.”
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Data Governance Cultures
HIGHLY
CENTRALIZED
GOVERNMENT
BALANCED
GOVERNMENT
HIGHLY
DECENTRALIZED
GOVERNMENT
Centralized EDW;
monolithic early
binding data model
Centralized EDW;
distributed late
binding data model
No EDW; multiple,
distributed analytic
systems
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Characteristics of Democracy
 Elements of centralized decision making
●
Elected or appointed, centralized representatives
●
Majority rules
 Elements of decentralized action
●
Direct voting and participation, locally
●
Everyone is expected to participate in developing
shared values, rules, and laws; then abide by them
and act accordingly
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What’s It Look Like?
Not enough data governance
 Completely decentralized, uncoordinated data analysis
resources-- human and technology
 Inconsistent analytic results from different sources,
attempting to answer the same question
 Poor data quality, e.g., duplicate patient records rate is >
10% in the master patient index
 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
requirements… like accountable care
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What’s It Look Like?
Too much data governance
 Unhappy data analysts… and their customers
 Everything takes too long
–
Loading new data
–
Making changes to data models to support new analytic use cases
–
Getting access to data
–
Resolving data quality problems
–
Developing new reports and analyses
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Poll Question
What best describes the current state of affairs for
data governance in your organization?
193 Respondents
Authoritarian – 19.7%
Democratic – 24.3%
Tribal – 56%
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Poll Question
How would you rate data governance effectiveness
in your organization?
179 Respondents
5 – Very effective – 1.6%
4 – 7.2%
3 – 22.3%
2 – 44.1%
1 – Ineffective – 24.8%
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The Triple Aim of Data Governance
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
•
Pushing the data-driven agenda for cost reduction,
quality improvement, and risk reduction
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Keys to Analytic Success
The Data Governance Committee should be a
driving force in all three…
Mindset
– Setting the tone of “data driven” for the culture
Skillset
– Actively building and recruiting for data literacy
among employees
Toolset
– Choosing the right kind of tools to support
analytics and data governance
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The Data Governance Layers
Happy Data
Analyst
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The Different Roles in Each Layer
Executive & Board Leadership
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.”
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The Different Roles in Each Layer
Data Governance Committee
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.”
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The Different Roles in Each Layer
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.”
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The Different Roles in Each Layer
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.”
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The Different Roles in Each Layer
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.”
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The Different Roles in Each Layer
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.”
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The Different Roles in Each Layer
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.”
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Who Is On The Data Governance
Committee?
Chief Analytics Officer
CIO
Representing the
analytics customers
The data technologist
CMO & CNO
The clinical data owners
CFO
The financial and supply
chain data owner
CRO
Representing the
researchers’ data needs
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Data Governance Committee Failure Modes
Wandering: Lacking direction and experience
●
“We know we need data governance, but we don’t know how to go about it.”
Technical Overkill: An overly passionate and inexperienced IT person leads the
data governance committee
●
Can’t see the forest for the trees
●
For example, Executives on the Data Governance Committee (DGC) are asked
to define naming conventions and data types for a database column
Politics: Members of the DGC are passive aggressive, narrowly motivated, data poseurs
●
They pretend to be data driven and selfless, but they aren’t
●
Territorial and defensive about “their” data
●
“That person isn’t smart enough to use my data properly.”
Red Tape: Committee members are not governors of the data, they are bureaucrats
●
Red tape processes for accessing data
●
Confuse data governance with data security
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Poll Question
Your organization’s biggest risks to the success of
the Data Governance Committee
182 Respondents – Multiple Choice
Wandering – 52%
Politics – 61%
Technical Overkill – 20%
Red Tape – 36%
Other – 16%
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Data Governance & Data Security
 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
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Tools for Data Governance
Data quality reports
–
Data Quality = Validity x Completeness
CRM tools for the data warehouse
–
Who’s using what data? When? Why?
“White Space” data management tools
–
For capturing and filling-in computable data that’s missing in the
source systems
Metadata repository
–
–
–
–
–
What’s in the data warehouse?
Are there any data quality problems?
Who’s the data steward?
How much data is available and over what period of time?
What’s the source of the data?
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The Four Levels of Closed Loop Analytics in Healthcare
CDS:
EDW:
EHR:
MTTI:
Clinical Information
Systems
Decisions & Actions
Supporting information
Clinical Decision Support
Enterprise Data Warehouse
Electronic Health Record
Mean Time To Improvement
Executive & Clinical
Leadership
Set expectations for use
of evidence & standards
Enterprise Clinical Teams
Act on performance
information
Clinical, EHR, EDW &
Analytics Teams
Update EHR protocols &
EDW metrics
Start here
Performance
Practice
MTTI
Protocols
Standards
© 2014 Denis Protti, Dale Sanders & Corinne Eggert
Internal Evidence
Clinicians’ suggestions
Optimal State
Clinical Variations
& Needs
Quality
Governance
Monitor baselines &
clinical processes
Select a problem
Set outcomes & metrics
External Evidence
Literature, reports, etc.
Clinicians use standard
protocols & orders
in daily care
Clinical, EHR, EDW &
Analytics Teams
Align metrics & data
Update EHR & EDW
with new data items if
needed & possible
Processing 
EDW
Analyzable data
EHR & CDS
Electronic clinical data
Sub-Optimal State
Clinicians use diverse
protocols & orders in
daily care
Best Evidence
Information that
clinicians trust
Clinical Analytics
Analyze data quality
& process/outcome
variations
Generate the
internal evidence
Quality
Governance
Use comparative data to
identify best outcomes
Determine standard
order sets, protocols &
decision support rules
Other Data Sources
External Evidence
Clinical, Financial, etc.
Literature, reports, etc.
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Healthcare Analytics Adoption Model
© Sanders,
Protti, Burton, 2013
Level 8
Personalized Medicine
& Prescriptive Analytics
Tailoring patient care based on population outcomes and
generic data. Fee-for-quality rewards health maintenance.
Level 7
Clinical Risk Intervention
& Predictive Analytics
Organizational processes for intervention are supported
with predictive risk models. Fee-for-quality includes fixed
per capita payment.
Level 6
Population Health Management
& Suggestive Analytics
Tailoring patient care based on population metrics. Feefor-quality includes bundled per case payment.
Level 5
Waste & Care Variability Reduction
Reducing variability in care processes. Focusing on
internal optimization and waste reduction.
Level 4
Automated External Reporting
Efficient, consistent production of reports & adaptability to
changing requirements.
Level 3
Automated Internal Reporting
Efficient, consistent production of reports & widespread
availability in the organization.
Level 2
Standardized Vocabulary
& Patient Registries
Relating and organizing the core data content.
Level 1
Enterprise Data Warehouse
Collecting and integrating the core data content.
Level 0
Fragmented Point Solutions
Inefficient, inconsistent versions of the truth. Cumbersome
internal and external reporting.
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Progression in the Model
The progressive patterns at each level
Data content expands
–
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”
The complexity of data binding and algorithms increases
–
–
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
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Six Phases of Data Governance
You need to move through
these phases in no more
than two years
–
Level 8
Personalized Medicine
& Prescriptive Analytics
2-4 years
Phase 6: Acquisition of Data
1-2 years
–
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”
Level 1
Enterprise Data Warehouse
3-12 months
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What Data Are We Governing?
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Master Data Management
Comprises the processes, governance, policies,
standards and tools that consistently define and
manage the critical data of an organization to
provide a single point of reference.”
- Wikipedia
The data that is mastered includes:
–
Reference data - the dimensions for analysis
– Analytical rules – supports consistent data binding
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Data Binding & Data Governance
Analytics
Software
Programming
Pieces of
meaningless
data
Vocabulary
Binds
data to
115
60
“systolic &
diastolic
blood pressure”
Rules
“normal”
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Why Is This Binding Concept
Important?
Knowing when to bind data, and how
tightly, to vocabularies and rules is
CRITICAL to analytic success and agility
Comprehensive
Agreement
Persistent
Agreement
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?
Data Governance needs to look for and facilitate both
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Vocabulary: Where Do We Start?
Charge code
CPT code
Date & Time
DRG code
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.
Drug code
Employee ID
Employer ID
Source data
vocabulary Z
(e.g., EMR)
Encounter ID
Gender
ICD diagnosis code
ICD procedure code
Department ID
Facility ID
Lab code
Patient type
Source data
vocabulary Y
(e.g., Claims)
Source data
vocabulary X
(e.g., Rx)
Patient/member ID
Payer/carrier ID
Postal code
Provider ID
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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
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Start Within Your Scope of Influence
We are still learning how to manage outpatient populations
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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
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OBJECTIVE
Obtain unbiased, practical, educational advice on
proven analytics solutions that really work in healthcare.
The future of healthcare requires transformative thinking
by committed leadership willing to forge and adopt new
data-driven processes. If you count yourself among this
group, then HAS ’14 is for you.
MOBILE APP
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that can be used for
audience response and
participation in real time.
Group-wide and individual
analytic insights will be
shared throughout the
summit, resulting in a more
substantive, engaging
experience while
demonstrating the power
of analytics.
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Contact Info and Q&A
[email protected]
@drsanders
www.linkedin.com/in/dalersanders/
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