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© 2014 Health Catalystwww.healthcatalyst.comCreative Commons Copyright
© 2014 Health Catalystwww.healthcatalyst.comCreative Commons Copyright
Dales Sanders – May 7, 2014
Demystifying Healthcare Data Governance
© 2014 Health Catalystwww.healthcatalyst.comCreative Commons Copyright 2
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
© 2014 Health Catalystwww.healthcatalyst.comCreative Commons Copyright 3
A Sampling of My Up & Down Journey
TOO LITTLE DATA GOVERNANCE
TOO MUCH DATA GOVERNANCE
WWMCCS: Worldwide Military Command & Control SystemMMICS: Maintenance Management Information Collection SystemNSA: National Security AgencyIMDB: Integrated Minuteman Data BasePIRS: Peacekeeper Information Retrieval SystemEDW: Enterprise Data Warehouse
(1986)WWMCCS
(1987)MMICS
(1992)NSA ThreatReporting
●
● ●● ●
●
●
●
(1995)IMDB
& PIRS
(1996)IntelLogisticsEDW
(1998)Intermountain
Healthcare
(2005)Northwestern
EDW
(2009)Cayman
Islands HSA
1983
2014
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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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Centralized EDW; monolithic early
binding data model
Data Governance Cultures
HIGHLY CENTRALIZED GOVERNMENT
BALANCED GOVERNMENT
HIGHLY DECENTRALIZED GOVERNMENT
AUTHORITARIAN DEMOCRATIC TRIBAL
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
© 2014 Health Catalystwww.healthcatalyst.comCreative Commons Copyright 8
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
© 2014 Health Catalystwww.healthcatalyst.comCreative Commons Copyright 9
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
© 2014 Health Catalystwww.healthcatalyst.comCreative Commons Copyright 12
Keys to Analytic Success
The Data Governance Committee should be a driving force in all three…
– Setting the tone of “data driven” for the culture
– Actively building and recruiting for data literacy among employees
– Choosing the right kind of tools to support analytics and data governance
Mindset
Skillset
Toolset
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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.”
© 2014 Health Catalystwww.healthcatalyst.comCreative Commons Copyright 21
Who Is On The Data Governance Committee?
Representing the analytics customers
The data technologist
The clinical data owners
The financial and supply chain data owner
Representing the researchers’ data needs
Chief Analytics Officer
CIO
CMO & CNO
CFO
CRO
© 2014 Health Catalystwww.healthcatalyst.comCreative Commons Copyright 22
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
© 2014 Health Catalystwww.healthcatalyst.comCreative Commons Copyright 23
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%
© 2014 Health Catalystwww.healthcatalyst.comCreative Commons Copyright
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
24
© 2014 Health Catalystwww.healthcatalyst.comCreative Commons Copyright
Tools for Data GovernanceData 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?
25
Practice
Protocols
Processing
EDWAnalyzable data
Clinicians use diverse protocols & orders in
daily care
Sub-Optimal State
The Four Levels of Closed Loop Analytics in Healthcare
© 2014 Denis Protti, Dale Sanders & Corinne Eggert
CDS:EDW:EHR:MTTI:
Clinical Decision SupportEnterprise Data WarehouseElectronic Health Record Mean Time To Improvement
Clinical Information SystemsDecisions & ActionsSupporting information
Clinical, EHR, EDW & Analytics Teams
Align metrics & data
Update EHR & EDW with new data items if needed & possible
Start here
Monitor baselines & clinical processes
Select a problem
Set outcomes & metrics
Quality Governance
Clinical Variations & Needs
Internal EvidenceClinicians’ suggestions
External EvidenceLiterature, reports, etc.
Quality Governance
Use comparative data to identify best outcomes
Determine standard order sets, protocols & decision support rules
External EvidenceLiterature, reports, etc.
Analyze data quality & process/outcome variationsGenerate the internal evidence
Clinical Analytics
Other Data SourcesClinical, Financial, etc.
MTTILo Hi
EHR & CDSElectronic clinical data
Clinicians use standard protocols & orders
in daily care
Optimal State
Clinical, EHR, EDW & Analytics Teams
Update EHR protocols & EDW metrics
Enterprise Clinical Teams Act on performance
information
Executive & Clinical Leadership
Set expectations for use of evidence & standards
Best EvidenceInformation that clinicians trust
Stan
dard
s
Performance
26
© 2014 Health Catalystwww.healthcatalyst.comCreative Commons Copyright 27
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
© 2014 Health Catalystwww.healthcatalyst.comCreative Commons Copyright 28
Progression in the Model
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
The progressive patterns at each level
© 2014 Health Catalystwww.healthcatalyst.comCreative Commons Copyright
Six Phases of Data Governance
You need to move through these phases in no more than two years
29
3-12 months
1-2 years
2-4 years
– 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”
Level 8
Level 1
Personalized Medicine& Prescriptive Analytics
Enterprise Data Warehouse
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What Data Are We Governing?
30
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Master Data Management
The data that is mastered includes:– Reference data - the dimensions for analysis– Analytical rules – supports consistent data binding
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
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Data Binding & Data Governance
“systolic &diastolicblood pressure”
Pieces ofmeaningless
data
11560
Bindsdata to
Analytics Software
Programming
Vocabulary
“normal”
Rules
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Why Is This Binding Concept Important?
Data Governance needs to look for and facilitate both
33
Knowing when to bind data, and howtightly, 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?
ComprehensiveAgreement
Is the rule or vocabulary stable and rarely change?
PersistentAgreement
© 2014 Health Catalystwww.healthcatalyst.comCreative Commons Copyright 34
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)
© 2014 Health Catalystwww.healthcatalyst.comCreative Commons Copyright 35
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
Start Within Your Scope of InfluenceWe are still learning how to manage outpatient populations
© 2014 Health Catalystwww.healthcatalyst.comCreative Commons Copyright 37
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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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.
OBJECTIVE
MOBILE APPAccess to a mobile app 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
@drsanders
www.linkedin.com/in/dalersanders/