SlideShare ist ein Scribd-Unternehmen logo
1 von 39
Downloaden Sie, um offline zu lesen
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
© 2014 Health Catalyst
www.healthcatalyst.comCreative Commons Copyright
Dales Sanders – May 7, 2014
Demystifying Healthcare Data
Governance
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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
2
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
A Sampling of My Up & Down Journey
TOO LITTLE DATA
GOVERNANCE
TOO MUCH 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
(1987)
MMICS
(1992)
NSA Threat
Reporting
(1995)
IMDB
& PIRS
(1996)
Intel
Logistics
EDW
(1998)
Intermountain
Healthcare
(2005)
Northwestern
EDW
(2009)
Cayman
Islands HSA
1983
2014
3
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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.”
4
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
Centralized EDW;
monolithic early
binding data model
Data Governance Cultures
HIGHLY
CENTRALIZED
GOVERNMENT
BALANCED
GOVERNMENT
HIGHLY
DECENTRALIZED
GOVERNMENT
Centralized EDW;
distributed late
binding data model
No EDW; multiple,
distributed analytic
systems
5
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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
6
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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
7
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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
8
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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%
9
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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%
10
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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
11
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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
12
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
The Data Governance Layers
Happy Data
Analyst
13
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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.”
14
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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.”
15
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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.”
16
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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.”
17
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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.”
18
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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.”
19
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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.”
20
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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
21
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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
22
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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%
23
© 2014 Health Catalyst
www.healthcatalyst.com
Creative 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 Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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?
25
Practice
Protocols
Processing
EDW
Analyzable 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 Support
Enterprise Data Warehouse
Electronic Health Record
Mean Time To Improvement
Clinical Information
Systems
Decisions & Actions
Supporting 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 Evidence
Clinicians’ suggestions
External Evidence
Literature, reports, etc.
Quality
Governance
Use comparative data to
identify best outcomes
Determine standard
order sets, protocols &
decision support rules
External Evidence
Literature, reports, etc.
Analyze data quality
& process/outcome
variations
Generate the
internal evidence
Clinical Analytics
Other Data Sources
Clinical, Financial, etc.
MTTI
EHR & CDS
Electronic 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 Evidence
Information that
clinicians trust
Standards

Performance
26
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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
27
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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
28
© 2014 Health Catalyst
www.healthcatalyst.com
Creative 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
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
What Data Are We Governing?
30
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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
31
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
Data Binding & Data Governance
“systolic &
diastolic
blood pressure”
Pieces of
meaningless
data
115
60
Binds
data to
Analytics
Software
Programming
Vocabulary
“normal”
Rules
32
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
Why Is This Binding Concept
Important?
Data Governance needs to look for and facilitate both
33
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?
Comprehensive
Agreement
Is the rule or vocabulary stable
and rarely change?
Persistent
Agreement
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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)
34
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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
35
Start Within Your Scope of Influence
We are still learning how to manage outpatient populations
36
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
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
37
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright 38
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 APP
Access 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.
© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
Contact Info and Q&A
dale.sanders@healthcatalyst.com
@drsanders
www.linkedin.com/in/dalersanders/
39

Weitere ähnliche Inhalte

Was ist angesagt?

BI Consultancy - Data, Analytics and Strategy
BI Consultancy - Data, Analytics and StrategyBI Consultancy - Data, Analytics and Strategy
BI Consultancy - Data, Analytics and StrategyShivam Dhawan
 
Implementing Effective Data Governance
Implementing Effective Data GovernanceImplementing Effective Data Governance
Implementing Effective Data GovernanceChristopher Bradley
 
Data Management Strategies
Data Management StrategiesData Management Strategies
Data Management StrategiesMicheal Axelsen
 
Data Governance: Keystone of Information Management Initiatives
Data Governance: Keystone of Information Management InitiativesData Governance: Keystone of Information Management Initiatives
Data Governance: Keystone of Information Management InitiativesAlan McSweeney
 
State of Data Governance in 2021
State of Data Governance in 2021State of Data Governance in 2021
State of Data Governance in 2021DATAVERSITY
 
Enterprise Data Governance for Financial Institutions
Enterprise Data Governance for Financial InstitutionsEnterprise Data Governance for Financial Institutions
Enterprise Data Governance for Financial InstitutionsSheldon McCarthy
 
Analytics & Data Strategy 101 by Deko Dimeski
Analytics & Data Strategy 101 by Deko DimeskiAnalytics & Data Strategy 101 by Deko Dimeski
Analytics & Data Strategy 101 by Deko DimeskiDeko Dimeski
 
Data Architecture Strategies: Data Architecture for Digital Transformation
Data Architecture Strategies: Data Architecture for Digital TransformationData Architecture Strategies: Data Architecture for Digital Transformation
Data Architecture Strategies: Data Architecture for Digital TransformationDATAVERSITY
 
Data Governance — Aligning Technical and Business Approaches
Data Governance — Aligning Technical and Business ApproachesData Governance — Aligning Technical and Business Approaches
Data Governance — Aligning Technical and Business ApproachesDATAVERSITY
 
Data Monetization
Data MonetizationData Monetization
Data MonetizationDATAVERSITY
 
How to Build & Sustain a Data Governance Operating Model
How to Build & Sustain a Data Governance Operating Model How to Build & Sustain a Data Governance Operating Model
How to Build & Sustain a Data Governance Operating Model DATUM LLC
 
MDM & BI Strategy For Large Enterprises
MDM & BI Strategy For Large EnterprisesMDM & BI Strategy For Large Enterprises
MDM & BI Strategy For Large EnterprisesMark Schoeppel
 
The Chief Data Officer Agenda: Metrics for Information and Data Management
The Chief Data Officer Agenda: Metrics for Information and Data ManagementThe Chief Data Officer Agenda: Metrics for Information and Data Management
The Chief Data Officer Agenda: Metrics for Information and Data ManagementDATAVERSITY
 
Data Governance Powerpoint Presentation Slides
Data Governance Powerpoint Presentation SlidesData Governance Powerpoint Presentation Slides
Data Governance Powerpoint Presentation SlidesSlideTeam
 
Introduction to Business Intelligence
Introduction to Business IntelligenceIntroduction to Business Intelligence
Introduction to Business IntelligenceRonan Soares
 
The Business Value of Metadata for Data Governance
The Business Value of Metadata for Data GovernanceThe Business Value of Metadata for Data Governance
The Business Value of Metadata for Data GovernanceRoland Bullivant
 
Data Governance for Clinical Information
Data Governance for Clinical InformationData Governance for Clinical Information
Data Governance for Clinical InformationChristopher Bradley
 
Data Strategy Best Practices
Data Strategy Best PracticesData Strategy Best Practices
Data Strategy Best PracticesDATAVERSITY
 

Was ist angesagt? (20)

BI Consultancy - Data, Analytics and Strategy
BI Consultancy - Data, Analytics and StrategyBI Consultancy - Data, Analytics and Strategy
BI Consultancy - Data, Analytics and Strategy
 
Implementing Effective Data Governance
Implementing Effective Data GovernanceImplementing Effective Data Governance
Implementing Effective Data Governance
 
Data Management Strategies
Data Management StrategiesData Management Strategies
Data Management Strategies
 
Data Governance: Keystone of Information Management Initiatives
Data Governance: Keystone of Information Management InitiativesData Governance: Keystone of Information Management Initiatives
Data Governance: Keystone of Information Management Initiatives
 
State of Data Governance in 2021
State of Data Governance in 2021State of Data Governance in 2021
State of Data Governance in 2021
 
Data Monetization Framework
Data Monetization FrameworkData Monetization Framework
Data Monetization Framework
 
Enterprise Data Governance for Financial Institutions
Enterprise Data Governance for Financial InstitutionsEnterprise Data Governance for Financial Institutions
Enterprise Data Governance for Financial Institutions
 
DMBOK and Data Governance
DMBOK and Data GovernanceDMBOK and Data Governance
DMBOK and Data Governance
 
Analytics & Data Strategy 101 by Deko Dimeski
Analytics & Data Strategy 101 by Deko DimeskiAnalytics & Data Strategy 101 by Deko Dimeski
Analytics & Data Strategy 101 by Deko Dimeski
 
Data Architecture Strategies: Data Architecture for Digital Transformation
Data Architecture Strategies: Data Architecture for Digital TransformationData Architecture Strategies: Data Architecture for Digital Transformation
Data Architecture Strategies: Data Architecture for Digital Transformation
 
Data Governance — Aligning Technical and Business Approaches
Data Governance — Aligning Technical and Business ApproachesData Governance — Aligning Technical and Business Approaches
Data Governance — Aligning Technical and Business Approaches
 
Data Monetization
Data MonetizationData Monetization
Data Monetization
 
How to Build & Sustain a Data Governance Operating Model
How to Build & Sustain a Data Governance Operating Model How to Build & Sustain a Data Governance Operating Model
How to Build & Sustain a Data Governance Operating Model
 
MDM & BI Strategy For Large Enterprises
MDM & BI Strategy For Large EnterprisesMDM & BI Strategy For Large Enterprises
MDM & BI Strategy For Large Enterprises
 
The Chief Data Officer Agenda: Metrics for Information and Data Management
The Chief Data Officer Agenda: Metrics for Information and Data ManagementThe Chief Data Officer Agenda: Metrics for Information and Data Management
The Chief Data Officer Agenda: Metrics for Information and Data Management
 
Data Governance Powerpoint Presentation Slides
Data Governance Powerpoint Presentation SlidesData Governance Powerpoint Presentation Slides
Data Governance Powerpoint Presentation Slides
 
Introduction to Business Intelligence
Introduction to Business IntelligenceIntroduction to Business Intelligence
Introduction to Business Intelligence
 
The Business Value of Metadata for Data Governance
The Business Value of Metadata for Data GovernanceThe Business Value of Metadata for Data Governance
The Business Value of Metadata for Data Governance
 
Data Governance for Clinical Information
Data Governance for Clinical InformationData Governance for Clinical Information
Data Governance for Clinical Information
 
Data Strategy Best Practices
Data Strategy Best PracticesData Strategy Best Practices
Data Strategy Best Practices
 

Andere mochten auch

TekMindz Master Data Management Capabilities
TekMindz Master Data Management CapabilitiesTekMindz Master Data Management Capabilities
TekMindz Master Data Management CapabilitiesAkshay Pandita
 
넥스트 컨퍼런스 2013: Conference on Innovation and The Future
넥스트 컨퍼런스 2013: Conference on Innovation and The Future넥스트 컨퍼런스 2013: Conference on Innovation and The Future
넥스트 컨퍼런스 2013: Conference on Innovation and The FutureBernard Moon
 
AIM-OPP for clearbook
AIM-OPP for clearbookAIM-OPP for clearbook
AIM-OPP for clearbookJinky Quizon
 
кратко
краткократко
краткоkulibin
 
5 Questions That You Should Ask in Any Negotiation
5 Questions That You Should Ask in Any Negotiation5 Questions That You Should Ask in Any Negotiation
5 Questions That You Should Ask in Any NegotiationManisha Dorawala
 
Підсумки роботи ДП «НАЕК «Енергоатом» за 8 місяців 2015 року (оперативні)
Підсумки роботи ДП «НАЕК «Енергоатом» за 8 місяців 2015 року (оперативні)Підсумки роботи ДП «НАЕК «Енергоатом» за 8 місяців 2015 року (оперативні)
Підсумки роботи ДП «НАЕК «Енергоатом» за 8 місяців 2015 року (оперативні)НАЕК «Енергоатом»
 
Consumer Web Platforms & Customer Acquisition
Consumer Web Platforms & Customer AcquisitionConsumer Web Platforms & Customer Acquisition
Consumer Web Platforms & Customer AcquisitionDave McClure
 
Local SEO - How to beat your clueless competitors
Local SEO - How to beat your clueless competitorsLocal SEO - How to beat your clueless competitors
Local SEO - How to beat your clueless competitorsGreg Gifford
 
NTXISSACSC3 - How Threat Modeling Can Improve Your IAM Solution by John Fehan
NTXISSACSC3 - How Threat Modeling Can Improve Your IAM Solution by John Fehan NTXISSACSC3 - How Threat Modeling Can Improve Your IAM Solution by John Fehan
NTXISSACSC3 - How Threat Modeling Can Improve Your IAM Solution by John Fehan North Texas Chapter of the ISSA
 
BALLSY Guide To The SXSW 2016 Talks You Should Vote For NOW
BALLSY Guide To The SXSW 2016 Talks You Should Vote For NOWBALLSY Guide To The SXSW 2016 Talks You Should Vote For NOW
BALLSY Guide To The SXSW 2016 Talks You Should Vote For NOWJon Burkhart
 

Andere mochten auch (20)

TekMindz Master Data Management Capabilities
TekMindz Master Data Management CapabilitiesTekMindz Master Data Management Capabilities
TekMindz Master Data Management Capabilities
 
Dicas presentes de natal 2014
Dicas presentes de natal 2014Dicas presentes de natal 2014
Dicas presentes de natal 2014
 
넥스트 컨퍼런스 2013: Conference on Innovation and The Future
넥스트 컨퍼런스 2013: Conference on Innovation and The Future넥스트 컨퍼런스 2013: Conference on Innovation and The Future
넥스트 컨퍼런스 2013: Conference on Innovation and The Future
 
用言等換言辞書を用いた換言結果の考察
用言等換言辞書を用いた換言結果の考察用言等換言辞書を用いた換言結果の考察
用言等換言辞書を用いた換言結果の考察
 
Automatic Selection of Predicates for Common Sense Knowledge Expression
Automatic Selection of Predicates for Common Sense Knowledge ExpressionAutomatic Selection of Predicates for Common Sense Knowledge Expression
Automatic Selection of Predicates for Common Sense Knowledge Expression
 
AIM-OPP for clearbook
AIM-OPP for clearbookAIM-OPP for clearbook
AIM-OPP for clearbook
 
用言等換言辞書の構築
用言等換言辞書の構築用言等換言辞書の構築
用言等換言辞書の構築
 
対訳コーパスから生成したワードグラフによる部分的機械翻訳
対訳コーパスから生成したワードグラフによる部分的機械翻訳対訳コーパスから生成したワードグラフによる部分的機械翻訳
対訳コーパスから生成したワードグラフによる部分的機械翻訳
 
Selecting Proper Lexical Paraphrase for Children
Selecting Proper Lexical Paraphrase for ChildrenSelecting Proper Lexical Paraphrase for Children
Selecting Proper Lexical Paraphrase for Children
 
кратко
краткократко
кратко
 
Cannes insights mma
Cannes insights mmaCannes insights mma
Cannes insights mma
 
5 Questions That You Should Ask in Any Negotiation
5 Questions That You Should Ask in Any Negotiation5 Questions That You Should Ask in Any Negotiation
5 Questions That You Should Ask in Any Negotiation
 
Підсумки роботи ДП «НАЕК «Енергоатом» за 8 місяців 2015 року (оперативні)
Підсумки роботи ДП «НАЕК «Енергоатом» за 8 місяців 2015 року (оперативні)Підсумки роботи ДП «НАЕК «Енергоатом» за 8 місяців 2015 року (оперативні)
Підсумки роботи ДП «НАЕК «Енергоатом» за 8 місяців 2015 року (оперативні)
 
Ordem trt2
Ordem trt2Ordem trt2
Ordem trt2
 
Consumer Web Platforms & Customer Acquisition
Consumer Web Platforms & Customer AcquisitionConsumer Web Platforms & Customer Acquisition
Consumer Web Platforms & Customer Acquisition
 
Local SEO - How to beat your clueless competitors
Local SEO - How to beat your clueless competitorsLocal SEO - How to beat your clueless competitors
Local SEO - How to beat your clueless competitors
 
Socialmedianew
SocialmedianewSocialmedianew
Socialmedianew
 
NTXISSACSC3 - How Threat Modeling Can Improve Your IAM Solution by John Fehan
NTXISSACSC3 - How Threat Modeling Can Improve Your IAM Solution by John Fehan NTXISSACSC3 - How Threat Modeling Can Improve Your IAM Solution by John Fehan
NTXISSACSC3 - How Threat Modeling Can Improve Your IAM Solution by John Fehan
 
BALLSY Guide To The SXSW 2016 Talks You Should Vote For NOW
BALLSY Guide To The SXSW 2016 Talks You Should Vote For NOWBALLSY Guide To The SXSW 2016 Talks You Should Vote For NOW
BALLSY Guide To The SXSW 2016 Talks You Should Vote For NOW
 
小学生の読解支援に向けた語釈文から語彙的換言を選択する手法
小学生の読解支援に向けた語釈文から語彙的換言を選択する手法小学生の読解支援に向けた語釈文から語彙的換言を選択する手法
小学生の読解支援に向けた語釈文から語彙的換言を選択する手法
 

Ähnlich wie Healthcare Data Governance Demystified

Demystifying Healthcare Data Governance
Demystifying Healthcare Data GovernanceDemystifying Healthcare Data Governance
Demystifying Healthcare Data GovernanceHealth Catalyst
 
7 Essential Practices for Data Governance in Healthcare
7 Essential Practices for Data Governance in Healthcare7 Essential Practices for Data Governance in Healthcare
7 Essential Practices for Data Governance in HealthcareHealth Catalyst
 
How to Choose the Best Healthcare Analytics Software Solution in a Crowded Ma...
How to Choose the Best Healthcare Analytics Software Solution in a Crowded Ma...How to Choose the Best Healthcare Analytics Software Solution in a Crowded Ma...
How to Choose the Best Healthcare Analytics Software Solution in a Crowded Ma...Health Catalyst
 
5 Reasons Why Healthcare Data is Unique and Difficult to Measure
5 Reasons Why Healthcare Data is Unique and Difficult to Measure5 Reasons Why Healthcare Data is Unique and Difficult to Measure
5 Reasons Why Healthcare Data is Unique and Difficult to MeasureHealth Catalyst
 
Data Mining in Healthcare: How Health Systems Can Improve Quality and Reduce...
Data Mining in Healthcare:  How Health Systems Can Improve Quality and Reduce...Data Mining in Healthcare:  How Health Systems Can Improve Quality and Reduce...
Data Mining in Healthcare: How Health Systems Can Improve Quality and Reduce...Health Catalyst
 
Webinar: Bad Data's Effect on Population Health PerformanceArcadia webinar da...
Webinar: Bad Data's Effect on Population Health PerformanceArcadia webinar da...Webinar: Bad Data's Effect on Population Health PerformanceArcadia webinar da...
Webinar: Bad Data's Effect on Population Health PerformanceArcadia webinar da...Modern Healthcare
 
Healthcare Data Management: Three Principles of Using Data to Its Full Potential
Healthcare Data Management: Three Principles of Using Data to Its Full PotentialHealthcare Data Management: Three Principles of Using Data to Its Full Potential
Healthcare Data Management: Three Principles of Using Data to Its Full PotentialHealth Catalyst
 
Disease Surveillance Monitoring and Reacting to Outbreaks (like Ebola) with a...
Disease Surveillance Monitoring and Reacting to Outbreaks (like Ebola) with a...Disease Surveillance Monitoring and Reacting to Outbreaks (like Ebola) with a...
Disease Surveillance Monitoring and Reacting to Outbreaks (like Ebola) with a...Health Catalyst
 
The Top Seven Quick Wins You Get with a Healthcare Data Warehouse
The Top Seven Quick Wins You Get with a Healthcare Data WarehouseThe Top Seven Quick Wins You Get with a Healthcare Data Warehouse
The Top Seven Quick Wins You Get with a Healthcare Data WarehouseHealth Catalyst
 
Is Big Data a Big Deal...or Not?
Is Big Data a Big Deal...or Not?Is Big Data a Big Deal...or Not?
Is Big Data a Big Deal...or Not?Health Catalyst
 
Data Is the New Strategic Asset in M&As: Is Ripping and Replacing EHRs Really...
Data Is the New Strategic Asset in M&As: Is Ripping and Replacing EHRs Really...Data Is the New Strategic Asset in M&As: Is Ripping and Replacing EHRs Really...
Data Is the New Strategic Asset in M&As: Is Ripping and Replacing EHRs Really...Health Catalyst
 
The Data Operating System: Changing the Digital Trajectory of Healthcare
The Data Operating System: Changing the Digital Trajectory of HealthcareThe Data Operating System: Changing the Digital Trajectory of Healthcare
The Data Operating System: Changing the Digital Trajectory of HealthcareDale Sanders
 
COVID Data Challenges - Updated 2021
COVID Data Challenges - Updated 2021COVID Data Challenges - Updated 2021
COVID Data Challenges - Updated 2021303Computing
 
The Data Operating System: Changing the Digital Trajectory of Healthcare
The Data Operating System: Changing the Digital Trajectory of HealthcareThe Data Operating System: Changing the Digital Trajectory of Healthcare
The Data Operating System: Changing the Digital Trajectory of HealthcareHealth Catalyst
 
Introducing catalyst.ai and MACRA Measures & Insights
Introducing catalyst.ai and MACRA Measures & InsightsIntroducing catalyst.ai and MACRA Measures & Insights
Introducing catalyst.ai and MACRA Measures & InsightsHealth Catalyst
 
The Case for Healthcare Data Literacy: It's Not About Big Data
The Case for Healthcare Data Literacy: It's Not About Big DataThe Case for Healthcare Data Literacy: It's Not About Big Data
The Case for Healthcare Data Literacy: It's Not About Big DataHealth Catalyst
 
Clinical Decision Support: Driving the Last Mile
Clinical Decision Support: Driving the Last MileClinical Decision Support: Driving the Last Mile
Clinical Decision Support: Driving the Last MileHealth Catalyst
 
3 Phases of Healthcare Data Governance in Analytics
3 Phases of Healthcare Data Governance in Analytics3 Phases of Healthcare Data Governance in Analytics
3 Phases of Healthcare Data Governance in AnalyticsHealth Catalyst
 
Artificial Intelligence and Machine Learning in Healthcare: Four Real-World I...
Artificial Intelligence and Machine Learning in Healthcare: Four Real-World I...Artificial Intelligence and Machine Learning in Healthcare: Four Real-World I...
Artificial Intelligence and Machine Learning in Healthcare: Four Real-World I...Health Catalyst
 
Accelerate Data-Driven Healthcare Improvement: 5 Tenets
Accelerate Data-Driven Healthcare Improvement: 5 TenetsAccelerate Data-Driven Healthcare Improvement: 5 Tenets
Accelerate Data-Driven Healthcare Improvement: 5 TenetsHealth Catalyst
 

Ähnlich wie Healthcare Data Governance Demystified (20)

Demystifying Healthcare Data Governance
Demystifying Healthcare Data GovernanceDemystifying Healthcare Data Governance
Demystifying Healthcare Data Governance
 
7 Essential Practices for Data Governance in Healthcare
7 Essential Practices for Data Governance in Healthcare7 Essential Practices for Data Governance in Healthcare
7 Essential Practices for Data Governance in Healthcare
 
How to Choose the Best Healthcare Analytics Software Solution in a Crowded Ma...
How to Choose the Best Healthcare Analytics Software Solution in a Crowded Ma...How to Choose the Best Healthcare Analytics Software Solution in a Crowded Ma...
How to Choose the Best Healthcare Analytics Software Solution in a Crowded Ma...
 
5 Reasons Why Healthcare Data is Unique and Difficult to Measure
5 Reasons Why Healthcare Data is Unique and Difficult to Measure5 Reasons Why Healthcare Data is Unique and Difficult to Measure
5 Reasons Why Healthcare Data is Unique and Difficult to Measure
 
Data Mining in Healthcare: How Health Systems Can Improve Quality and Reduce...
Data Mining in Healthcare:  How Health Systems Can Improve Quality and Reduce...Data Mining in Healthcare:  How Health Systems Can Improve Quality and Reduce...
Data Mining in Healthcare: How Health Systems Can Improve Quality and Reduce...
 
Webinar: Bad Data's Effect on Population Health PerformanceArcadia webinar da...
Webinar: Bad Data's Effect on Population Health PerformanceArcadia webinar da...Webinar: Bad Data's Effect on Population Health PerformanceArcadia webinar da...
Webinar: Bad Data's Effect on Population Health PerformanceArcadia webinar da...
 
Healthcare Data Management: Three Principles of Using Data to Its Full Potential
Healthcare Data Management: Three Principles of Using Data to Its Full PotentialHealthcare Data Management: Three Principles of Using Data to Its Full Potential
Healthcare Data Management: Three Principles of Using Data to Its Full Potential
 
Disease Surveillance Monitoring and Reacting to Outbreaks (like Ebola) with a...
Disease Surveillance Monitoring and Reacting to Outbreaks (like Ebola) with a...Disease Surveillance Monitoring and Reacting to Outbreaks (like Ebola) with a...
Disease Surveillance Monitoring and Reacting to Outbreaks (like Ebola) with a...
 
The Top Seven Quick Wins You Get with a Healthcare Data Warehouse
The Top Seven Quick Wins You Get with a Healthcare Data WarehouseThe Top Seven Quick Wins You Get with a Healthcare Data Warehouse
The Top Seven Quick Wins You Get with a Healthcare Data Warehouse
 
Is Big Data a Big Deal...or Not?
Is Big Data a Big Deal...or Not?Is Big Data a Big Deal...or Not?
Is Big Data a Big Deal...or Not?
 
Data Is the New Strategic Asset in M&As: Is Ripping and Replacing EHRs Really...
Data Is the New Strategic Asset in M&As: Is Ripping and Replacing EHRs Really...Data Is the New Strategic Asset in M&As: Is Ripping and Replacing EHRs Really...
Data Is the New Strategic Asset in M&As: Is Ripping and Replacing EHRs Really...
 
The Data Operating System: Changing the Digital Trajectory of Healthcare
The Data Operating System: Changing the Digital Trajectory of HealthcareThe Data Operating System: Changing the Digital Trajectory of Healthcare
The Data Operating System: Changing the Digital Trajectory of Healthcare
 
COVID Data Challenges - Updated 2021
COVID Data Challenges - Updated 2021COVID Data Challenges - Updated 2021
COVID Data Challenges - Updated 2021
 
The Data Operating System: Changing the Digital Trajectory of Healthcare
The Data Operating System: Changing the Digital Trajectory of HealthcareThe Data Operating System: Changing the Digital Trajectory of Healthcare
The Data Operating System: Changing the Digital Trajectory of Healthcare
 
Introducing catalyst.ai and MACRA Measures & Insights
Introducing catalyst.ai and MACRA Measures & InsightsIntroducing catalyst.ai and MACRA Measures & Insights
Introducing catalyst.ai and MACRA Measures & Insights
 
The Case for Healthcare Data Literacy: It's Not About Big Data
The Case for Healthcare Data Literacy: It's Not About Big DataThe Case for Healthcare Data Literacy: It's Not About Big Data
The Case for Healthcare Data Literacy: It's Not About Big Data
 
Clinical Decision Support: Driving the Last Mile
Clinical Decision Support: Driving the Last MileClinical Decision Support: Driving the Last Mile
Clinical Decision Support: Driving the Last Mile
 
3 Phases of Healthcare Data Governance in Analytics
3 Phases of Healthcare Data Governance in Analytics3 Phases of Healthcare Data Governance in Analytics
3 Phases of Healthcare Data Governance in Analytics
 
Artificial Intelligence and Machine Learning in Healthcare: Four Real-World I...
Artificial Intelligence and Machine Learning in Healthcare: Four Real-World I...Artificial Intelligence and Machine Learning in Healthcare: Four Real-World I...
Artificial Intelligence and Machine Learning in Healthcare: Four Real-World I...
 
Accelerate Data-Driven Healthcare Improvement: 5 Tenets
Accelerate Data-Driven Healthcare Improvement: 5 TenetsAccelerate Data-Driven Healthcare Improvement: 5 Tenets
Accelerate Data-Driven Healthcare Improvement: 5 Tenets
 

Mehr von Health Catalyst

2024 HCAT Healthcare Technology Insights
2024 HCAT Healthcare Technology Insights2024 HCAT Healthcare Technology Insights
2024 HCAT Healthcare Technology InsightsHealth Catalyst
 
Three Keys to a Successful Margin: Charges, Costs, and Labor
Three Keys to a Successful Margin: Charges, Costs, and LaborThree Keys to a Successful Margin: Charges, Costs, and Labor
Three Keys to a Successful Margin: Charges, Costs, and LaborHealth Catalyst
 
2024 CPT® Updates (Professional Services Focused) - Part 3
2024 CPT® Updates (Professional Services Focused) - Part 32024 CPT® Updates (Professional Services Focused) - Part 3
2024 CPT® Updates (Professional Services Focused) - Part 3Health Catalyst
 
2024 CPT® Code Updates (HIM Focused) - Part 2
2024 CPT® Code Updates (HIM Focused) - Part 22024 CPT® Code Updates (HIM Focused) - Part 2
2024 CPT® Code Updates (HIM Focused) - Part 2Health Catalyst
 
2024 CPT® Code Updates (CDM Focused) - Part 1
2024 CPT® Code Updates (CDM Focused) - Part 12024 CPT® Code Updates (CDM Focused) - Part 1
2024 CPT® Code Updates (CDM Focused) - Part 1Health Catalyst
 
What’s Next for Hospital Price Transparency in 2024 and Beyond
What’s Next for Hospital Price Transparency in 2024 and BeyondWhat’s Next for Hospital Price Transparency in 2024 and Beyond
What’s Next for Hospital Price Transparency in 2024 and BeyondHealth Catalyst
 
Automated Patient Reported Outcomes (PROs) for Hip & Knee Replacement
Automated Patient Reported Outcomes (PROs) for Hip & Knee ReplacementAutomated Patient Reported Outcomes (PROs) for Hip & Knee Replacement
Automated Patient Reported Outcomes (PROs) for Hip & Knee ReplacementHealth Catalyst
 
2024 Medicare Physician Fee Schedule (MPFS) Final Rule Updates
2024 Medicare Physician Fee Schedule (MPFS) Final Rule Updates2024 Medicare Physician Fee Schedule (MPFS) Final Rule Updates
2024 Medicare Physician Fee Schedule (MPFS) Final Rule UpdatesHealth Catalyst
 
What's Next for OPPS: A Look at the 2024 Final Rule
What's Next for OPPS: A Look at the 2024 Final RuleWhat's Next for OPPS: A Look at the 2024 Final Rule
What's Next for OPPS: A Look at the 2024 Final RuleHealth Catalyst
 
Insight into the 2024 ICD-10 PCS Updates - Part 2
Insight into the 2024 ICD-10 PCS Updates - Part 2Insight into the 2024 ICD-10 PCS Updates - Part 2
Insight into the 2024 ICD-10 PCS Updates - Part 2Health Catalyst
 
Vitalware Insight Into the 2024 ICD10 CM Updates.pdf
Vitalware Insight Into the 2024 ICD10 CM Updates.pdfVitalware Insight Into the 2024 ICD10 CM Updates.pdf
Vitalware Insight Into the 2024 ICD10 CM Updates.pdfHealth Catalyst
 
Driving Value: Boosting Clinical Registry Value Using ARMUS Solutions
Driving Value: Boosting Clinical Registry Value Using ARMUS SolutionsDriving Value: Boosting Clinical Registry Value Using ARMUS Solutions
Driving Value: Boosting Clinical Registry Value Using ARMUS SolutionsHealth Catalyst
 
Tech-Enabled Managed Services: Not Your Average Outsourcing
Tech-Enabled Managed Services: Not Your Average OutsourcingTech-Enabled Managed Services: Not Your Average Outsourcing
Tech-Enabled Managed Services: Not Your Average OutsourcingHealth Catalyst
 
2023 Mid-Year CPT/HCPCS Code Set Updates
2023 Mid-Year CPT/HCPCS Code Set Updates2023 Mid-Year CPT/HCPCS Code Set Updates
2023 Mid-Year CPT/HCPCS Code Set UpdatesHealth Catalyst
 
How Managing Chronic Conditions Is Streamlined with Digital Technology
How Managing Chronic Conditions Is Streamlined with Digital TechnologyHow Managing Chronic Conditions Is Streamlined with Digital Technology
How Managing Chronic Conditions Is Streamlined with Digital TechnologyHealth Catalyst
 
COVID-19: After the Public Health Emergency Ends
COVID-19: After the Public Health Emergency EndsCOVID-19: After the Public Health Emergency Ends
COVID-19: After the Public Health Emergency EndsHealth Catalyst
 
Automated Medication Compliance Tools for the Provider and Patient
Automated Medication Compliance Tools for the Provider and PatientAutomated Medication Compliance Tools for the Provider and Patient
Automated Medication Compliance Tools for the Provider and PatientHealth Catalyst
 
A Facility-Focused Guide to Applying Modifiers Corectly.pptx
A Facility-Focused Guide to Applying Modifiers Corectly.pptxA Facility-Focused Guide to Applying Modifiers Corectly.pptx
A Facility-Focused Guide to Applying Modifiers Corectly.pptxHealth Catalyst
 
Self-Service Analytics: How to Use Healthcare Business Intelligence
Self-Service Analytics: How to Use Healthcare Business IntelligenceSelf-Service Analytics: How to Use Healthcare Business Intelligence
Self-Service Analytics: How to Use Healthcare Business IntelligenceHealth Catalyst
 
Optimize Your Labor Management with Health Catalyst PowerLabor™
Optimize Your Labor Management with Health Catalyst PowerLabor™Optimize Your Labor Management with Health Catalyst PowerLabor™
Optimize Your Labor Management with Health Catalyst PowerLabor™Health Catalyst
 

Mehr von Health Catalyst (20)

2024 HCAT Healthcare Technology Insights
2024 HCAT Healthcare Technology Insights2024 HCAT Healthcare Technology Insights
2024 HCAT Healthcare Technology Insights
 
Three Keys to a Successful Margin: Charges, Costs, and Labor
Three Keys to a Successful Margin: Charges, Costs, and LaborThree Keys to a Successful Margin: Charges, Costs, and Labor
Three Keys to a Successful Margin: Charges, Costs, and Labor
 
2024 CPT® Updates (Professional Services Focused) - Part 3
2024 CPT® Updates (Professional Services Focused) - Part 32024 CPT® Updates (Professional Services Focused) - Part 3
2024 CPT® Updates (Professional Services Focused) - Part 3
 
2024 CPT® Code Updates (HIM Focused) - Part 2
2024 CPT® Code Updates (HIM Focused) - Part 22024 CPT® Code Updates (HIM Focused) - Part 2
2024 CPT® Code Updates (HIM Focused) - Part 2
 
2024 CPT® Code Updates (CDM Focused) - Part 1
2024 CPT® Code Updates (CDM Focused) - Part 12024 CPT® Code Updates (CDM Focused) - Part 1
2024 CPT® Code Updates (CDM Focused) - Part 1
 
What’s Next for Hospital Price Transparency in 2024 and Beyond
What’s Next for Hospital Price Transparency in 2024 and BeyondWhat’s Next for Hospital Price Transparency in 2024 and Beyond
What’s Next for Hospital Price Transparency in 2024 and Beyond
 
Automated Patient Reported Outcomes (PROs) for Hip & Knee Replacement
Automated Patient Reported Outcomes (PROs) for Hip & Knee ReplacementAutomated Patient Reported Outcomes (PROs) for Hip & Knee Replacement
Automated Patient Reported Outcomes (PROs) for Hip & Knee Replacement
 
2024 Medicare Physician Fee Schedule (MPFS) Final Rule Updates
2024 Medicare Physician Fee Schedule (MPFS) Final Rule Updates2024 Medicare Physician Fee Schedule (MPFS) Final Rule Updates
2024 Medicare Physician Fee Schedule (MPFS) Final Rule Updates
 
What's Next for OPPS: A Look at the 2024 Final Rule
What's Next for OPPS: A Look at the 2024 Final RuleWhat's Next for OPPS: A Look at the 2024 Final Rule
What's Next for OPPS: A Look at the 2024 Final Rule
 
Insight into the 2024 ICD-10 PCS Updates - Part 2
Insight into the 2024 ICD-10 PCS Updates - Part 2Insight into the 2024 ICD-10 PCS Updates - Part 2
Insight into the 2024 ICD-10 PCS Updates - Part 2
 
Vitalware Insight Into the 2024 ICD10 CM Updates.pdf
Vitalware Insight Into the 2024 ICD10 CM Updates.pdfVitalware Insight Into the 2024 ICD10 CM Updates.pdf
Vitalware Insight Into the 2024 ICD10 CM Updates.pdf
 
Driving Value: Boosting Clinical Registry Value Using ARMUS Solutions
Driving Value: Boosting Clinical Registry Value Using ARMUS SolutionsDriving Value: Boosting Clinical Registry Value Using ARMUS Solutions
Driving Value: Boosting Clinical Registry Value Using ARMUS Solutions
 
Tech-Enabled Managed Services: Not Your Average Outsourcing
Tech-Enabled Managed Services: Not Your Average OutsourcingTech-Enabled Managed Services: Not Your Average Outsourcing
Tech-Enabled Managed Services: Not Your Average Outsourcing
 
2023 Mid-Year CPT/HCPCS Code Set Updates
2023 Mid-Year CPT/HCPCS Code Set Updates2023 Mid-Year CPT/HCPCS Code Set Updates
2023 Mid-Year CPT/HCPCS Code Set Updates
 
How Managing Chronic Conditions Is Streamlined with Digital Technology
How Managing Chronic Conditions Is Streamlined with Digital TechnologyHow Managing Chronic Conditions Is Streamlined with Digital Technology
How Managing Chronic Conditions Is Streamlined with Digital Technology
 
COVID-19: After the Public Health Emergency Ends
COVID-19: After the Public Health Emergency EndsCOVID-19: After the Public Health Emergency Ends
COVID-19: After the Public Health Emergency Ends
 
Automated Medication Compliance Tools for the Provider and Patient
Automated Medication Compliance Tools for the Provider and PatientAutomated Medication Compliance Tools for the Provider and Patient
Automated Medication Compliance Tools for the Provider and Patient
 
A Facility-Focused Guide to Applying Modifiers Corectly.pptx
A Facility-Focused Guide to Applying Modifiers Corectly.pptxA Facility-Focused Guide to Applying Modifiers Corectly.pptx
A Facility-Focused Guide to Applying Modifiers Corectly.pptx
 
Self-Service Analytics: How to Use Healthcare Business Intelligence
Self-Service Analytics: How to Use Healthcare Business IntelligenceSelf-Service Analytics: How to Use Healthcare Business Intelligence
Self-Service Analytics: How to Use Healthcare Business Intelligence
 
Optimize Your Labor Management with Health Catalyst PowerLabor™
Optimize Your Labor Management with Health Catalyst PowerLabor™Optimize Your Labor Management with Health Catalyst PowerLabor™
Optimize Your Labor Management with Health Catalyst PowerLabor™
 

Kürzlich hochgeladen

Study on the Impact of FOCUS-PDCA Management Model on the Disinfection Qualit...
Study on the Impact of FOCUS-PDCA Management Model on the Disinfection Qualit...Study on the Impact of FOCUS-PDCA Management Model on the Disinfection Qualit...
Study on the Impact of FOCUS-PDCA Management Model on the Disinfection Qualit...MehranMouzam
 
PERFECT BUT PAINFUL TKR -ROLE OF SYNOVECTOMY.pptx
PERFECT BUT PAINFUL TKR -ROLE OF SYNOVECTOMY.pptxPERFECT BUT PAINFUL TKR -ROLE OF SYNOVECTOMY.pptx
PERFECT BUT PAINFUL TKR -ROLE OF SYNOVECTOMY.pptxdrashraf369
 
Radiation Dosimetry Parameters and Isodose Curves.pptx
Radiation Dosimetry Parameters and Isodose Curves.pptxRadiation Dosimetry Parameters and Isodose Curves.pptx
Radiation Dosimetry Parameters and Isodose Curves.pptxDr. Dheeraj Kumar
 
SYNDESMOTIC INJURY- ANATOMICAL REPAIR.pptx
SYNDESMOTIC INJURY- ANATOMICAL REPAIR.pptxSYNDESMOTIC INJURY- ANATOMICAL REPAIR.pptx
SYNDESMOTIC INJURY- ANATOMICAL REPAIR.pptxdrashraf369
 
COVID-19 (NOVEL CORONA VIRUS DISEASE PANDEMIC ).pptx
COVID-19  (NOVEL CORONA  VIRUS DISEASE PANDEMIC ).pptxCOVID-19  (NOVEL CORONA  VIRUS DISEASE PANDEMIC ).pptx
COVID-19 (NOVEL CORONA VIRUS DISEASE PANDEMIC ).pptxBibekananda shah
 
The next social challenge to public health: the information environment.pptx
The next social challenge to public health:  the information environment.pptxThe next social challenge to public health:  the information environment.pptx
The next social challenge to public health: the information environment.pptxTina Purnat
 
ANTI-DIABETICS DRUGS - PTEROCARPUS AND GYMNEMA
ANTI-DIABETICS DRUGS - PTEROCARPUS AND GYMNEMAANTI-DIABETICS DRUGS - PTEROCARPUS AND GYMNEMA
ANTI-DIABETICS DRUGS - PTEROCARPUS AND GYMNEMADivya Kanojiya
 
PNEUMOTHORAX AND ITS MANAGEMENTS.pdf
PNEUMOTHORAX   AND  ITS  MANAGEMENTS.pdfPNEUMOTHORAX   AND  ITS  MANAGEMENTS.pdf
PNEUMOTHORAX AND ITS MANAGEMENTS.pdfDolisha Warbi
 
Basic principles involved in the traditional systems of medicine PDF.pdf
Basic principles involved in the traditional systems of medicine PDF.pdfBasic principles involved in the traditional systems of medicine PDF.pdf
Basic principles involved in the traditional systems of medicine PDF.pdfDivya Kanojiya
 
Measurement of Radiation and Dosimetric Procedure.pptx
Measurement of Radiation and Dosimetric Procedure.pptxMeasurement of Radiation and Dosimetric Procedure.pptx
Measurement of Radiation and Dosimetric Procedure.pptxDr. Dheeraj Kumar
 
Presentació "Real-Life VR Integration for Mild Cognitive Impairment Rehabilit...
Presentació "Real-Life VR Integration for Mild Cognitive Impairment Rehabilit...Presentació "Real-Life VR Integration for Mild Cognitive Impairment Rehabilit...
Presentació "Real-Life VR Integration for Mild Cognitive Impairment Rehabilit...Badalona Serveis Assistencials
 
Primary headache and facial pain. (2024)
Primary headache and facial pain. (2024)Primary headache and facial pain. (2024)
Primary headache and facial pain. (2024)Mohamed Rizk Khodair
 
April 2024 ONCOLOGY CARTOON by DR KANHU CHARAN PATRO
April 2024 ONCOLOGY CARTOON by  DR KANHU CHARAN PATROApril 2024 ONCOLOGY CARTOON by  DR KANHU CHARAN PATRO
April 2024 ONCOLOGY CARTOON by DR KANHU CHARAN PATROKanhu Charan
 
Music Therapy's Impact in Palliative Care| IAPCON2024| Dr. Tara Rajendran
Music Therapy's Impact in Palliative Care| IAPCON2024| Dr. Tara RajendranMusic Therapy's Impact in Palliative Care| IAPCON2024| Dr. Tara Rajendran
Music Therapy's Impact in Palliative Care| IAPCON2024| Dr. Tara RajendranTara Rajendran
 
Wessex Health Partners Wessex Integrated Care, Population Health, Research & ...
Wessex Health Partners Wessex Integrated Care, Population Health, Research & ...Wessex Health Partners Wessex Integrated Care, Population Health, Research & ...
Wessex Health Partners Wessex Integrated Care, Population Health, Research & ...Wessex Health Partners
 
LESSON PLAN ON fever.pdf child health nursing
LESSON PLAN ON fever.pdf child health nursingLESSON PLAN ON fever.pdf child health nursing
LESSON PLAN ON fever.pdf child health nursingSakthi Kathiravan
 
Presentation for Bella Mahl 2024-03-28-24-MW-Overview-Bella.pptx
Presentation for Bella Mahl 2024-03-28-24-MW-Overview-Bella.pptxPresentation for Bella Mahl 2024-03-28-24-MW-Overview-Bella.pptx
Presentation for Bella Mahl 2024-03-28-24-MW-Overview-Bella.pptxpdamico1
 
medico legal aspects of wound - forensic medicine
medico legal aspects of wound - forensic medicinemedico legal aspects of wound - forensic medicine
medico legal aspects of wound - forensic medicinethanaram patel
 
Apiculture Chapter 1. Introduction 2.ppt
Apiculture Chapter 1. Introduction 2.pptApiculture Chapter 1. Introduction 2.ppt
Apiculture Chapter 1. Introduction 2.pptkedirjemalharun
 
Statistical modeling in pharmaceutical research and development.
Statistical modeling in pharmaceutical research and development.Statistical modeling in pharmaceutical research and development.
Statistical modeling in pharmaceutical research and development.ANJALI
 

Kürzlich hochgeladen (20)

Study on the Impact of FOCUS-PDCA Management Model on the Disinfection Qualit...
Study on the Impact of FOCUS-PDCA Management Model on the Disinfection Qualit...Study on the Impact of FOCUS-PDCA Management Model on the Disinfection Qualit...
Study on the Impact of FOCUS-PDCA Management Model on the Disinfection Qualit...
 
PERFECT BUT PAINFUL TKR -ROLE OF SYNOVECTOMY.pptx
PERFECT BUT PAINFUL TKR -ROLE OF SYNOVECTOMY.pptxPERFECT BUT PAINFUL TKR -ROLE OF SYNOVECTOMY.pptx
PERFECT BUT PAINFUL TKR -ROLE OF SYNOVECTOMY.pptx
 
Radiation Dosimetry Parameters and Isodose Curves.pptx
Radiation Dosimetry Parameters and Isodose Curves.pptxRadiation Dosimetry Parameters and Isodose Curves.pptx
Radiation Dosimetry Parameters and Isodose Curves.pptx
 
SYNDESMOTIC INJURY- ANATOMICAL REPAIR.pptx
SYNDESMOTIC INJURY- ANATOMICAL REPAIR.pptxSYNDESMOTIC INJURY- ANATOMICAL REPAIR.pptx
SYNDESMOTIC INJURY- ANATOMICAL REPAIR.pptx
 
COVID-19 (NOVEL CORONA VIRUS DISEASE PANDEMIC ).pptx
COVID-19  (NOVEL CORONA  VIRUS DISEASE PANDEMIC ).pptxCOVID-19  (NOVEL CORONA  VIRUS DISEASE PANDEMIC ).pptx
COVID-19 (NOVEL CORONA VIRUS DISEASE PANDEMIC ).pptx
 
The next social challenge to public health: the information environment.pptx
The next social challenge to public health:  the information environment.pptxThe next social challenge to public health:  the information environment.pptx
The next social challenge to public health: the information environment.pptx
 
ANTI-DIABETICS DRUGS - PTEROCARPUS AND GYMNEMA
ANTI-DIABETICS DRUGS - PTEROCARPUS AND GYMNEMAANTI-DIABETICS DRUGS - PTEROCARPUS AND GYMNEMA
ANTI-DIABETICS DRUGS - PTEROCARPUS AND GYMNEMA
 
PNEUMOTHORAX AND ITS MANAGEMENTS.pdf
PNEUMOTHORAX   AND  ITS  MANAGEMENTS.pdfPNEUMOTHORAX   AND  ITS  MANAGEMENTS.pdf
PNEUMOTHORAX AND ITS MANAGEMENTS.pdf
 
Basic principles involved in the traditional systems of medicine PDF.pdf
Basic principles involved in the traditional systems of medicine PDF.pdfBasic principles involved in the traditional systems of medicine PDF.pdf
Basic principles involved in the traditional systems of medicine PDF.pdf
 
Measurement of Radiation and Dosimetric Procedure.pptx
Measurement of Radiation and Dosimetric Procedure.pptxMeasurement of Radiation and Dosimetric Procedure.pptx
Measurement of Radiation and Dosimetric Procedure.pptx
 
Presentació "Real-Life VR Integration for Mild Cognitive Impairment Rehabilit...
Presentació "Real-Life VR Integration for Mild Cognitive Impairment Rehabilit...Presentació "Real-Life VR Integration for Mild Cognitive Impairment Rehabilit...
Presentació "Real-Life VR Integration for Mild Cognitive Impairment Rehabilit...
 
Primary headache and facial pain. (2024)
Primary headache and facial pain. (2024)Primary headache and facial pain. (2024)
Primary headache and facial pain. (2024)
 
April 2024 ONCOLOGY CARTOON by DR KANHU CHARAN PATRO
April 2024 ONCOLOGY CARTOON by  DR KANHU CHARAN PATROApril 2024 ONCOLOGY CARTOON by  DR KANHU CHARAN PATRO
April 2024 ONCOLOGY CARTOON by DR KANHU CHARAN PATRO
 
Music Therapy's Impact in Palliative Care| IAPCON2024| Dr. Tara Rajendran
Music Therapy's Impact in Palliative Care| IAPCON2024| Dr. Tara RajendranMusic Therapy's Impact in Palliative Care| IAPCON2024| Dr. Tara Rajendran
Music Therapy's Impact in Palliative Care| IAPCON2024| Dr. Tara Rajendran
 
Wessex Health Partners Wessex Integrated Care, Population Health, Research & ...
Wessex Health Partners Wessex Integrated Care, Population Health, Research & ...Wessex Health Partners Wessex Integrated Care, Population Health, Research & ...
Wessex Health Partners Wessex Integrated Care, Population Health, Research & ...
 
LESSON PLAN ON fever.pdf child health nursing
LESSON PLAN ON fever.pdf child health nursingLESSON PLAN ON fever.pdf child health nursing
LESSON PLAN ON fever.pdf child health nursing
 
Presentation for Bella Mahl 2024-03-28-24-MW-Overview-Bella.pptx
Presentation for Bella Mahl 2024-03-28-24-MW-Overview-Bella.pptxPresentation for Bella Mahl 2024-03-28-24-MW-Overview-Bella.pptx
Presentation for Bella Mahl 2024-03-28-24-MW-Overview-Bella.pptx
 
medico legal aspects of wound - forensic medicine
medico legal aspects of wound - forensic medicinemedico legal aspects of wound - forensic medicine
medico legal aspects of wound - forensic medicine
 
Apiculture Chapter 1. Introduction 2.ppt
Apiculture Chapter 1. Introduction 2.pptApiculture Chapter 1. Introduction 2.ppt
Apiculture Chapter 1. Introduction 2.ppt
 
Statistical modeling in pharmaceutical research and development.
Statistical modeling in pharmaceutical research and development.Statistical modeling in pharmaceutical research and development.
Statistical modeling in pharmaceutical research and development.
 

Healthcare Data Governance Demystified

  • 1. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright © 2014 Health Catalyst www.healthcatalyst.comCreative Commons Copyright Dales Sanders – May 7, 2014 Demystifying Healthcare Data Governance
  • 2. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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 2
  • 3. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright A Sampling of My Up & Down Journey TOO LITTLE DATA GOVERNANCE TOO MUCH 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 (1987) MMICS (1992) NSA Threat Reporting (1995) IMDB & PIRS (1996) Intel Logistics EDW (1998) Intermountain Healthcare (2005) Northwestern EDW (2009) Cayman Islands HSA 1983 2014 3
  • 4. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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.” 4
  • 5. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Centralized EDW; monolithic early binding data model Data Governance Cultures HIGHLY CENTRALIZED GOVERNMENT BALANCED GOVERNMENT HIGHLY DECENTRALIZED GOVERNMENT Centralized EDW; distributed late binding data model No EDW; multiple, distributed analytic systems 5
  • 6. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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 6
  • 7. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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 7
  • 8. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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 8
  • 9. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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% 9
  • 10. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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% 10
  • 11. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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 11
  • 12. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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 12
  • 13. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright The Data Governance Layers Happy Data Analyst 13
  • 14. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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.” 14
  • 15. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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.” 15
  • 16. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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.” 16
  • 17. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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.” 17
  • 18. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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.” 18
  • 19. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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.” 19
  • 20. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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.” 20
  • 21. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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 21
  • 22. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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 22
  • 23. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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% 23
  • 24. © 2014 Health Catalyst www.healthcatalyst.com Creative 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
  • 25. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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? 25
  • 26. Practice Protocols Processing EDW Analyzable 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 Support Enterprise Data Warehouse Electronic Health Record Mean Time To Improvement Clinical Information Systems Decisions & Actions Supporting 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 Evidence Clinicians’ suggestions External Evidence Literature, reports, etc. Quality Governance Use comparative data to identify best outcomes Determine standard order sets, protocols & decision support rules External Evidence Literature, reports, etc. Analyze data quality & process/outcome variations Generate the internal evidence Clinical Analytics Other Data Sources Clinical, Financial, etc. MTTI EHR & CDS Electronic 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 Evidence Information that clinicians trust Standards  Performance 26
  • 27. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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 27
  • 28. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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 28
  • 29. © 2014 Health Catalyst www.healthcatalyst.com Creative 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
  • 30. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright What Data Are We Governing? 30
  • 31. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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 31
  • 32. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Data Binding & Data Governance “systolic & diastolic blood pressure” Pieces of meaningless data 115 60 Binds data to Analytics Software Programming Vocabulary “normal” Rules 32
  • 33. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Why Is This Binding Concept Important? Data Governance needs to look for and facilitate both 33 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? Comprehensive Agreement Is the rule or vocabulary stable and rarely change? Persistent Agreement
  • 34. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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) 34
  • 35. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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 35
  • 36. Start Within Your Scope of Influence We are still learning how to manage outpatient populations 36
  • 37. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 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 37
  • 38. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright 38 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 APP Access 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.
  • 39. © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright Contact Info and Q&A dale.sanders@healthcatalyst.com @drsanders www.linkedin.com/in/dalersanders/ 39

Hinweis der Redaktion

  1. Clinical TeamEHR TeamAnalytics TeamPerformance Loop