Health Information Analytics: Data Governance, Data Quality and Data Standards

Health Information Analytics
Data Governance, Data Quality and Data Standards
HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 1

Materials Covered
• Textbook Chapter 5
• Supplemental materials I uploaded to Blackboard
• HIMSS Data Collections
Need to review them throughout the rest of the class
• Reference Book
It all starts with a data warehouse

What Data Governance Is
• Data governance (Information Governance) is:
• The scope of data governance
includes data stewardship, storage,
and technical roles and
responsibilities.
• It also requires leadership and
processes to get the most out of an
investment in analytics.
 The specification of decision rights and an accountability framework to
ensure appropriate behavior in the valuation, creation, storage, use,
archiving, and deletion of information. It includes the processes, roles and
policies, standards, and metrics that ensure the effective and efficient use
of information in enabling an organization to achieve its goals.”

4
Enterprise Business Intelligence and Analytics Architecture
SatisfySource Store
Organization, Stewardship and Governance
Data and Metadata Management
Data Flows
Existing
Planned
Manual
Data Back
to Sources
P_Central
Financial
Systems
(e.g. Epicor)
InfoPath
ReferralComplex
Paybase
Kwiktag
NuView
(HR)
ARCH
PRN
CRM
CFMS
Hosted
CATS
EBS
(Payroll)
Health-eSystems
(Rx) CS Stars
(Paid Claims)
Systems
Existing
Planned
External
Heat
Stage
Master Data
Data
Stewards
Match/Merge
Master Data Management
Scorecards &
Dashboards
OLAP
Enterprise
Reports
Ad hoc
Reports
DW
/ODS
Benchmark
Tasking
Client
Portal
Enterprise
BI Tool
(SSRS)
SAS
FutureStateRecommendations
HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang
• Data Governance and Data Stewardship throughout the life cycle of enterprise information
management (EIM)

Governance of Healthcare Data
• Data governance describes the concept of
managing and influencing the collection and
utilization of data in an organization.
• Demand for data governance growing due to
increased data demand for ACO and population
health management
• Tendency to operate in extremes, either too
much or too little governance
266

Keys to Analytic Success – A Combination of Right “Sets”
• Setting the tone of “data driven” for the
culture so that the organization is
embracing it.
• Actively building and recruiting for data
literacy among employees and provide
training to physicians and other frontline
staff members
• Choosing the right kind of tools to
support analytics and data governance
Mindset
Skillset
Toolset

Healthcare Analytic Adoption Model
In the Healthcare Analytic Adoption Model, a robust data governance
function is required in order to achieve the conditions of Level 5 maturity.
Level 8
Level 7
Level 6
Level 5
Level 4
Level 3
Level 2
Level 1
Level 0
Precision Medicine, Big Data
& Prescriptive Analytics
Clinical Risk Intervention
& Predictive Analytics
Population Health Management
& Active Analytics
Data-driven Improvement of
Clinical Process & Outcome
Automated External Reporting
Automated Internal Reporting
Standardized Controlled Vocabulary
& Patient Registries
Enterprise Data Warehouse
Fragmented Point Solutions
• Tailoring patient care based on population outcomes and
genomics data. Treatment and engagement include IoT.
• 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 & dashboards
widely available 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.

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

The Data Governance Layers
Happy Data
Analyst and
User

Different Roles Have Different Analytic Needs
Executive & Board Leadership, Sponsors
• Objectives: 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.
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 experienced in descriptive and
predictive analysis, as well as the IT staff who can support them.
• The following roles in the organization should have the following
types of access to the EDW and our analytics system.

Data Stewards
• I’m responsible for patient
registration and related data
integrity.
• I’m responsible for clinical
documentation in EMR and can
participate in data quality
improvement efforts.
• I am responsible for revenue
cycle and cost accounting and
can help reconcile administrative
data.
Data Architects & Programmers
• We will extract and organize the data
from the registration, EMR, revenue
cycle, and cost accounting and load
them into the data warehouse.
• “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.”

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.”
Data access & control system
• When this person logs in, they
have the following rights to
create, read, update, and
delete this data in the system.”
Data Analysts
• I’ll log into the system and build
a query against the data that
should answer these types of
questions.
• “Data Stewards, can I cross
check my results with you to
make sure I’m pulling
the data correctly?”
• “Data architects, I’ll let you
know if I have any trouble with
the way the data is organized or
modeled.”

Data Governance
• Govern to the least extent required for the common good
Base your committee charter on…
Encouraging more,
not less, data access
Increasing data content
in the datawarehouse
Campaigning for
data literacy
Resolving analytic
priorities
Enhancing data quality
Establishing standards for
Master Reference Data

Data QualityEssentials
of
DATA
GOVERNANCE
1
Data Quality = Completeness x Validity x Timeliness of Data.
• Data quality is the single most important function of data
governance.
• Low data quality negatively impacts decision accuracy or
timeliness
• Related to Master Data, Metadata, Data Standards, etc.

“Quality is Free. . . . What costs money are the
bad quality things — all the actions that
involve not doing jobs right the first time”
Philip Crosby,
Quality is Free. New York: McGraw-Hill
1979

Definition of Data Quality
• Data Quality is the theory of controls of
Quality (data profiling - data cleansing)
Quantity (data auditing)
• on Data for
Verifying
Improving
Information Accuracy.

Causes of Low Data Quality
• Type of Data
Many types of data become quickly obsolete (approximately in a
month 2% of a healthcare database records change: changes of
insurance, changes of residence, died)
• Technological
Old legacy systems and/or with few controls in data entry
Errors in conversion’s routines
Data coming from external sources (for example, Web)
Redundant Data Architectures
• Organization’s culture and process
Insufficient perception of data quality level
Absence of commitment in improving data quality
Knowledge workers often are producers of data custom

From January through
March 2007, Gartner
conducted a survey
among attendees of 3 BI
conference in Chicago,
Sydney and London.
Those surveyed were
made aware that their
answers would be
treated as anonymous.
The survey lasted about
15 minutes and had 301
respondents, of which
142 were in London, 136
in Chicago and 23 were
in Sydney.
Gartner’s Survey

• Data stored in separate silo systems
Are Financial Data in Healthcare Accurate and Valid?
• Large quantities of data to provide
billing and patient care
 Estimates of 100 MB of data generated
PPPY
• Healthcare data is highly volatile
 Business definitions are very complex
and data metrics are constantly
changing.
 Different clinicians may use different
definitions for the same metric and
decisions can be skewed if users don’t
know which metric was reported.

Functional Components
Data Quality
Data Profiling
Data Cleansing
Data Audit

Data Profiling
• Data profiling determines the level of satisfaction and
accuracy maintaining the rules of the quality of data:
Evaluate the degree of compliance of each data source with the
expectation of business
Provide statistical information on the distribution of values and
patterns related with each attribute (p.e. range analysis, missing
value, recognition of abstract types,analysis of overloading
attributes, …)
Analyze relationships and dependencies between attributes to
discover hidden identifiers, embedded structures, duplicated values
Define business rules not represented in data and relation and allow
to define new one
Data Profiling is responsible for identifying suitable Data Source for "Master"
business entity that ensure the required quality

Data Cleansing
• Data Cleansing is responsible for standardization,
reconciliation and correction of information.
• Supplies instruments for quality rules design
• Implements standard processes of data quality
• Normalizes data that do not respect quality rules
Data Cleansing solves missing data, corrects conflicts manages constraints,
resolves relations and hierarchies to ensure the quality of data

Data Auditing
• Data Auditing is the component responsible for the logging
and auditing of data memorization and migration:
• Provides services used by run-time components to collect information on
data on transit
• Collects and organizes data usable by data profiling
• Logs of data on transit are provided in a standard format and are collected
at enterprise level
Data Auditing is responsible for tracing the data movement in the
framework information life cycle
Copyright © 2016 Frank F. WangHCAD 6635 Health Information Analytics 23

Master Data ManagementEssentials
of
DATA
GOVERNANCE
2 The Data Governance Committee defines, encourage use,
and resolves conflicts in master data management.
• Master Data is critical business data shared among
multiple systems.
• In healthcare, Master Data are devided into three types:
Core measures and algorithms—such as
readmission criteria, or attributing patients to
providers in accountable care arrangements
Reference data—which includes common
linkable controlled vocabulary like ICD, CPT,
DRG, SNOMED, LOINC, RxNorm, and order sets
Identity data—such as patient, provider, and
location data standards identifiers (facility
codes, department codes, etc)

What is Master Data Management (MDM)
• Master data management is,
the process of linking identity
data and reference data
across multiple IT systems
into a single, consistent point
of reference.
• A more formal definition:
MDM 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.
Mergers and Acquisitions (M&A): Because
data configuration of multiple providers are
usually so different, MDM is needed to
merge the data.
Health information exchanges (HIEs): To
successfully exchange information across
locations and organizations, HIEs have to be
able to reconcile master data.
ACOs: To understand and manage their
patient populations, ACOs bring together
health system data and payer data. This
process demands a solid MDM.

Three Approaches to MDM
IT system consolidation
• To abandon best-of-breed
solutions in favor of
monolithic EMR (Epic,
Cerner) and ERP (Lawsons
and Peoplesofts) solutions.
• Large hospital systems favor
this approach
Upstream MDM implementationI
• keep their disparate IT systems but map
their master data through a third-party
tool such as an enterprise master patient
index (EMPI).
Pros:
• Relatively comprehensively
• MDM is handled at the level
of transactional systems,
master data is reconciled at
the time of the transaction.
Cons:
• Complicated and expense
• May be a need for more
MDM between data sources.
I
Pros:
• Master data problems aren’t reconciled in
the source, they are reconciled very near
the source. In addition, these systems
allow for extensive manual adjudication.
Cons:
• Complicated, large, expensive, and slow-
moving IT projects.
• Tends to have a high failure rate.

Three Approaches to MDM
Pros:
• Is a very achievable solution to the
problem.
Downstream master data
reconciliation in an enterprise
data warehouse (EDW)
Cons:
• Drawback of this approach, is that the
mastered data is only available for
analytics.
• An EDW will not solve master data
challenges at the level of
transactional systems.
• When an organization needs to do
analytics, but doesn’t have another
MDM solution in place.
• When an organization inevitably
starts integrating data sources from
outside its consolidated
infrastructure or EMPI.
• ACO and PHM require insurance
claims data. MDM of claims data
simply not available in existing
healthcare providers’ solutions.
Is EDW the Right Solutions to
Address MDM?

28
Enterprise BI /Analytics Architecture – Future State
SatisfySource Store
Organization, Stewardship and Governance
Data and Metadata Management
Data Flows
Existing
Planned
Manual
Data Back
to Sources
P_Central
Financial
Systems
(e.g. Epicor)
InfoPath
ReferralComplex
Paybase
Kwiktag
NuView
(HR)
ARCH
PRN
CRM
CFMS
Hosted
CATS
EBS
(Payroll)
Health-eSystems
(Rx) CS Stars
(Paid Claims)
Systems
Existing
Planned
External
Heat
Stage
Master Data
Data
Stewards
Match/Merge
Master Data Management
Scorecards &
Dashboards
OLAP
Enterprise
Reports
Ad hoc
Reports
DW
/ODS
Benchmark
Tasking
Client
Portal
Enterprise
BI Tool
(SSRS)
SAS
FutureStateRecommendations

“Metadata is akin to an encyclopedia for
the data warehouse.”
Ralph Kimball, Margy Ross
The Data Warehouse Toolkit – Second Edition,
The Complete Guide to Dimensional Modeling
John Wiley & Sons, Inc
2002

Metadata Definitions and Why We Need to Manage Metadata
• Metadata is information regarding the characteristics of any artifact, such
as its name, location, perceived importance, quality or value to the
enterprise, and its relationships to other artifacts that an enterprise has
deemed worth managing.
• Metadata is all of information in the data warehouse enviroment that is
not the actual data itself.
“Metadata is Data of Data.”
• Understanding metadata means knowing clearly the meaning of what is
described (Knowledge Information)
• They grant a clear communication because they allow the sharing of the
same concepts with the goal to join the same target (Data Dictionary)
• They represent a guide for browsing in the different areas of the Company
(Cross Knowledge)

Why Metadata Matters to Analytics/Business Ingelligence Initiatives
Traditional business intelligence has focused on collating metadata from
two or more data repositories, rather than reconciling the enormous
amount of assumed and undocumented metadata regarding the business
process that populate those repositories
A modern, enterprise-capable initiative was always the goal of analytics,
but this has been thwarted by a belief that metadata is useful for
documentation only
The creation and capture of metadata is the real key to ensuring the longevity of
an information management life cycle — and analytics/business intelligence is one
beneficiary, or victim, of its own behavior regarding metadata.

Types of Metadata
• Based on their content
 Business Metadata
 Technical Metadata
 Operational Metadata
• Based on their organization
 Structured (records, tables, schemas, ETL mapping, …)
 Unstructured (file, email, documents, diagrams, …)
• Business Metadata
 Common Data Model (Business Units, Business Entities, business attributes,
relations between business entities)
 Business Rules and Policies
 Business Views
 Ontologies (categories and terms, synonym and correlation)

Healthcare Specific Business Metadata: Revenue Cycle Management
• The provider submits a claim
 Claim must include at least one diagnosis code, and one procedure code for each
service rendered
• Diagnosis code = ICD-10-CM (ICD-9-CM prior to Oct 1 2015)
• Procedure code = CPT code or DRG code
Appointment Registration
•Registration:
Demographic and
insurance info
Service
Rendered
•Services (Diagnosis,
Procedure,
Treatment, Lab)
Charge capture coding
Claims
submitted
Reimbursement
received
Settlement with
patients
Simplified
Revenue
Cycle
Process
Charge capture process: collecting a list of
all services, procedures, and supplies
provided during an encounter
Charge Description Master List (price list)
Coding and Code Sets are Metadata
• Coding: process of translating the written diagnosis and procedures relating to a patient
encounter into a numeric classification or code
• Code set: group of numeric or alphanumeric codes used to encode descriptive data
elements
 Tables of terms, medical concepts, medical diagnostic codes, or medical
procedure codes
 A code set includes the codes and the descriptors of the codes

SNOMED CT and ICD
Allergic asthma
389145006
Aspirin-induced
asthma
407674008
Acute asthma
304527002
Drug-induced asthma
93432008
Work aggravated
asthma
416601004
Allergic bronchitis
405720007
Chemical-induced
asthma 92807009
Brittle asthma
225057002
Sulfite-induced
asthma 233688007
Millers' asthma
11641008
Asthma attack
266364000
Asthma night-time
symptoms 95022009
Etc.
SNOMED CT
Asthma
95967001
Asthma, Unspecified,
uncomplicated
J45.909
ICD-10-CM
Other asthma
J45.998
ICD-10-CM
Asthma, Unspecified
Type, unspecified
493.90
ICD-9-CM
OR
Cross Links between Different Code Sets

Data Quality Control: Medical Coding and Controlled Vocabulary
Hemolytic uremic syndrome
(disorder) 111407006
(SNOMED CT)
Stool culture + e.coli 0157
(Lab Code)
Hemolytic-uremic Syndrome
D59.3
(ICD-10CM)
(SNOMED CT)
Hemolytic anemia
(disorder) 61261009
Serum creatinine raised
(finding) 166717003
Hemorrhagic diarrhea
(disorder) 95545007
Abdominal pain
(finding) 21522001
Patient
Signs & Symptoms
Diagnosis
Lab Result
Notifiable Disease
Billing

HIPAA Code Sets
• Health Care Common Procedure Coding System (HCPCS) & Current
Procedural Terminology (CPT) by American Medical Association (AMA)
• ICD-10-CM (diagnosis codes)
• ICD-10-PCS (procedures codes) by National Center for Health Statistics
& CMS respectively
• National Drug Codes (NDC) by Food and Drug Administration and drug
manufacturers
• Code on Dental Procedures and Nomenclature (CDT) by American
dental Association (ADA)

Code Sets Used by Healthcare Providers
• Diagnosis
 Upper respiratory infection = J01.99 (ICD-10-CM)
• Service, procedure or test
 New patient, office visit, level II = 99202 (CPT)
 Biopsy of skin, subcutaneous tissue and/or mucous membrane(including
simple closure), unless otherwise listed; single lesion = 11100 (CPT)
 Immune globulin 10 mg = J1564 (HCPCS Level II)
• Physician - Inpatient and outpatient
 Diagnosis – ICD-10-CM
 Procedure – CPT
• Hospital Facility – inpatient
 Procedure – ICD-10-PCS
• Hospital Facility – outpatient
 Procedure – HCPCS (CPT Level I and HCPCS Level II)

Messaging
Vocabularies
Data Models
Standards for Clinical
Research and Pharmaceutical
Product Development
Standards for Healthcare
HL7 RPS, Clinical Genomics )
E2B (for safety reports )
DICOM (for images )
HL7 v2.x and v3.0
NCPDP (for Rx)
DICOM
IEEE (Bedside Instruments , MIB)
X12N (for Financial data / HIPAA)
MedDRA (for drug safety)
WHODrug (for drug safety )
VA/KP/SNOMED (for SPL)
FDA DRLS, FDA SRS (for SPL)
NCI Thesaurus (for SPL)
LOINC (for SPL )
NDF-RT (for SPL )
CDISC/RCRIM terminology (for CRF)
HUGN (genomic data )
SNOMED CT (for clinical data )
ICD9CM (for billing diagnoses )
CPT (for billing procedures )
LOINC (for lab)
NDF-RT, RxNorm for drugs
HCPCS/APC’s (add’l claims data )
HUGN (genomic data )
CDISC
SDTM ODM LAB Define.XML
PROTOCOL (SCTP) ADaM
HL7 RIM HL 7 CDA
Templates
Order sets
Medical Metadata: Code Sets, Standards & Controlled Vocabularies

Data Model …
“Design process which aims to
identify and organize the
required data logically and
physically”
• Which (attributes) information
should be included in the database
• How the information will be used
• How the data in the database are
related to each other

Data Modeling: a Conceptual Data Model of Dietician

Data Modeling: a Logical Data Model of Diabetics Diagnosis and Treatment

Design of Data Model is based on Analytic Requirement
Diabetes
Patient
Typical Analyses
• How many diabetes patients do I have?
• When was there last HA1C, LDL, Foot
Exam, Eye Exam?
• What was the value for each instance for
the last 2 years?
• What are all the medications they are on?
• How long have they been taking each
medication?
• What was done at each of their visits for
the last 2 years?
• Which doctors have seen these patients
and why?
• List of all admissions and reason for
admission?
• What co-morbid conditions do these
patient have?
• Which interventions (diet, exercise,
medications) are having the biggest
impact on LDL, HA1C scores?
Procedure
History
Vital Signs
History
Current Lab
Result
Lab Result
History
Office Visit
Exam Type
Exam History
Diagnosis
History
Diagnosis
Code
Procedure
Code
Lab Type
Sample Data Model of Diabetics Diagnosis and Treatment

Data AccessEssentials
of
DATA
GOVERNANCE
3
Increasing access to data,
including external stakeholders,
community members and
especially patients, is a critical
Committee function.
The Committee bridges stakeholders to
streamline decision making and
departmental reconciliation.

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

Data ContentEssentials
of
DATA
GOVERNANCE
5
• Activity-based-costing data, genetic and familial data,
bedside devices data, patient reported observations and
outcomes data, and ultimately sensor data (Internet of
Things) in the analytic journey.
• Recognize this is an evolutionary process and can take as
many as ten or more years to complete.
• The Data Governance Committee should plan a multi-
year strategy for data provisioning and acquisition

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

Effective Healthcare Data Governance Is Adaptive
Data Governance Life Cycle
1. The Early Stage of Healthcare Data
Governance
2. The Mid-term Stage of Healthcare Data
Governance
3. The Steady State of Healthcare Data
Governance
• No single template for data
governance.
• Experience in other industry suggests
data governance approached as a rigid,
idealized plan often ends up being
scrapped.

The Early Stage of Healthcare Data Governance
Committee Makeup
• In the early stages, sponsors of the health system’s data
governance initiative must have some decision-making authority.
THREE PHASES
DATA
GOVERNANCE
1
• Leaders have a passion for
using data.
• Executives, directors in the
quality department, nurses,
or physicians.
• Where should the initiative begin?
• Where should resources focus?
• First role will be to keep the peace,
and ensure everyone impacted
understands the priority.
• They must protect the integrity of
the initiative to drive real quality
and cost improvement.
Committee Fous

The Mid-Term Stage of Healthcare Data Governance
Committee Makeup
As your implementation of analytics continues, the nature of the
decisions made by the data governance committee will change.
THREE PHASES
DATA
GOVERNANCE
2
• Some members will lose interest
and too busy with other initiatives.
• Viewed as an operational problem.
• Their replacements may bring new
energy to the initiative.
• Responsible for monitoring the
progress of existing initiatives.
 How are things going?
 Who is using the system?
 What additional training or tools are
needed to increase utilization?
 What should we keep doing/stop
doing/do more of?
• Start a new initiative in another
department.
Committee Focus

The Steady State of Healthcare Data Governance
Council Makeup
• May change the name to
governance council.
• Few of the initial leaders will
still be involved. New
leaders with consensus
building style are likely to
succeed.
• Hear customers’ concerns,
make their voices heard,
champion for improvement,
and keep stakeholders in
the loop until their concerns
are addressed.
THREE PHASES
DATA
GOVERNANCE
3
• Stay the course in
spite of vocal
resistance from other
departments.
• If the initiative is working for most of the
organization, actively engage the
disenfranchised and bring them around.
• Continue to be responsible for
monitoring the success of the initiative
and prioritizing efforts.
Council Focus

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

Too Much Data Governance
• Unhappy data analysts… and their customers
 Everything takes too long
 Loading new data
 Changes data models to support new analytic use cases
 Getting access to data
 Resolving data quality problems
 Developing new reports and analyses

How to Know If Healthcare Data Governance is Working
• Healthcare data governance is most effective when it is allowed
flexibility to adapt, change and evolve when organization
becomes mature. Key Metrics to Study:
• # of users
• # of requests
• # of queries, reports and page
views
• # of success stories from your
users!
• # of key stakeholders who are
aware of your group and what
you do
If governance is successful,
these metrics will trend up:
• User/customer satisfaction
• Technology/analytics team satisfaction
• Time and resources it takes to answer
common analytic questions
• # of requests you get to evaluate
competing analytic systems
These metrics should hopefully trend
down:
These metrics should stay solid, or
trend upwards in rare cases:

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

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
56

Health Information Analytics: Data Governance, Data Quality and Data Standards

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Health Information Analytics: Data Governance, Data Quality and Data Standards