demystifying healthcare data governance

Demystifying Healthcare Data Governance

— Dale Sanders

© 2014 Health Catalyst

www.healthcatalyst.comProprietary. Feel free to share but we would appreciate a Health Catalyst citation.

Data Governance in Healthcare

As the age of analytics

emerges in healthcare,

health system executives

are increasingly challenged

to define a data governance

strategy that maximizes

healthcare data’s value to

the mission of their

organizations

2

— Andreas WeigendFormer Amazon Scientist

Data is the new oil!”

https://www.healthcatalyst.com/5-reasons-healthcare-data-is-difficult-to-measure



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

Dale Sanders



The Sanders Philosophy of Data Governance

4



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



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



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



What’s It Look Like?

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

8



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

9



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

10



The Data Governance Layers

11

Happy Data

Analyst



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.”

12




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.”

13




Data Stewards

I’m responsible for patient

registration. I can help.”

I’m responsible for clinical

documentation in Epic. I can help.”

I’m responsible for revenue cycle

and cost accounting. I can help.”

14




Data Architects & Programmers

We will extract and organize the data from the

registration, EMR, rev cycle, and cost

accounting and load it into the EDW.”

“Data stewards, can we sit down with you and

talk about the data content in your areas?”

“DBAs and Sys Admins, here are the roles

and access control procedures for this data.”

15




DBAs & System Administrators

Here is the access control list and

procedures for approving access to this

data. Let’s build the data base roles and

audit trails to support these.”

16




Data access & control system

When this person logs in, they have the

following rights to create, read, update,

and delete this data in the EDW.”

17




Data Analysts

I’ll log into the EDW and build a query

against the data in the EDW that should be

able to answer these types of questions.”

“Data Stewards, can I cross check my

results with you to make sure I’m pulling

the data properly?”

“Data architects, I’ll let you know if I have

any trouble with the way the data is

organized or modeled.”

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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

19



Data Governance Committee Failure Modes

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

20




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

21




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

22




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

23



Data Governance & 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



Tools for Data Governance

Data quality reports

25

Data Quality = Validity x Completeness

To achieve the Triple Aim of Data Governance, the governance committee needs reports that exposes data quality.

Data stewards use these reports in their efforts to close the gaps in data quality for the systems of their responsibility.

https://www.healthcatalyst.com/how-analytics-achieve-triple-aim




CRM tools for the data warehouse

26

The data governance committee will also need reports for understanding how the data warehouse is being used.

• Who’s using the data?

• When is the data being used?

• Why acquire the data?

https://www.healthcatalyst.com/success_stories/improve-quality-with-hospital-EDW/




“White Space” data management tools

27

For capturing and filling-in computable data missing from source systems.

Sometimes this white space data is manually abstracted and manually integrated on desktop computers using Excel or Access.

These tools replace spreadsheets and databases by providing an easy-to-use data entry tool that is tightly coupled with the EDW.




Metadata Repository

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The metadata repository serves as the “Yellow Pages” for the EDW. It is the tool used to browse the EDW data and attributes.

– What’s in the data warehouse?

– Are there any data quality problems?

– Who’s the data steward?

– How much data is available and over what period of time?

– What’s the source of the data?

https://www.healthcatalyst.com/late-binding-data-warehouse/data-acquisition-storage/metadata-engine/



Healthcare Analytics Adoption Model

29

Modeled after the HIMSS Analytics EMR Adoption Model, the Healthcare Analytics Adoption Model provides a framework for evaluating an organization’s adoption of analytics.

It also provides a roadmap for developing analytics strategies, both for vendors and for internal use by healthcare delivery organizations.

https://www.healthcatalyst.com/healthcare-analytics-adoption-model/



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

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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”

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

31



Six Phases of Data Governance

You need to move through

these phases in no more

than two years

32

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



What Data Are We Governing?

33



Master Data Management

The data that is mastered includes:

– Reference data - the dimensions for analysis

– Analytical rules – supports consistent data binding

Master data management is comprised of processes, governance, policies, standards, and tools that consistently define and manage the critical data of an organization to provide a single point of reference.

- Wikipedia

34

https://www.healthcatalyst.com/master-data-management-in-healthcare-3-approaches



Data Binding & Data Governance

“systolic &

diastolic

blood pressure”

Pieces ofmeaningless

data

11560

Bindsdata to

Analytics

Software

Programming

Vocabulary

“normal”

Rules

35



Why Is This Binding Concept Important?

Data Governance needs to look for and facilitate both

36

Knowing when to bind data, and how

tightly, to vocabularies and rules is

CRITICAL to analytic success and agility

Is the rule or vocabulary widely

accepted as true and accurate in

the organization or industry?

ComprehensiveAgreement

Is the rule or vocabulary stable

and rarely change?

PersistentAgreement



Vocabulary: Where Do We Start?

Charge code

CPT code

Date & Time

DRG code

Drug code

Employee ID

Employer ID

Encounter ID

Gender

ICD diagnosis code

ICD procedure code

Department ID

Facility ID

Lab code

Patient type

Patient/member ID

Payer/carrier ID

Postal code

Provider ID

In today’s environment, about 20 data elements

represent 80-90% of analytic use cases. This

will grow over time, but right now, it’s fairly simple.

Source data vocabulary Z (e.g., EMR)

Source data vocabulary Y (e.g., Claims)

Source data vocabulary X

(e.g., Rx)

37



Where Do We Start, Clinically?

We see consistent opportunities, across the industry,

in the following areas:

• CAUTI

• CLABSI

• Pregnancy management, elective induction

• Discharge medications adherence for MI/CHF

• Prophylactic pre-surgical antibiotics

• Materials management, supply chain

• Glucose management in the ICU

• Knee and hip replacement

• Gastroenterology patient management

• Spine surgery patient management

• Heart failure and ischemic patient management

38



Start Within Your Scope of InfluenceWe are still learning how to manage outpatient populations

39



In Conclusion

Practice democratic data governance

– Find the balance between central and decentralized governance

– Federal vs. States’ rights is a good metaphor

The Triple Aim of Data Governance

– Data Quality, Data Literacy, and Data Exploitation

Analytics gives data governance something to govern

– Start within your current scope of influence and data, then grow from there

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More about this topic

Becoming the Change Agent Your Healthcare System Needs

Dr. John Haughom, Senior Advisor

3 Phases of Healthcare Data Governance in Analytics

Mike Doyle, Vice President of Sales

Data Governance: 7 Essential Practices

Dale Sanders, Senior Vice President of Strategy

How Accountable Care Organizations Will Drive Demand for Data Analytics

Dr. David Burton, Former CEO and Executive Chairman

Discovering Patterns in the Data to Improve Patient Care

Dr. John Haughom, Senior Advisor

Link to original article for a more in-depth discussion.

Demystifying Healthcare Data Governance

https://www.healthcatalyst.com/becoming-change-agent-healthcare-system-needs

https://www.healthcatalyst.com/3-phases-healthcare-data-governance-analytics

https://www.healthcatalyst.com/healthcare-data-governance-practices

https://www.healthcatalyst.com/acos-drive-deman-for-data-warehouse-html

https://www.healthcatalyst.com/webinar/the-analytic-system-discovering-patterns-in-the-data/

https://www.healthcatalyst.com/demystifying-healthcare-data-governance



For more information:

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The New Era of Opportunity

“This is a knowledge source for clinical and

operational leaders, as well as front-line

caregivers, who are involved in improving

processes, reducing harm, designing and

implementing new care delivery models, and

undertaking the difficult task of leading

meaningful change on behalf of the patients

they serve.”

– John Haughom, MD, Senior Advisor, Health Catalyst

https://www.healthcatalyst.com/ebooks/healthcare-transformation-healthcare-a-better-way/

https://www.healthcatalyst.com/ebooks/healthcare-transformation-healthcare-a-better-way/



Other Clinical Quality Improvement Resources

Click to read additional information at www.healthcatalyst.com

Dale Sanders has been one of the most influential leaders in healthcare analytics and

data warehousing since his earliest days in the industry, starting at Intermountain

Healthcare from 1997-2005, where he was the chief architect for the enterprise data

warehouse (EDW) and regional director of medical informatics at LDS Hospital. In

2001, he founded the Healthcare Data Warehousing Association. From 2005-2009, he

was the CIO for Northwestern University’s physicians’ group and the chief architect of

the Northwestern Medical EDW.

From 2009-2012, he served as the CIO for the national health system of the Cayman Islands where

he helped lead the implementation of new care delivery processes that are now associated with

accountable care in the US. Prior to his healthcare experience, Dale had a diverse 14-year career

that included duties as a CIO on Looking Glass airborne command posts in the US Air Force; IT

support for the Reagan/Gorbachev summits; nuclear threat assessment for the National Security

Agency and START Treaty; chief architect for the Intel Corp’s Integrated Logistics Data Warehouse;

and co-founder of Information Technology International. As a systems engineer at TRW, Dale and

his team developed the largest Oracle data warehouse in the world at that time (1995), using an

innovative design principle now known as a late binding architecture. He holds a BS degree in

chemistry and minor in biology from Ft. Lewis College, Durango Colorado, and is a graduate of the

US Air Force Information Systems Engineering program.

http://www.healthcatalyst.com/