7 Essential Practices for Data Governance in Healthcare

© 2014 Health Catalystwww.healthcatalyst.comProprietary. Feel free to share but we would appreciate a Health Catalyst citation.


7 Essential Practices for Data Governance in HealthcareBy Dale Sanders

© 2014 Health Catalystwww.healthcatalyst.comProprietary. Feel free to share but we would appreciate a Health Catalyst citation. 2

The Value of Healthcare Data

As healthcare has become a more analytically driven industry, data is now one of the most valuable assets outliving facilities, devices and people.


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

Tendency to operate in extremes, either too much or too little governance


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

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.

http://www.slideshare.net/dalesanders1/analytic-adoption-model-v4


Balanced, Lean Governance

5

Start with a broad vision and framework but limited application.

Expand governance function only as needed

Govern to the least extent required for the common good.

Essentials of

DATAGOVERNANCE

1

Enhancing data qualityIncreasing data content

in the EDW

Encouraging more, not less data access

Resolving analytic priorities

Campaigning fordata literacy

Establishing standards for master reference data

Base your committee charter on…

Data governance committee needs data steward SMEs

When in doubt, govern less, not more. Keep it lean.


Data Quality

6

Essentials of

DATAGOVERNANCE

2

Data Quality = Completeness x Validity x Timeliness of Data. .

Data quality is probably the single most important function of data governance.

Low data quality negatively impacts decision accuracy or timeliness


Data Access

7

Essentials of

DATAGOVERNANCE

3

Increasing access to data, across all members of the enterprise – external stakeholders, community members and especially patients, is a critical Committee function.

The Committee bridges internal stakeholders to streamline decision making and departmental reconciliation.


Data Literacy

8

Essentials of

DATAGOVERNANCE

4

Data literacy can be increased by:

1. Education – good data from bad data

2. Data analysis tools

3. Data driven process improvement

4. Applying statistical techniques to improve decision making process

5. Deliberate collection and dissemination of metadata

Data serves no purpose if intended beneficiaries cannot interpret or use the data.


Data Content

9

Essentials of

DATAGOVERNANCE

5

For example, activity-based-costing data, genetic and familial data, bedside devices data, and patient reported observations and outcomes data are all critically important to the evolution of analytics in the industry.

Building and acquiring the systems to collect this data is the first step in the analytic journey and can take as long as five years to complete.

The Data Governance Committee should plot a multi-year strategy for data provisioning and acquisition


Analytic Prioritization

10

Essentials of

DATAGOVERNANCE

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%


Master Data Management

11

Essentials of

DATAGOVERNANCE

7 Over time, the Data Governance Committee defines, encourage use, and resolves conflicts in master data management.

Local data standards (facility codes, department codes, etc.);

Regional and industry standards (CPT, ICD, SNOMED, LOINC, etc.).

Defining algorithms such as readmission criteria, or attributing patients to providers in accountable care arrangements.

© 2013 Health Catalystwww.healthcatalyst.com

Other Clinical Quality Improvement Resources

Click to read additional information at www.healthcatalyst.com

A Landmark, 12-Point Review of Population Health Management Companies (Also by Dale Sanders)

Prior to his healthcare experience, Dale Sanders worked for fourteen years in the military, national-intelligence, and manufacturing sectors, specializing in analytics and decision support. In addition to his role at Health Catalyst, he continues to serve as the senior technology advisor and CIO for the National Health System in the Cayman Islands. Previously, he was CIO with

the Northwestern University Medical Center, and regional director of Medical Informatics at Intermountain Healthcare where he served in a number of capacities, including chief architect of Intermountain’s enterprise data warehouse. Dale is a founder of the Healthcare Data Warehousing Association. He holds Bachelor of Science degrees in Chemistry and in Biology from Ft. Lewis College, Durango Colorado, and is a graduate of the U.S. Air Force Information Systems Engineering program.

http://www.healthcatalyst.com/

http://www.healthcatalyst.com/landmark-review-of-population-health-management-companies



http://www.healthcatalyst.com/author/dale-sanders/

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7 Essential Practices for Data Governance in Healthcare