1. John Sharp, MSSA, PMP, FHIMSS
Research Informatics
Developing a Centralized
Repository Strategy:
The Top Three
Success Factors

2. Introduction
• As healthcare organizations seek ways to better
predict the true transformation of healthcare,
centralized data repositories hold the keys to unlocking
the potential for predicting the impact of quality on
patient care.
• Learning Objectives:
– Determine your healthcare quality challenge for leveraging data
– Identify the top three critical success factors for a centralized
repository strategy

3. Current State of Quality Reporting –
Multiple Demands
• Joint Commission
• CMS
• Get with the Guidelines – AHA
• Meaningful Use
• Core Measures

4. Current State of the Data
Claims data
EMR data – new for many health systems and
reporting systems emerging
Other clinical systems
Public health, population data
Reference data
Genomic data

5. Three Success Factors of a Centralize Data Repository
Buy in and
governance
Organizing Data
Displaying data in
a meaningful way

6. BUY-IN AND GOVERNANCE

7. Buy-in and Governance
Hazards of Decentralized approach
– Many copies of the data – storage issues
– Different data definitions
– Confusion over public reporting
– Are all data repositories up-to-date?
– Multiple teams with different degrees of expertise
– Different ways of transforming the data
– Taxing systems which data is extracted from

8. Buy-in and Governance
Complaints about a centralized approach
• Give up control
• Too IT centric
• Slow response – queue is too long
• Can’t get my request prioritized

9. Buy-in and Governance
Solution
• Involve key stakeholders from the beginning
• Include institution leadership and clinical leadership
• Include data stewards
• Define end users of the data broadly

10. Buy-in and Governance
Data Stewards
• Develop consistent definitions and interpretations of
data and data concepts so that information can be
consistently interpreted
• Document where data originates and the processes
that act on it
• Help ensure data quality, accuracy, consistency,
timeliness, validity, and completeness
• Define appropriate controls to address data security
and privacy requirements
• http://himss.files.cms-
plus.com/HIMSSorg/Content/files/201304_DATA_GOVERNANCE_FINAL.p
df

11. Buy-in and Governance
Governance Committee Role
Increasing consistency and confidence in decision
making
Decreasing the risk of regulatory fines
Improving data security
Maximizing the income generation potential of data
Designating accountability for information quality
Enable better planning by supervisory staff
Minimizing or eliminating re-work

12. Buy-in and Governance
Governance Committee tasks
• Standards for defining – definitions and taxonomies
• Processes for managing – data quality, change
management
• Organizational responsibilities – oversight, prioritization
• Technologies for managing – data dictionaries, data
discovery tools

13. Organizing Data
Aggregate
Normalize
Analyze
DATA

14. DataFlux Data Governance Maturity Model
http://www.dataflux.com/DataFlux-Approach/Data-Governance-Maturity-Model.aspx

15. Organizing Data – Data Quality
• Data definitions – making sure that data are well
understood across the organization
• Data lineage – documenting how data are created,
transformed and intermingled
• Data accuracy – making sure that data accurately
reflect the clinical and business transactions and
activities of the organization
HIMSS Clinical & Business Intelligence: Data Management
– A Foundation for Analytics

16. Data Quality (2)
• Data consistency – making sure that data within and
across data stores provide a consistent representation
facts
• Data accessibility / availability – making sure those
who need data to perform their job function have
access to relevant data
• Data security – making sure that data access is
restricted to those who have a legitimate and legally
allowable need for the data

17. Data Quality - Definitions
How do you define a hospital admission?
• > 24 hours
• >= 24 hours
• Include time in the ED?
• What about direct admits after a procedure?
• What if the patient dies in a hospital bed in less than
24 hours
• Does CMS and private insurers have the same
definition?

18. Data Definitions
• ICD-9, ICD-10
• SNOMED-CT
• LOINC
• RxNorm
• National Quality Forum measure – definitions
– Numerators/Denominators

19. DISPLAYING DATA IN A
MEANINGFUL WAY

20. Basic Reports
Simplest solution for many
requirements
Rows and columns
Detail, summary or both
Daily, weekly, monthly
Must know source of data
Validation

21. Basic Graphs
 Bar, line, area – picking the appropriate display for
the data
Comparing trends – YTD, previous year, nursing units,
product lines
Making data actionable – how will this graph change
what I am doing?

22. Big Data Visualization
Assist in understanding greater degrees of complexity
May be 3 dimensional
Geocoding of data into maps
Network diagrams
Layers of data
Must work with customers to find most effective
display which is actionable

23. Disease prevalence and socioeconomic status
across the US

24. Network Diagram showing relationships between concepts

25. Heatmaps

26. Future of Clinical Data Repositories
Become familiar with big data tools, such as, Hadoop,
NOSQL, and use in unstructured data
Understand data growth – how big will your data be a
year from now?
Begin to do real-time or near real-time reporting to
impact quality
Stay on top of changing definitions

27. Three Success Factors of a Centralize Data Repository
Buy in and
governance
Organizing Data
Displaying data in
a meaningful way