This document summarizes the role and work of NEHTA, an organization established by the Australian government to facilitate e-health initiatives. It discusses NEHTA's work on healthcare identifiers and developing a data quality strategy, including defining data quality dimensions, standards, policies, and a maturity model. It also provides advice on prioritizing data quality and ensuring senior management support for these efforts.

1. From Data Quality to Clinical Safety
A year after the Award
Tatiana Stebakova
29 March 2010

2. Role of NEHTA
• NEHTA was set up and funded by Federal, State and
Territory Governments as a separate entity in 2005
• We facilitate and progress e-health for Australia
• Our Board comprises heads of health departments in all
Australian States and Territories
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3. Healthcare Identifiers
The Healthcare Identifiers (HI) Service
components:
? Individual Healthcare Identifier (IHI)
? Healthcare Provider Identifier –
Individual (HPI-I)
? Healthcare Provider Identifier –
Organisation (HPI-O)
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4. Data quality strategy
Data Quality Governance
Data Quality Dimensions
1. Semantic
2. Structure
3. Provenance
4. Completeness
5. Consistency
6. Currency
7. Timeliness
8. Accuracy
9. Fitness for Use
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10. Compliance
Quality Strategy 11. Quality rating
Quality Framework
Data Quality Standards & Practices
? Structure and format standards adhered to in all data exchanges Data Quality
? Certification of trusted data sources in place
? Community-wide data standards metadata management
Maturity Model
Data
? Exchange schemas are endorsed through data standards oversight
Data
process
Level 1 – Initial
Data Quality Policies & Protocols Level 2 – Repeatable
? Policy-based Data Quality management on individual and at Level 3 – Defined
community level Level 4 – Managed
? Data validation protocols Level 5 - Optimized
? Data Provenance management
Data Quality Technology and Operations Guidelines
? Standardization of Technology components across the community
? Design and service use guidelines
? Standardized techniques and procedures for data validation,
certification, quality assurance , and reporting
Data Quality Performance Management
? Measuring conformance to data quality standards, expectations
? Identifying where significant negative impacts are incurred due to
poor data quality
? Providing longitudinal tracking for identifying and measuring areas
for improvement.
Data Quality Implementation Roadmap
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5. Data Quality Performance measurement- Metrics
Dimension Characteristic Number of
Metrics
Semantic Data Definitions 3
Name Ambiguity 3
Structure Structural Consistency 22
Provenance Originating Data 3
Source
Completeness Optionality 41
Population density 35
Consistency Capture and collection 14
Presentation 4
Currency Age/Freshness 17
Temporal 1
Time of Release 1
Timeliness Accessibility 3
Response Time 3
Accuracy Precision 15
Value Range 44
Fitness for Use Coverage 49
Identifier Uniqueness 40
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6. Advice That Stood the Time
• Data quality means clinical safety in healthcare systems.
• Do not try to educate senior management on the importance of DQ
and
how it works. Just do it. They will thank you later.
• Write clear and detailed DQ requirements, measurements and KPIs .
• Make sure they are included in the design and operational contract.
• Define a clear DQ Strategy and Blueprint. Try to involve the best DQ
practitioners.
• Focus on the quality of attributes, which are strategic for your
business.
• Define a capability maturity model and a roadmap on how to achieve
the
desired level of maturity.
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7. The Biggest Impact
• Data Quality has full support of Senior Management!
• DQ requirements, measurements and KPIs are mandatory
for
each system or product, developed by NEHTA.
• DQ requirements are included in the design and operational
contract of Healthcare Identification Service and National
Authentication Service for Health.
• The decision is made to involve the best DQ practitioners to
write DQ Strategy and Blueprint for Personally Controlled
Electronic Health Records.
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8. Thank you and Questions
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