Tatiana Stebakova


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Tatiana Stebakova

  1. 1. From Data Quality to Clinical SafetyA year after the Award Tatiana Stebakova 29 March 2010
  2. 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 TerritoriesPage 1
  3. 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)Page 2
  4. 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 Click to add text 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 RoadmapPage 3
  5. 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 40Page 4 Search and match 27
  6. 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.Page 5
  7. 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.Page 6
  8. 8. Thank you and QuestionsPage 7