1. Content Interoperability: Achieving Clinical Data Quality Success Lin Wan, Ph.D., Chief Technology Officer Stella Technology

2. Learning Objectives Recognize the importance of having “meaningful” clinical data Learn how to efficiently identify data quality issues Learn how to overcome the clinical data quality challenge

3. Data Quality Challenges National efforts around interoperability have been focused on messaging and transport Getting meaningful, reliable, high quality data from disparate source systems for Health Information Exchange (HIE), population health management, quality measures or any analytics-related needs has been a pain point Vendors don’t implement standards the same way The standards are not clear Users do not enter values consistently Hard to evaluate and analyze the clinical data

4. iQHD – A flexible and configurable web tool that: Evaluates clinical data against published and custom data standards and rules (e.g., CCDA, C32) Provides reports on the “quality” of clinical data from its sources by scoring each result

5. Use Case / Purpose of Use Determine clinical data readiness for data for HIE on-boarding, quality measures, analytics, etc. Understand quality of data sent from various sources Quickly and efficiently identify data quality issues Saves a LOT of administrative and clean-up time for both sending and receiving organizations

6. Success Story: Western New York Clinical Information Exchange With iQHD, HEALTHeLINK is able to: Analyze the content of the information flowing through its clinical network Qualify the data being sent to HEALTHeLINK and provide a grade to determine the value of using that data for specific population health analysis Understand quality characteristics from different data sources

7. Western New York Clinical Information Exchange – cont’d Produce reports with the exact description of what and where the errors are in the document Identify and work with data source vendors or stakeholders to correct any issues – optimizing the data collection process

8. Increased Levels of Data Quality Completeness Standardization Validity Uniqueness Consistency Syntax/Format Conformance Accuracy Better Quality Lower Quality

9. Data Quality Levels 1.The syntax layer checks the data's structure and format conformance against standard specifications (e.g. HL7 v2.5, C32 CCD, etc.) 2.The completeness layer checks for the presence of required data elements, e.g. names, addresses, vital signs, etc. 3.The standardization/semantic interoperability layer ensures that the data has been coded/standardized properly, e.g. local labs codes vs. LOINC, standardized postal codes, gender codes, etc.

10. Data Quality Levels 4.Validity checks verify whether data provided are within allowable value set 5. The next layer validates the data's uniqueness – duplicate data are identified 6. The consistency layer identifies contradictions within the data set 7. Lastly, the most advanced layer, accuracy, assesses how a given data set measures against expected results (based on analysis and understanding of other or previously collected data, control data samples,...)

11. Questions/Discussion For more information, please visit: Stella Technology www.stellatechnology.com Contact: Lin Wan, Ph.D., Chief Technology Officer Stella Technology lin@stellatechnology.com

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