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Digital Data Stewardship™ for Trusted, High-Quality Data Exchange

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Real world use cases for exchanging patient data continue to grow, and organizations face a very real challenge ensuring that the data they receive and send is consistent, accurate, and usable. Use cases vary widely and typically include the following:

  • Receiving multi-source data from across health information networks and grappling with the variation in formats and content
  • Merging and de-duplicating provider, payer, and research data to form a comprehensive view of a patient’s care
  • Uplifting legacy data for use in current use cases and exchange formats
  • Normalizing and formatting data for specialized uses such as public health surveillance, quality measure reporting, or providing directly to the patient

Ensuring high quality data exchange across these use cases requires diligent validation and a multi-pronged strategy tor ingesting, normalizing, de-duplicating, and formatting data. Existing solutions to automate data quality workflow often do not address end-to-end management and full-spectrum data quality uplift.


Carol Graham, MS, RN

Carol Graham is a health informatics professional and registered nurse (RN) with a passion for improving healthcare through technology innovations. She has over twenty years of experience in the IT, healthcare, life science, and pharmaceutical domains, including end-to-end software development and major project experience in healthcare interoperability, healthcare data quality, clinical workflow, data modeling, and decision support solutions.

Carol is the Product Manager for Pivot™, Nomentys™, and Symedical on FHIR™ offerings at Clinical Architecture, where she leads a multi-disciplinary team in defining, building, and launching healthcare interoperability and data quality solutions. In that role, she also serves as a clinical architect and subject matter expert in interoperability, frequently consulting with customers, partners, and industry stakeholders in the use of Clinical Architecture solutions to solve complex interoperability and data quality problems.