Content Variance Analysis Project

In 2019, the Department of Veterans Affairs (VA) wanted to correct certain data variations in accordance with the Electronic Health Records Modernization (EHRM) requirements for legacy VistA, Cerner Millennium and HealtheIntent. This project examined data in non-standard domains in legacy systems and other data sources to check for consistency of structure, content and format in preparation for the import of the data into Cerner Millennium. For clinical terminology sustainment purposes, we performed an analysis on the contents of the VistA legacy data files. The various types of inconsistencies were identified, such as duplicate spellings for the same word, and standardized. Some data fields whose purpose had changed over the decades were brought up to date. (TO26)

We also supported the HL7 Patient Care Workgroup which continues to work on several projects:

  • Adverse Events. PC works with Biomedical Research and Regulation (BRR) to bring the immature Adverse Event resource to maturity. Work on this domain continues to focus on addressing FHIR trackers, building up the model incrementally, without a commonly agreed description of the domain.
  • Care Plan Domain Analysis Model. This effort aims to update the initial model published in 2016. Challenges include the size and complexity of the domain, and the divergence between conceptions of the plan as a forward-looking instrument, a care coordination tool, a filtered view of the patient record, and a set of automated processes for supporting these conceptions.
  • Gravity. This is a coordinated effort on the part of many organizations (including the University of California San Francisco and the Robert Wood Johnson Foundation) to mature support for social determinants of health by documenting use cases, identifying useful data elements, and developing FHIR guidance for supporting these cases and elements.
  • Proposed solution for clinical status harmonization across C-CDA and FHIR and focused on divergence with International Patient Summary (IPS).

We also produced white papers on "A Risk-Based Methodology for the Independent Validation and Verification of Healthcare Knowledge." Sharing clinical knowledge and person-specific information is an imperative for government agencies such as Veterans Health Administration (VHA) and Military Health Systems (MHS). Clinical Decision Support (CDS), is a process for enhancing health-related decisions and actions with pertinent clinical knowledge has been an important capability within health information technology systems.  If we are to realize the bold evolution of interoperable CDS predicted for the next twenty-five years, there needs to be new tools continuously managed clinical knowledge, with critical attention paid to ensuring that informatics methods assess the quality of knowledge in decision making.

Today, organizations like NASA and the US Department of Defense (DOD) make heavy use of Independent Verification and Validation (IV&V) techniques to improve the quality of systems and reduce the risks associated with the deployment of those systems. As the US Department of Veterans Administration (VA) moves to the outsourcing of their Electronic Health Record modernization (EHRM), IV&V will become increasingly valuable to VA as part of their project activities.  As a result, there is a critical need for cost effective IV&V. The domain area of health informatics plays a vital role in validating and verifying the quality of the cognitive, information processing, and communication tasks provided for medical practice, education, and research, including the information science and the technology to support these tasks. Health Informatics provides the perspective for medical education, research, and interoperability, and Enterprise Architecture (EA) provides the business, technology, and governance perspectives to help guide implementation, planning, and modernization. An Independent Verification & Validation (IV&V) delivers the objective evidence that assesses how modernization will improve veteran care through information, but also addresses business and organizational issues.

For example, laboratory systems can benefit greatly from data aggregation. This enables sharing of lab results with internal and external partners.