Governments are overwhelmed balancing consumer expectations, aging workforce, regulations, rapid technology change and fiscal deficits. This blog gathers a community of SMEs who discuss trends and outline how public sector organizations can leverage relevant best practices to drive their software-led transformation and build the future of technology – today!

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August 26, 2019

Seamless healthcare data Interchange - are we there yet?

Healthcare data interchange has evolved with time and kept pace with changing technologies. Standardized data exchange frameworks as well as legislation have shaped this evolution. The focus had been on how to exchange healthcare data, but for individual organizations, be it government agencies, healthcare providers, pharmaceutical entities, or insurance, compliance was limited to external information exchange driven by HIPAA and Meaningful Use guidelines.

Healthcare data management within an organization presents significant challenges in reconciling semantic interoperability with the internal data management systems. The most common issues include;

  • Interface and access to legacy data sources
  • Conversion of standardized EMR/EHR data in HL7/FHIR to legacy records and vice versa for bi-directional data interchange
  • Consolidation of data to create a longitudinal health record in a standardized format from disparate sources and business lines
  • Lack of a healthcare/clinical data source of record
  • Code sets, clinical terminologies and classification systems must be mapped, based both on the content and the version of the standard (e.g., ICD, CPT, etc.).
  • Managing structured and unstructured data
  • Inconsistencies in the interpretation of the data 
  • To remediate these gaps, organizations are using multiple, generally disconnected, interventions like adding a data element or a new table. This resolves issues over the short term, but create a larger issue where there is no single source of record for healthcare and clinical data.  

Issues like those outlined above need to be resolved in two stages.

Stage I is the Data transformation layer which resides between the interoperability layer and the data sources.  In this layer, data is interpreted, business logic is incorporated to handle non-standard data formats, and data is mapped according to the organization's data standards.

Stage II is the Clinical Data Repository (CDR) which will be a source of record for all healthcare/clinical records for the entire organization.  This would lead to true semantic interoperability within the organization and will provide for accurate information exchange between entities in a structured manner.

Data transformation is not the same as the interoperability/interface layer which interacts with external sources of information. Experience shows that whenever an organization has to manage new source of data, a manual intervention is required to determine how to store the data in the system. This is tedious and difficult to monitor as coding standards and styles vary.  The Data Transformation layer will solve this issue and bring organizations a step closer to semantic interoperability.

If you are attending ISM 2019 conference, drop by our booth 431 to see our solutions that are solving the semantic interoperability issues, and enabling agencies to reduce the effort required to standardize data and generate actionable insights.

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