Interoperability in healthcare includes both complete access to essential clinical and diagnostic data that is appropriately aggregated and normalized for meaningful use in clinical decision-making by physicians and providers, as well as healthcare analytics and predictive modelling programs used towards improving population health and disease management. 

by William Seay & Erik McHugh

Physicians, health systems, accountable care and health insurance organizations and other medical stakeholders are striving to access significant amounts of aggregated clinical information for analysis for care planning and management.  They need to share this data across the collective community of care and illuminate patterns for optimal analysis; blending biostatistics, bioinformatics, information technology, data analysis, and clinical research.  Actionable and complete clinical data has the potential to transform the process of clinical decision-making improve healthcare practices, reduce costs and ultimately improve population health overall. Further necessitating the need to access complete clinical data is the fact that reimbursements are being tied directly to the demonstration of quality care as measured by this data.  And what accounts for the bulk of this data?  It is laboratory, pathology and other diagnostic testing results.  It is believed that up to 70% of critical decisions in diagnosis and treatment involve quantifiable laboratory data.(1)  The hope for improved population health is in accessing the information from these growing data repositories—including lab and diagnostic test results—and analyzing them with intelligent software solutions to strengthen the evidence base across the healthcare spectrum.

In healthcare, lab and diagnostic data is an essential, significant component of the complete health information puzzle. Hospitals, health systems, patients, physicians, health insurers, laboratories, diagnostic imaging centers, health information exchanges and accountable care organizations all are impacted by the need for diagnostic test result data. This data is used throughout the care community to:

  • Analyze lab and diagnostic imaging data for early identification of disease
  • Identify predictive measures to identify those at risk for a disease
  • Diagnose and confirm disease identification, extent and type
  • Determine treatment plan based upon lab and diagnostic test results
  • Ascertain whether treatment is working using data for compliance surveillance
  • Make clinical decisions and determine treatment plan
White Paper Interoperability EMR Hub


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