Data Adjudication Architecture for Health Information Exchange (HIE): a Case of Adjudicating and Storing Hemoglobin A1C Values

Discussion • The proposed architecture and data flow are designed to transform raw HIE data into trustworthy data and improve data integrity by examining data quality attributes of physiological compatibility, completeness, and redundancy. • By filtering out previously processed data and retaining only new HIE data, the architecture eliminates duplicate processing of discrete data from incoming documents, thereby improving efficiency of data adjudication. • Data adjudication reduces the burden of manual resolution. Data that may require manual review is sent for resolution using HIE workflows. • Flood of data from HIE may overwhelm EHR systems and may not support optimal care. The proposed architecture suggests an approach to integrate HIE data focusing on episodic care details into a patient's longitudinal data record. • Implement the CDS Data Adjudication rules for all categories of interest for HIE data. • Analyze effects on quality and cost of care. • Hemoglobin A1C (HgbA1C) result records (n = 2.2 million) were categorically analyzed. • Data sources include Intermountain and non-Intermountain laboratories as well as manually entered point-of-care laboratory results.