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Operationalizing the DataGauge Framework in a Health Information Exchange Utilizing Hepatitis C Data

Author: Edward Yao, MS (2023)

Primary advisor: Angela Ross, DNP

Committee members: Debora Simmons, PhD and Tiffany Champagne-Langabeer, PhD

DHI Translational Project, McWilliams School of Biomedical Informatics at UTHealth Houston


This project aims to implement the DataGauge framework in a health information exchange (HIE) setting as a proof of concept. The modified DataGauge framework, described by DiazGarelli et al. (2019), is utilized to test its functionality and applicability with any dataset. The specific objective of the project is to determine the number of hepatitis C-positive tests within the HIE. The implementation involved multiple iterations following the DataGauge framework's steps for data extraction and analysis. Five iterations were conducted, resulting in both successful and failed queries based on the validity of the data standards. The findings revealed that the HIE, in this case, did not have complete access to the clinical data required to answer the initial question about the number of hepatitis C-positive patients; rather, the HIE only received information from patients who consented to share their health data and were approved by their physicians. To address this limitation, a recommendation is proposed based on Guerrero et al.'s (2019) workflow. The recommendation suggests granting an intermediary actor (referred to as the analyst) access to all clinic data, regardless of patient consent status. The analyst would then gather and deidentify the relevant clinical data, with explicit permission from the clinic, and provide it to the Rio Grande Valley HIE (RGV HIE). This approach would enable the RGV HIE to legally access non-participant data through deidentified datasets or aggregated count/sum data, while ensuring compliance and collaboration. By implementing this recommended process, the RGV HIE can enhance its preparedness for future clinical questions, grants, partnerships, and public health emergencies. Moreover, this model can be applied to other data warehouses and HIEs nationwide.