Assessing the Effectiveness and Scalability of Fast Healthcare Interoperability Resource-Based Granular Data Segmentation Technology
January 1, 2026·
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0 min read
Soroush Dianaty
Abstract
Evaluates SHARES, a FHIR-based granular data segmentation platform for protecting sensitive substance-use health information. Tested against 11,519 synthetic patient records, the system achieved a throughput of 10.36 bundles per second at a cost of under one cent per segmentation.
Type
Publication
Applied Clinical Informatics
Authors
Physician-Scientist & PhD Student | Biomedical Informatics & Data Science
Physician and Biomedical Informatics Ph.D. researcher at Arizona State University advancing computational cardiology, health wearables, and digital health systems. Combines clinical training and frontline primary care experience with machine learning, wearable biosignals, and cardiovascular physiological modeling. Research centers on anchoring continuous wearable sensor streams and clinical data in cardiovascular mechanics and physiological rationale, with a focus on cardio-oncology and evidence-grounded health data interoperability. Bridges the gap between physiological modeling and real-world clinical practice.