Project ERDAM: Advancing Rare Disease Research Through Clinical Informatics

INTRODUCTORY OVERVIEW

Project ERDAM, the Enhanced Rare Disease Assessment Model, is organized around the central health informatics question: can existing health data be organized in a way that helps clinicians recognize rare disease patterns earlier, understand the diagnostic journey more clearly, and quantify the downstream burden on health systems?

This dissertation-style manuscript brings together three related articles as chapters. The first chapter focuses on developing an interpretable early-warning screening framework using ICD-10 encounter histories. The second chapter shows a methodology to evaluate mapping diagnostic pathways and proxy time-to-diagnosis in rare-like patients using structured EHR-style data. The third chapter provides a structure to quantify utilization and cost burden using a transparent claims-based rare disease proxy phenotype.

The work below should be read as a connected proof-of-concept framework rather than as a claim of final clinical deployment for Rare Disease patients. The goal is not to automatically diagnose rare disease. The goal is to show how routinely available coded data can support earlier clinical attention, clearer pathway review, and more systematic measurement of healthcare burden. This dissertation thoughtfully provides a model framework that informaticians can apply to any clinical system and develop studies to support Rare Disease populations. The end goal is to help create decision support tools for clinicians as they support rare disease patients and their families. Together, the chapters move from a showcasing how to identify a signal to developing a pathway to evaluating the burden of rare disease patients. This structure helps by identifying patients who may deserve earlier review, explaining the visible rare disease journey before recognition, and measuring the utilization and cost profile that can appear downstream for rare disease patients.

Read the Full Dissertation on ProQuest