Systematic identification of rare disease patients in electronic health records enables evaluation of clinical outcomes.

ABSTRACT:

Background

Identifying rare disease (RD) patients in electronic health records (EHR) is challenging, as more than 10,000 rare diseases are not typically captured by clinical coding systems. This limits the assessment of clinical outcomes for RD patients. This study introduces a semiautomated approach to map RDs to appropriate codes, that is applicable across various EHR systems. By improving RD patient identification, this method facilitates the analysis of clinical outcomes and disease severity in the RD population. We exemplify this by utilizing large EHR datasets such as those in the National COVID Cohort Collaborative (N3C) with over 21 million patients.

Methods

We developed a semiautomated workflow to enumerate RD-specific SNOMED-CT and ICD-10 codes, starting with 12,00

SUBMITTER: Yadaw AS 

PROVIDER: S-EPMC12083593 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature