Watch the Novel Cohorts podcast with special guest Melissa Haendel Director of Precision Health & Translational Informatics at University of North Carolina
In this podcast episode, Dan and Melissa dive into the complexities of rare disease research and the need for better data infrastructure to support diagnosis and care. The conversation highlights the importance of collaboration, infrastructure, and standardized data to improve early diagnosis and care for rare disease populations. Some key takeaways from the discussion are:
- Rare disease research is challenged by fragmented, unstructured data and the lack of consistent diagnosis codes, making it hard to identify and support patients
- EHRs often fail to capture the nuanced biological characteristics of rare diseases; and integrating clinical & biological datasets is essential to improve diagnostic accuracy
- Government-backed efforts such as ARPA-H are investing in shared infrastructure and national-scale modeling to enable earlier and more accurate rare disease diagnoses
- Progress in rare disease research depends on coordinated efforts among patients, academic institutions, and data-sharing networks like eHealth Exchange
- Artificial intelligence and machine learning are key to structuring unstructured clinical notes, enhancing decision support, and identifying new diagnostic opportunities