Multidisciplinary Machine-Assisted, Genomic Analysis and Clinical Approaches to Shortening the Rare Diseases Diagnostic Odyssey

The Need for New Approaches to Diagnose Rare Diseases More Quickly

For many people with rare diseases, getting a correct diagnosis can take years and lots of visits to different doctors. During this “diagnostic odyssey,” people with rare diseases might have unnecessary tests and procedures, receive the wrong diagnosis, and experience delays in getting effective care. This long timeline means that many people with rare diseases experience irreversible damage as the disease progresses. Some may miss points in time when interventions could help.

To improve the lives of people with rare diseases, NCATS wants to find innovative ways to shorten the timeline for getting a correct diagnosis. To do this, it has awarded funding to support three research projects that will study new tools and approaches to make it easier to correctly diagnosis people with rare diseases.

About the Research

Each of the three research projects is exploring a different approach to speed up the timeline for a correct diagnosis. These approaches include machine learning, genetic analyses and medical evaluation. To be successful, the approaches must be easy to apply and used early in patient care by front-line health care providers.

Funding for the awards comes in two phases. Research teams must meet goals in the first phase before they move on to the next. In the first phase, researchers must develop a strategy for using their proposed approach to make faster diagnoses and then test that strategy in a real-world setting. In the second phase, researchers will test their strategy with patients in a different health care setting that presents new challenges or obstacles.

For more information, contact Alice Chen Grady, M.D. or Eric W.K. Sid, M.D., M.H.A.

Multidisciplinary Machine-Assisted, Genomic Analysis and Clinical Approaches to Shortening the Rare Diseases Diagnostic Odyssey (UG3/UH3 Clinical Trial Optional)

Principal Investigator(s)

Year Awarded



Gelb, Bruce D.; Chen, Rong; Balwani, Manisha


Icahn School of Medicine at Mount Sinai

Using Electronic Medical Record Data to Shorten Diagnostic Odysseys for Rare Genetic Disorders in Children and Adults in Two New York City Health Care Settings

Gropman, Andrea Lynne; Berger, Seth I.; Vilain, Eric J.


Children’s Research Institute

Machine-Assisted Interdisciplinary Approach for Early Clinical Evaluation of Neurodevelopmental Disorders

Lalani, Seema R.; Lee, Brendan


Baylor College of Medicine

Virtual Platforms for Genetics Evaluation in the Medically Underserved

Visit our rare diseases web page to find information about other rare diseases research.