Funding Opportunity Announcement (FOA) Number: RFA-TR-20-030
This FOA invites researchers to submit applications for support of clinical projects that address the critical need for timely identification and accurate diagnosis of rare disease patients. The Office of Rare Diseases Research (ORDR) within the National Center for Advancing Translational Sciences (NCATS) [along with the Institutes and Centers (ICs) listed in Part 1 at the National Institutes of Health (NIH)] intends to facilitate rare diseases research by enabling more rapid identification and diagnosis of patients, who may then be eligible for targeted interventions, research protocols (including clinical trials, natural history studies and registries, and epidemiologic studies), or disease-appropriate care and evaluation of their disease. Applications are being sought that propose diagnostic strategies that incorporate clinical consultation, machine-assistance and genomic analyses that could provide more rapid identification, escalation, and accurate diagnosis of hard-to-diagnose patients, and that could be readily integrated into front-line clinical care…
The objective of this FOA is to promote the planning and development of multi-disciplinary rare disease diagnostic strategies that will rapidly identify and escalate hard-to-diagnose or undiagnosed patients, and that must be applicable to a broad array of rare diseases. Diagnostic strategies must integrate machine-assistance strategies, rapid genomic analysis or interpretation of a laboratory testing panel, and clinical consultation within the project. Importantly, these strategies must be able to be adopted and performed at the primary or secondary care levels by front-line healthcare providers, and must be readily integrated into their clinical care workflow.
Examples of approaches that could be incorporated into a diagnostic strategy supported through this FOA include, but are not limited to, those listed below. The overall approach must include one strategy each for clinical, genomic analyses and machine-assistance.
Clinical strategies:
- Creation of a multidisciplinary expert diagnostic team
- Creation of a framework through which primary care providers can rapidly escalate hard-to-diagnose patients
- Machine-assistance
- Development of disease-agnostic algorithms to identify hard-to-diagnose patients through the EMR or other healthcare system databases
- Use of facial recognition or augmented reality software in the diagnostic process
- Development of a strategy to seamlessly integrate machine-assistance into the diagnostic process, such as through machine-alerts to clinicians
