Improving Diagnostic Safety and Quality in Healthcare
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Topic Description
Post Date: July 28, 2026
Expiration Date: July 28, 2028
Background
There are several critical gaps in the current healthcare system that impede diagnostic safety and quality. A dedicated research effort aimed at improving medical diagnosis, particularly diagnostic failures that contribute to patient harm will help to close these gaps.
Purpose
The purpose of this topic is to encourage research that addresses critical gaps and advances diagnostic excellence across healthcare settings. Three overarching high‑priority strategic research areas are prioritized as follows:
Technology and Innovation
Advancing diagnostic safety and quality through the innovation and development of diagnostic technologies, including:
- Artificial intelligence–enabled tools, such as large language models
- Telehealth and other digital health solutions
- Point of care diagnostic testing
- Enhanced data sources, sensors, and analytic methods
- Other novel diagnostic tests, tools, or measurement approaches
Infrastructure and System Design
Strengthening diagnostic performance by improving system-level infrastructure, including:
- Coordination of care across the full diagnostic trajectory
- Integration of diagnostic processes, clinical tools, and patient-reported information
- Improved communication among patients, clinicians, and care teams
Advancing the Science of Diagnosis
Enhancing the evidence base and scientific foundations of diagnosis by:
- Improving uptake of clinical guidelines and follow-up of abnormal diagnostic results
- Expanding high-quality evidence, particularly from real-world data sources
- Modernizing scientific approaches to disease recognition and diagnostic reasoning
It aligns with:
- NIH MAHA Chronic Disease Initiative : The NIH will launch a new Whole-Person-Health approach to chronic disease prevention research and leverage collective expertise across the agency to catalyze transformative discovery science and intervention strategies that promote wellness, resilience, and optimal health, including metabolic health, at all stages of life.
- Artificial Intelligence : HHS, NIH, and the Office of Science and Technology Policy will develop an evidenced-based and AI-driven approach to harnessing the data and technology available to transform research and clinical trials on pediatric cancer. This can be a model for future research in other critical areas.
Participating ICOs
The primary focus of the NIBIB interest is technological innovations with the purpose of directly improving diagnostic safety and quality. Research aligned with other strategic areas might also be considered, provided the proposed work is focused on developing innovative technologies with broad applicability. Examples include but are not limited to:
- AI technology to improve diagnostic quality of Point-of-Care imaging
- Targeted motion correction technology to improve diagnostic radiological imaging safety and quality
- Novel phantom technology (e.g. 3D printing) to improve diagnostic safety and quality
- AI tools for surveillance of diagnostic errors and misses
- Workflow management or reporting platform to reduce diagnostic delays, errors, and improve quality and timeliness
- Advanced visualization tools (e.g. Virtual Reality/Augmented Reality) to improve detection and diagnosis
Qi Duan
[email protected]
A cancer diagnosis includes ascertainment of the subtype and stage of cancer. The cancer diagnostic process involves several clinicians and a variety of test methods. NCI supports research to improve the safety and quality of the cancer diagnostic process. Examples include (but are not limited to):
- Use of new technology, including AI, to improve the accuracy and timeliness of cancer diagnosis, including diagnosis of rare and early-onset cancers
- Tools to improve communication and coordination among clinicians involved in the cancer diagnostic process
- Approaches to improve timely and accurate interpretation of test results
- Tools to improve communication of test results between clinicians and patients and their caregivers
- Approaches to decrease the time for follow-up testing after abnormal test results, including abnormal screening test results
- Approaches to reduce the logistical and cognitive burden of the cancer diagnostic process
Gurvaneet Randhawa, MD, MPH
[email protected]
NIDA supports research that improves diagnostic safety and quality for people who use drugs and those with or at risk for substance use disorder (SUD). Areas of interest include:
- Applying AI, machine learning, digital health, telehealth, and real-world data to enhance detection of substance use, SUD progression, and comorbidities (e.g., HIV, HCV)
- Advancing neuroscience-informed diagnostics (e.g., neuroimaging, physiological measures, digital biomarkers, molecular signatures)
- Developing and implementing scalable, user-centered workflows that enhance efficiencies addressing service needs across the full prevention-treatment-recovery services spectrum within and across settings, ranging from identification of SUD and comorbidities to referral and linkage to care, to actionable feedback loops
Candace Webb, MPH, MCHES
[email protected]
Lorena Baccaglini, DDS, MS, PhD
[email protected]
Emily Evans, Ph.D.
[email protected]
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