Improving Diagnostic Safety and Quality in Healthcare

Improving Diagnostic Safety and Quality in Healthcare is sponsored by National Institutes of Health (NIH). This program encourages research that addresses critical gaps and advances diagnostic excellence, including through artificial intelligence-enabled tools like large language models and neuroimaging. Doctoral research focusing on deep learning for improving diagnostic accuracy in medical imaging would be highly relevant.

Improving Diagnostic Safety and Quality in Healthcare | Grants & Funding U.S. Department of Health and Human Services National Institutes of Health Improving Diagnostic Safety and Quality in Healthcare When beginning your next investigator-initiated application, consider the following NIH highlighted topic. The area of science described below is of interest to the listed NIH Institutes, Centers, and Offices (ICOs).

This is not a notice of funding opportunity (NOFO). Apply through an appropriate NIH Parent Funding Announcement or another broad NIH opportunity available on Grants. gov .

Learn how to interpret and use Highlighted Topics . Expiration Date: July 28, 2028 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.

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 This topic is being issued as part of the Make America Healthy Again initiative, which is expanding NIH and agency research into specific areas.

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.

Central Scientific Contact: National Institute of Biomedical Imaging and Bioengineering (NIBIB) 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 National Cancer Institute (NCI) 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 National Institute on Drug Abuse (NIDA) 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 National Institute of Dental and Craniofacial Research (NIDCR) IC may give special consideration to support meritorious applications in this topic area.

Lorena Baccaglini, DDS, MS, PhD Office of Behavioral and Social Sciences Research (OBSSR) This office does not award grants. Applications must be relevant to the objectives of at least one of the participating Institutes or Centers listed in this topic. For technical issues E-mail OER Webmaster