�鶹��Ʒ��Ƶ

�鶹��Ʒ��ƵReceives $500,000 NIH Grant to Tackle Chronic Disease Disparities

One major recurring challenge faced by organizations that want to use their own institutional EHR data for research is establishing a suitable research environment in which the patient population can be profiled and research cohorts identified.

---

Chronic diseases such as diabetes, heart disease and cancer disproportionally affect racial and ethnic minorities. Of the 45 percent of Americans who have one or more chronic diseases, underserved populations are three to six times more likely than whites to have a chronic disease.

Researchers from �鶹��Ʒ��Ƶ’s , in collaboration with the , Inc., and the , have received a $500,000 grant from the National Institutes of Health (NIH) for a project to tackle chronic health disparities through the use of electronic health records (EHR), artificial intelligence, machine learning (AI/ML) and the Internet of Things (IoT).

The project, “Developing a Precise, Localized, Community Focused, Population Health Framework in an FQHC to Tackle Chronic Disease Disparities through EHR Data,” is part of the NIH’s “Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD).” This project is made possible by NIH Other Transaction Agreement Number 1OT2OD032581. 

AIM-AHEAD’s program goal is to establish mutually beneficial, coordinated and trusted partnerships to enhance participation and representation of researchers and communities currently underrepresented in the development and AI/ML models, and improve the capabilities of this emerging technology, beginning with the use of EHR and extending to other diverse data to address health disparities.

The AIM-AHEAD program consists of four cores — partnerships, research, infrastructure, and data science training—and this collaboration falls under the infrastructure core, which is headed by Nick Tsinoremas, Ph.D., vice provost for research, data and computing at the University of Miami and the founding director of its Institute for Data Science and Computing (IDSC), serving as principal investigator.

One major recurring challenge faced by organizations that want to use their own institutional EHR data for research is establishing a suitable research environment in which the patient population can be profiled and research cohorts identified. Addressing this challenge is the first requisite step to enabling community focused, EHR-based, research projects that aim to apply AI/ML methods or any other methods to these sets of data.

FAU’s Schmidt College of Medicine and its affiliated health clinics, together with the Caridad Health Center – Florida’s largest free health clinic established in 1989 – and the University of Miami, are developing this pilot program as a national model on how to implement AI/ML in community health centers and federally qualified health centers to improve their AI/ML delivery and research operations.

“Little has been done to actively incorporate data derived from electronic health records of federally qualified health centers and community centers that directly serve underrepresented and disadvantaged groups burdened by health disparities,” said , Ph.D., principal investigator, chair of the , senior associate dean for research, and a professor of biomedical science, �鶹��Ʒ��ƵSchmidt College of Medicine. “Although these centers serve as the primary source of medical care for communities affected by health disparities, they unfortunately lack adequate data, artificial intelligence and machine-learning capabilities needed to collect, collate and analyze substantial amounts of patient data.”

The project is spearheaded by Robishaw; Laura Kallus, chief executive officer of Caridad Center, Inc.; and Azizi Seixas, Ph.D., associate professor in the Department of Psychiatry and Behavioral Sciences at the Miller School of Medicine and director of the Population Health Informatics Program at IDSC.

With this grant, researchers will tackle these health disparity challenges by implementing a research tool developed by the University of Miami with funding from the NIH’s Clinical and Translational Science Award Program. The University Research Informatics Data Environment, also known as URIDE, is a web-based platform that aggregates and visualizes de-identified data from multiple clinical health systems within the organization. URIDE enables clinical research investigators and their teams to easily explore demographics, diagnoses, procedures, vitals, medications, labs, notes, allergies, comorbidities and other information.

URIDE previously received funding from the NIH Clinical and Translational Science Award program. This new pilot program will increase URIDE’s utilization with a much wider expansion of the use of this cyber infrastructure platform.

“We are very excited to collaborate with �鶹��Ʒ��Ƶand Caridad to expand the URIDE platform, creating a more representative community with this cutting-edge, health informatics tool,” said Tsinoremas.

The project team will establish a research environment to support the identification of research cohorts. Using URIDE, and with the incorporation of AI/ML and IoT, they will be able to conduct remote health monitoring. Patients with cardiometabolic health conditions such as high blood pressure and diabetes will be monitored remotely, which will enable the Caridad Center to implement a tailored AI/ML query and analytical platform in their EHR and conduct personalized queries in their research questions to address chronic disease within their patient population.

“As a medical school of the community and for the community, we are very excited to collaborate with the Caridad Center and the University of Miami to bring together experts and resources to advance the goals of the National Institutes of Health’s AIM-AHEAD program,” said , M.D., Ph.D., dean and vice president of medical affairs, Schmidt College of Medicine. “AI and machine learning are powerful tools that will help us to optimize health care delivery and drive health care innovation.” 

-FAU-

Tags: medicine | faculty and staff | technology | research