About the Research Fellowship

The AIM-AHEAD Research Fellows Program provides early-career researchers (graduate students, postdocs, junior faculty, and others conducting research outside of an academic institution, within 10 years of completing their highest degree) the opportunity to receive funding and support for research on novel and innovative data science and data-focused research problems; specifically, those focused around health disparities and inequities. The guiding principles for project selection will include the consideration of the applicant’s career goals, the potential for future productivity, and the alignment of the proposed research with the overarching goals of the AIM-AHEAD initiative. Fellows will work in collaboration with the AIM-AHEAD Coordinating Center, a nationwide consortium establishing mutually beneficial, coordinated, and trusted partnerships between institutions and communities promote broate participation in the development of Artificial Intelligence and Machine Learning (AI/ML) models.

AIM-AHEAD Research Fellowship Program will engage a group of early-career researchers to actively participate in biomedical research that involves the use of AI/ML methodologies on biomedical research data, clinical and genomic cohorts to address health disparities and inequities, including bias in AI/ML algorithms or data.

The program is open to individuals conducting research at the intersection of healthcare practice and delivery, AI/ML methods, and the advancement of health equity. The AIM-AHEAD Consortium From institutions and their investigators from a variety of fields of study, institutions, and geography. 

Consistent with applicable law, the AIM-AHEAD Research Fellows Program will not use the race, ethnicity, or sex (including gender identity, sexual orientation, or transgender status) of prospective program participants or faculty as an eligibility or selection criteria.

The Research Fellows will be able to choose from a variety of available datasets to study their research question. Fellows are limited to choosing one primary data source on which to focus their research and an optional secondary one if justified. Each data source has corresponding infrastructure and training available to support the fellows throughout the fellowship program. Available data sources are described below.

The research project needs to focus on AIM-AHEAD North Star (III): Use AI/ML to address disparities and minority health in behavioral health, cardiometabolic health, and cancer.

As the AIM-AHEAD consortium develops, more data, infrastructure, and training will be made available to the fellows and to the broader community. 

Research Fellows Will:

• Be our collaborators. Together, we will test and expand the AIM-AHEAD ecosystem with new tools, data, resources, and capabilities. The Fellows will provide insight and guidance as we build and refine the AIM-AHEAD program. 
• Be members of the AIM-AHEAD consortium.
• Be the first users of the ecosystem, chosen in part because they have projects that can benefit from, and be of benefit to, AIM-AHEAD.
• Access training, AI/ML expertise, data, tools, and resources to conduct the research fellowship research.
• Access institutional support for obtaining IRB approvals and signing data use agreements to conduct research.
• Dedicate sufficient time to carry out their projects, accelerating their research productivity.
• Access data (see options below and requirements for data access).
• Access AI/ML scalable cloud infrastructure (multiple options available).
• Participate in selected AIM-AHEAD consortium, core, and workgroup meetings.
• Receive training in ethics and equity issues in AI/ML, as well as gain exposure to methodologies and software to detect and mitigate algorithmic bias.
• Publish their results in peer-reviewed journals and at the AIM-AHEAD annual meeting in accordance with AIM-AHEAD publication policy.

Dataset Accessible

Fellows will be able to pick one primary data source to use for their project. A secondary data source can be selected if relevant to the research project. A justification is required. Patient-level data from the various datasets cannot be combined - no exceptions. Each data source will have a specific infrastructure that must be utilized for the project, and selecting a secondary data source may mean working on multiple infrastructure platforms.

• For example, if a research fellow selects OCHIN as their primary source of data and a curated study from Data Bridge as their secondary source, they will be expected to perform their analyses on the AIM-AHEAD Service Workbench for the OCHIN data and AIM-AHEAD Data Bridge Virtual Machine for their secondary analysis. 

Infrastructure

Infrastructure of 1) OCHIN and 2) AWS Open Datasets will use AIM-AHEAD Service Workbench (SWB). SWB  is a tool for any cloud computing data analysis that will be conducted throughout this fellowship. More information is available here.

Applicants may want to explore the OCHIN Community Health Equity Database for feasibility assessment as they write their proposals. OCHIN provides the means to do so through the use of Cohort Discovery, a web-based software tool for obtaining counts of patients matching user-specified inclusion/exclusion criteria. To gain access to Cohort Discovery, AIM-AHEAD program applicants must have completed and be up to date with standard training in Human Subjects Research and Responsible Conduct of Research such as those offered by the CITI Program. To request access to OCHIN’s Cohort Discovery, AIM-AHEAD program applicants can complete the “i2b2 End User Agreement.”

Infrastructure for AIM-AHEAD Data Bridge will connect to a Windows Virtual Machine (VM). 

Applicants whose research question can be addressed with AIM-AHEAD Data Bridge (AADB) data may choose to schedule a pre-award data consultation with AADB to discuss their research question with the AADB team and methodologist. The AADB can perform a preliminary cohort discovery to provide estimated sample sizes and confirm the availability of relevant variables for custom dataset requests. 

Available EHR data include (but are not limited to): demographics, problems list, surgical history, observations, medical history, procedures, prescriptions, billing, provider, clinical notes, clinical unit/department, allergies, images, social history, diagnoses, admissions, encounters, social history, environmental and social determinants of health, device implants, images.

Infrastructure for BioData Catalyst will use the NHLBI BioData Catalyst  PIC-SURE and Seven Bridges Platforms. An NIH eRA Commons ID is mandatory in order to access these resources.

Infrastructure for All of Us Research Program will use the All of Us Workbench, a cloud-based platform where registered researchers can access Registered and Controlled Tier data.

Infrastructure for ScHARe Data Ecoysystem will use the ScHARe Collaborative Workspace by Terra, an open-source data analysis platform based on Google Cloud Platform.