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Issued by

Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) program

 

 

Overview

The 2024 Call for Proposals (CFP) of the AIM-AHEAD Consortium Development Program (CDP) focuses on Innovation for Equity in Low-Resource Settings and provides funding to catalyze multi-disciplinary research projects to plan and pilot Artificial Intelligence/Machine Learning (AI/ML) algorithms or tools to address health disparities and minority health in cancer, cardiometabolic and mental/behavioral health. The projects must engage healthcare organizations serving patients in Federally Qualified Health Centers (FQHCs) or Community Health Centers (CHCs) from Medically Underserved Areas (MUAs) as defined by Health Resources and Services Administration (HRSA) and/or serving a disproportionately high percentage(s) of NIH-designated health disparity populations (as defined below) to design and pilot AI/ML-aided tools, models or interventions. As such, a community-engaged design phase grounded in AIM-AHEAD Ethics and Equity Principles is required for all funded projects, with ongoing engagement and bidirectional communication between AI and ML developers and communities (e.g., health care professionals, patient representatives) throughout the project. Innovation for Equity is responding to the challenge that medically underserved areas are not benefiting from advances in AI/ML at the same pace as higher-resource settings, and many AI/ML models are not informed by the data, needs, and perspectives of communities impacted by health disparities.

 

About AIM-AHEAD

The AIM-AHEAD Coordinating Center (A-CC) was established to enhance diversity in the field of artificial intelligence and machine learning, with an emphasis on reducing health disparities and promoting health equity. To achieve this objective, A-CC is engaged in a fair, equitable, and transparent process of building a consortium of AI/ML to promote health equity and develop an inclusive and diverse workforce. Many communities have untapped potential to contribute new expertise, data, recruitment strategies, and cutting-edge science to the AI/ML field. A-CC seeks to increase participation and engagement through mutually beneficial partnerships, stakeholder engagement, and outreach to advance health equity.

AIM-AHEAD consists of its Leadership Core made up of Regional Hubs and the following Cores:

 

• • Data Science Training Core (DSTC)
  • Data and Research Core (DRC),
  • Infrastructure Core (IC) and its Applied Ethical AI (AEAI) Subcore

 

Details on AIM-AHEAD resources are in Appendix A.

 

Purpose/Objectives

The primary objective of this solicitation is to support the development of multidisciplinary research pilot projects that will use AI/ML with novel algorithms or approaches with potential to significantly impact healthcare access, utilization, healthcare quality, value, and health outcomes for populations that experience health disparities in cancer, cardiometabolic and behavioral/mental health. Applications that do not include FQHCs and/or Community Health Centers serving MUAs and/or serving health disparity populations as partners will be considered non-responsive to the Call for Proposals. Teams selected for funding by the AIM-AHEAD Innovation for Equity in Low Resource Settings Program will receive a first year of funding to form collaborative, equal partnerships with FQHCs or CHCs in MUAs and/or serving health disparity populations. The project teams will design, plan, and conduct a pilot project that will meet each of the following requirements:

• • Engage health care settings and patient representatives in planning AI/ML development, implementation and assessment based on AIM-AHEAD Ethics and Equity Principles.
  • Demonstrate feasibility of translating AI/ML tools or algorithms into clinical or operational practice with potential to improve equity in the AIM-AHEAD North Star III health categories (cancer, cardiometabolic and behavioral/mental health).
  • Transfer knowledge and capacity between commercial or high-resource academic institutions and lower-resource organizations in FQHCs, MUAs and/or serving disproportionately high percentages of health disparity patients.
  • Augment the shared learnings of the AIM-AHEAD Consortium through the engagement of diverse, interdisciplinary project teams committed to tangible deliverables to advance health equity and minority health.

 

At the end of the Period of Performance, funded project teams will be required to deliver a detailed plan for how the piloted innovations will be scaled into future interventional studies and/or projects to build AI/ML capacity in the partner institution(s) and similar low-resource settings/communities.

In summary, this CFP seeks to catalyze partnerships into projects that will transfer knowledge and AI/ML capabilities with institutions or medical care communities serving populations that disproportionately experience health disparities and barriers to care. The program seeks applications that go beyond data-only studies by co-designing and piloting AI/ML interventions in partnership with impacted communities. Applications should demonstrate potential to reduce health disparities while improving healthcare and outcomes in alignment with the AIM-AHEAD North Star (III): Use AI/ML to address disparities and minority health in behavioral health, cardiometabolic health, and cancer.

 

Project Guidance

AIM-AHEAD envisions that research projects will:

• • Use diverse population data to generate AI/ML with enhanced potential to lessen health disparities.
  • Foster understanding of how to detect and mitigate bias in AI/ML models to avoid perpetuating or exacerbating and instead reduce health disparities.
  • Improve health care operations or health outcomes related to prevention, diagnosis, treatment, intervention, or implementation strategies in alignment with the North Stars of the Program.
  • Bring together a broad consortium of institutions and organizations, including non-traditional industry and non-profit organizations as part of AIM-AHEAD AI/ML research enterprise.

 

Applications responding to this solicitation should propose novel, explainable AI/ML approaches in care settings that otherwise will be “left behind” in AI/ML development and implementation. Applicants are encouraged to propose projects that consider ethical issues and potential unintended and/or adverse outcomes of bias in applying AI/ML approaches to improving health outcomes. Examples of the types of research projects that will emerge through this opportunity include but are not limited to:

 

Ethics and bias detection in AI/ML

• • Address ethical, legal, and social implications, including regulatory and oversight aspects, of AI/ML.
  • Evaluate the results of an increase in the demographic/geographic diversity of population data used to train, validate, or calibrate algorithms to help detect and mitigate bias.

 

Data use in AI/ML

• • Enhance AI/ML tools to improve care for disparity populations by using more diverse population datasets to train and/or evaluate the impact of algorithms.
  • Elucidate the role and impact of Social Determinants of Health (SDOH) in development of representative predictive AI/ML models.
  • Assess the validity of metrics and statistical methods used to measure health disparities and health inequities using AI/ML approaches.
  • Determine whether AI/ML models that use EHR, SDOH, and other data predict risk for adverse health outcomes in cancer, cardiometabolic, or behavioral health differently across NIH-designated health disparity populations.

 

Clinical/operational application of AI/ML in low-resource settings

• • Identify appropriate uses for models owned by industry to facilitate innovative applications in low-resource settings of care.
  • Conduct implementation science of AI/ML applications in FQHCs, MUAs, and/or health disparity population-serving institutions to identify patient community and/or health care workforce needs to achieve successful results.
  • Identify safety events in the electronic health record (EHR) and potential bias of safety events due to incomplete medical records, fragmentation of care, or bias in healthcare.

• • Determine the effectiveness of AI/ML models that use EHR, SDOH, and other data that identify patients at heightened risk for low or high service utilization and who may need additional health service support.

 

Novel AI/ML tools

• • Evaluate practical use cases (e.g., large language models) in under-resourced settings of care.

• • Apply Natural Language Processing (NLP) to identify social needs, documented SDOH, family/patient engagement, and social support available.

 

The NIH defines health disparities as differences in the incidence, prevalence, morbidity, mortality, and burden of diseases and other adverse health outcomes among specific population groups. Applications responding to this call should focus on one or more NIH-designated health disparity populations in the United States. These population groups include racial and ethnic minorities (African Americans, American Indians, Alaska Natives, Asian Americans, Hispanic Americans, Native Hawaiians, and other U.S. Pacific Islanders, as well as subpopulations of all of these racial/ethnic groups), socioeconomically disadvantaged individuals, sexual and gender minorities, and medically underserved populations including individuals residing in rural and urban areas (see https://www.nimhd.nih.gov/about/strategic-plan/nih-strategic-plan-definitions-and-parameters.html). Research projects that include populations that identify across more than one population with health disparities are encouraged.

 

Proposals to this program must include a plan for ensuring work is guided by a concern for human and social impact and attention to ethical, legal, and socio-economic implications of AI/ML, including but not limited to (1) biases in datasets, algorithms, and applications; (2) issues related to identifiability and privacy; (3) impacts on disadvantaged or marginalized groups; (4) health disparities; and (5) unintended, adverse social, individual, and community consequences of research and development.

 

Examples of project plans that will not meet the goals of the program:

• • Use of AI/ML models for biomarker interrogation across populations.
  • Research studies focused on clinical trials.
  • Proposals without a focus on minority health and/or health disparity populations
  • Proposals unrelated to cardiometabolic, cancer, or mental/behavioral health
  • Proposals with no plan to detect and mitigate potential bias in AI/ML models
  • Proposals that do not include partnership with healthcare organizations serving patients in FQHCs or CHCs in MUAs and/or serving a disproportionately high percentage(s) of NIH-designated health disparity populations
  • Proposals with no commitment to ethics/equity consultation or principles
  • Proposals with no plausible transfer of knowledge and capacity between commercial or high-resource institutions to lower-resource organizations serving medically underserved and/or health disparity populations.
  • Proposals that do not state a clear objective and do not propose a small-scale pilot to inform a more detailed plan or proposal to translate AI/ML into operational or clinical practice to address the needs of low-resource settings of care.

 

Organizational Structure and Nature of Collaboration Among the Required Partners

Organizational structure: The team structure should avoid giving any single individual undue authority that prevents contributions from the wider team for setting program priorities, resource distribution, and rewards. Strong leadership is required for complex team efforts to succeed, while at the same time effective team leadership requires decision-making based on an amalgam of interests, expertise, and roles, guided by recognized project objectives. Applicants should develop a management structure based on project objectives that effectively promotes the proposed research.

 

Nature of the Collaboration: A framework for sharing and/or integrating data across team members must be customized to fit the specific data needs of the project. Plans for data archiving and long-term preservation for team use should be described in the proposal (See Partnership Plan Section). Depending on the needs and challenges of managing team data, applicants may also include and justify data/resource sharing and management systems and/or hiring of professional data science staff.

 

Equitable Engagement: Consistent with NIH’s Project Unite, the proposed partners are encouraged to utilize equitable engagement in all aspects of the project.

 

Eligibility 

Eligible Organizations

 

Consistent with the goals of the AIM-AHEAD Coordinating Center, the following types of Higher Education and other institutions/organizations are highly encouraged to apply for support:

 

Higher Education Institutions

• • Public/State Controlled Institutions of Higher Education
  • Private Institutions of Higher Education

 

The following types of Higher Education Institutions are always encouraged to apply for NIH support as Public or Private Institutions of Higher Education:

• • Hispanic-serving Institutions
  • Historically Black Colleges and Universities (HBCUs)
  • Tribally Controlled Colleges and Universities (TCCUs)
  • Alaska Native and Native Hawaiian Serving Institutions
  • Asian American Native American Pacific Islander Serving Institutions (AANAPISIs)

 

Nonprofits Other Than Institutions of Higher Education

• • Nonprofits with 501(c)(3) IRS Status
  • Nonprofits without 501(c)(3) IRS Status
  • Tribal health and/or human service organizations or Tribally derived institutions (Urban Indian Health Organizations, Tribal Epidemiology Centers)

 

For Profit businesses

• • Small Businesses
  • For-Profit Organizations (Other than Small Businesses)

 

To be eligible for this CFP, the applicant institution must be a domestic institution located in the United States and its territories which meet both of the following requirements:

• • Has received an average of less than $50 million per year total costs of NIH support for the past three fiscal years.
  • This requirement can be checked online on https://report.nih.gov/award/index.cfm.
    • Follow this quick tutorial here to check your institutional NIH support.
    • The administrative review (before scientific review) will check this requirement.
  • Except for community organizations, nonprofits and non-academic institutions, has a documented historical mission to educate students from any of the populations that have been identified as underrepresented in biomedical, behavioral and social science research as defined by the National Science Foundation NSF, see http://www.nsf.gov/statistics/wmpd/) (i.e., African Americans or Blacks, Hispanic or Latino Americans, American Indians, Alaska Natives, Native Hawaiians, U.S. Pacific Islanders, and persons with disabilities) or has a documented historical track record of:
    • Recruiting, training and/or educating, and graduating underrepresented students as defined by NSF (see above),
    • Working with community stakeholders (e.g., Community-based organizations, Nonprofits (with or without 501(c)(3) IRS status, Faith-based organizations, Healthcare Providers, Health Systems, Small businesses, Large businesses, start-ups.) that have historically not participated in biomedical, behavioral, and social sciences research in the areas of AIM/ML

 

Community organizations, Nonprofits and Non-academic institutions are strongly encouraged to apply but should have a documented interest in working with health disparity populations. Before applying, these organizations must be registered with System for Award Management (SAM; see https://sam.gov/content/home) and must maintain active SAM registration throughout the award period.

 

Foreign Institutions

Non-domestic (non-U.S.) Entities (Foreign Institutions) are not eligible to apply.

Non-domestic (non-U.S.) components of U.S. Organizations are not eligible to apply.

Foreign components, as defined in the NIH Grants Policy Statement, are not allowed.

 

Required Registrations

Applicant organizations

Applicant organizations must complete and maintain the following registrations as described in the SF 424 (R&R) Application Guide to be eligible to apply for or receive an award. All registrations must be completed prior to the application being submitted. Registration can take 6 weeks or more, so applicants should begin the registration process as soon as possible. The NIH Policy on Late Submission of Grant Applications states that failure to complete registrations in advance of a due date is not a valid reason for a late submission.

 

• • System for Award Management (SAM) Applicants must complete and maintain an active registration, which requires renewal at least annually. The renewal process may require as much time as the initial registration. SAM registration includes the assignment of a Commercial and Government Entity (CAGE) Code for domestic organizations which have not already been assigned a CAGE Code.
    • NATO Commercial and Government Entity (NCAGE) Code Foreign organizations must obtain an NCAGE code (in lieu of a CAGE code) in order to register in SAM.
    • Unique Entity Identifier (UEI) - A UEI is issued as part of the SAM.gov registration process. The same UEI must be used for all registrations, as well as on the grant application.
  • eRA Commons - Once the unique organization identifier is established, organizations can register with eRA Commons in tandem with completing their Grants.gov registration; all registrations must be in place by time of submission. eRA Commons requires organizations to identify at least one Signing Official (SO) and at least one Program Director/Principal Investigator (PD/PI) account in order to submit an application.
  • Grants.gov Applicants must have an active SAM registration in order to complete the Grants.gov registration.

 

Program Directors/Principal Investigators (PD(s)/PI(s))

All PD(s)/PI(s) must have an eRA Commons account. PD(s)/PI(s) should work with their organizational officials to either create a new account or to affiliate their existing account with the applicant organization in eRA Commons. If the PD/PI is also the organizational Signing Official, they must have two distinct eRA Commons accounts, one for each role. Obtaining an eRA Commons account can take up to 2 weeks.

 

Application Submission Guidelines, Components, and Review Process

Application Deadlines

Application process opens	April 26, 2024
Application deadline	June 17, 2024
Awards announced	Sept 1, 2024
Award start date	Sept 16, 2024

 

Project Milestones

Kickoff conference	Sept 18, 2024
IRB approval determinations target	Nov 16, 2024
Community-engaged design & project development phase	Sept 2024 - January 2025
Community-engaged piloting phase	Feb 2025 - July 2025
Community-engaged plan for sustainability and scalability	Aug - Sept 2025
Period of Performance ends	September 15, 2025

 

Budget

Funding Amounts

NIH envisions supporting six Innovation for Equity projects, each with a budget cap of $500,000 total costs for a first year of funding to plan and conduct a community-engaged pilot and deliver a full research plan to meet the objectives of the program. This cap includes indirect cost allowances.

 

This CFP will permit applicants to co-develop project proposals with institutions engaged in AIM-AHEAD Cores and Hubs. Inclusion of personnel and/or resources in AIM-AHEAD Hubs or Cores in the project budget is allowable, though not required. Personnel and/or resources from Hubs or Cores may account for no more than 30 percent of proposed project direct and indirect costs.

 

The 1-year project proposals are required to include the following phases and target timelines:

• • Months 0-4: Community-engaged design
  • Months 5-10: Community-engaged piloting
  • Months 11-12: Community-engaged plan for sustainability and scalability

 

At the end of the Period of Performance, awardees must deliver a detailed plan to scale the piloted innovation in a future interventional research study or project to meet the goals of the Innovation for Equity Program. This plan must address scale-up and sustainability. Applicants to this solicitation are encouraged to propose other tangible artifacts (e.g., lessons learned document for AIM-AHEAD Consortium, code released to GitHub, datasets made available in NIH-designated repository, presentation, manuscript).

 

(NIH support beyond the first year of the Innovation for Equity Program is subject to the availability of funding.)

 

Application and Submission Requirements

 

Required Format:

• Arial font and no smaller than 11 point; margins at least 0.5 inches (sides, top and bottom); single-spaced lines. Submit application components as pdf documents.

 

Required Elements of the proposal

1. Title: The title should describe the project in concise, informative language.

 

2. Project Summary/Abstract (1-page limit): Provide a succinct description of the proposed work including the project’s long-term objectives, and a description of the research design and methods for the entire AI/ML project.

 

3. Project Description: The project description should contain the following components adhering to the page limits.

1. Specific Aims (1 page): Provide a clear, concise summary of the aims of the work proposed and its relationship to your long-term goals. State the hypothesis to be tested and anticipated outcomes or benefits upon successful completion of the project.

 2. Research Plan (5 pages)

i. Background and Significance: Summarize the background and state the problem this proposal will address. Summarize important findings of the applicant and others in the same field, critically evaluating existing knowledge. Identify gaps that this project is intended to fill. State concisely the importance and relevance of the research to community-engaged AI and health disparities research. Describe the health disparity population of interest. Also, it is incumbent upon the applicant to make a clear link between the project and AIM-AHEAD North Star III. The significance section will be assessed in terms of the potential impact on the AIM-AHEAD mission, and will be factored into the overall priority score as noted in the peer review criteria.

 

ii. Preliminary Studies: Describe concisely previous work by the applicant related to the proposed research that will help to establish the experience and competence of the investigator to pursue the proposed project. Include pilot studies showing the work is feasible. (If none, so state.)

 

iii. Community-Engaged Research Design and Methods: Description of proposed tests, methods or procedures should be explicit, sufficiently detailed, and well-defined to allow adequate evaluation of the approach to the problem. Describe any n community engagement methods, and how your research idea innovates or has an advantage over existing methods/applications and brings together diverse disciplines. Clearly describe the overall design of the study, with careful consideration to AI/ML and health disparities aspects of the approach or, and how the proposed methods will control for bias and address ethics, as well as how results will be analyzed. Include details of any collaborative arrangements that have been made. Applicants must explain how relevant social determinants of health, and biological variables, such as sex, are factored into the research design, analysis and reporting. Furthermore, describe the infrastructure modality that would be used, data sources needed and used, computing infrastructure needed and used (see data and infrastructure section). Describe plan for ensuring work is guided by a concern for human and social impact and attention to ethical, legal, privacy, and social implications of AI/ML including but not limited to (1) biases in datasets, algorithms, and applications; (2) issues related to identifiability and privacy; (3) impacts on disadvantaged or marginalized groups; (4) health disparities; and (5) unintended and/or adverse social, individual, and community consequences of research and development.

The application should include a description of any historical collaboration among the partners with FQHCs, MUAs, and Community Health Centers. It should a describe strategies for inclusive participation of partners in this project. Furthermore, the proposal should clearly articulate the goals of the project and expected outcomes in terms of collaboration and partnership, project plan, research products and other artifacts.

 

iv. Data: Applicants must describe (1) reference(s) to the data under consideration and reasons for this choice; (2) the potential impact of scientific advances that could be made from AI/ML applications developed with the data; (3) proposed methods/data modalities to be used; (4) how the data will be made available to AI/ML applications and researchers, for example, through NIH repositories, NIH knowledge bases, or other data sharing resources including those appropriate for controlled access data, and (5) how the ethical implications of data will be identified and addressed.

 

v. Consortium Development: Applicants are required to describe how the proposed project teams will engage and collaborate with the AIM-AHEAD community (e.g., contribute to documentation and training resources, welcome and empower new users, and help foster a diverse and inclusive community).

 

4. References Cited (maximum 3 pages): List only references cited in the Project Description or supplementary documents of the proposal.

 

5. Detailed Budget and Budget Justification: The budget justification should entail a narrative explanation of each of the components of the cost required of the proposed work. The budget explanations should focus on how each budget item is required to achieve the project’s objectives and how the estimated costs in the budget were calculated.

 

6. Facilities, Equipment and Other Resources: Facilities, Equipment and Other Resources should describe the resources needed and those that are available to the applicant and collaborators for the proposed research project. Please also describe cloud compute resources required for the proposed project, if applicable. Applicants should convey how the scientific environment in which the research will be conducted contributes to the probability of success.

 

7. Senior Personnel Documents:

• • Biographical Sketches: Biographical sketches in NIH format or resumes (5-page limit) are required for the PI, any co-PIs, and each of the participating Senior Personnel listed in the Project Description (including postdocs, staff, and /or students.
  • Current and Pending Support: Each PI, any co-PIs, and each of the participating Senior Personnel listed in the Project Description (including postdocs, staff, and /or students) must disclose any previous funding from NIH or AIM-AHEAD or Federal sources over the past 3 years. If none, please state "None."
  • Collaborators and Other Affiliations Information, including names of organizations, their tax status, and roles in the proposed project.

 

8. Human Subjects Research.

• • Does any of the proposed research involve human subjects?
  • Does any of the proposed research involve human specimens and/or data (Including EHR and/or repository data)?
  • Provide an explanation for any use of human specimens and/or data not considered to be human subjects research.
  • Does your research require a Data Use Agreement or other agreement(s) for use of data?
  • If yes, what is the plan of how these agreements will be executed between all partners/collaborators?

 

Note: Definition of Human subject: A living individual about whom an investigator (whether professional or student) conducting research: (1) obtains information or biospecimens through intervention or interaction with the individual, and uses, studies, or analyzes the information or biospecimens; or (2) obtains, uses, studies, analyzes, or generates identifiable private information or identifiable biospecimens. [45 CFR 46.102(e)].

 

9. Other Required Documents:

• • Institutional Need and Support Statement (up to 2 pages). This statement must be signed by the leadership of the institution (e.g., CEO, Director, University president, provost or college/school dean, vice president of research) as applicable to show support for the partnership and commitment to additional resources necessary to ensure that these partnerships will have the maximum sustainability. This letter should include
    • Assessment of the institution’s commitment to AI-driven solutions, its current AI research and education, and/or its data and infrastructure capacity;
    • A statement of commitment of institutional support for the proposed activities. List the specific resources, space, protected time, etc. These statements should also identify the specific number of positions that are wholly dedicated to AI data and infrastructure under the proposed partnership. In addition, if American Indians are involved, a Letter of Commitment from the Tribal Nation Leader is required.

 

• • Letter of collaboration: This letter from at least one organization(s) serving in an FQHC, MUA or an organization serving disproportionately high health disparity populations will describe its contribution to the project.

 

• • Results from Prior NIH/AIM-AHEAD Support. If applicable, you must submit the Results from prior NIH/AIM-AHEAD support as a supplementary document, not as part of your Project Description. This approach allows you to maximize the use of the page allowance to describe the proposed activities.

 

10. Budget

Funding Period

Applicants may request up to 1 year of funding support commensurate with project scope. Include project personnel from both the host institution and partner organization(s) (required).

Sample Budget Template:

Budget: Year One

 

Personnel	Base Salary	Months Effort	Requested Salary	Fringe	Total Funds Requested
Principal Investigator:	$		$	$	$
MPI:	$		$	$	$
CoI:	$		$	$	$
CoI:	$		$	$	$
Research Coordinator:	$		$	$	$
Research Assistant	$		$	$	$
Subtotal: Personnel	$		$	$	$
Maintenance & Operation					Funds Requested
Subject Payments					$
Vertebrate Animals					$
Consumable Supplies					$
Equipment					$
Travel					$
Subtotal: Maintenance & Operation					$
Total Direct Costs					$
Indirect Costs/F&A (__% of Direct Costs)					$
Total Costs					$

 

Allowable cost:

• • Academic Institutions: The award may be used for salary and fringe benefits of the Multiple Principal Investigators (MPIs), collaborating investigator(s), and other participants with faculty appointments, consistent with percent effort, and for project-related expenses, such as salaries of technical personnel essential to the conduct of the project, supplies, equipment, computers/electronics, travel (including international travel), volunteer subject costs, data management, and publication costs, etc. Tuition support for graduate students may also be requested.
  • Other non-Academic Institutions/Organizations: The award may be used for salary and fringe benefits of the Multiple Principal Investigators, collaborating investigator(s), and other participants consistent with salary structure, and for project-related expenses, such as salaries of technical personnel essential to the conduct of the project, supplies, equipment, computers/electronics, travel (including international travel), volunteer subject costs, data management, and publication costs, etc. Awardees are expected to attend the AIM-AHEAD Annual Conference.

 

Unallowable cost:

• • Funds to support general staff or administrative support.
  • Funds to support international travel.

 

Review Criteria

Scientific merit will be guided by 10 specific questions related to the proposed project:

Alignment with program:

1. 1. To what extent does the proposal align with North Star (III): Use AI/ML to address disparities and minority health in behavioral health, cardiometabolic health, and cancer and overall goals of AIM AHEAD?
   2. Does the proposal engage one or more FQHCs or CHCs from MUAs in collaborative partnership? Are specific strategies for effective collaboration between partners described in the proposal?
   3. Does the proposal develop novel algorithms or methods for addressing a health disparity problem with potential to improve understanding and outcomes of FQHC, MUA and/or health disparity populations?
   4. To what extent does the proposal appropriately consider ethical, legal, and privacy concerns and provide a plan to engage communities in design, implementation, and assessment of the project?

 

Expected outcomes:

1. 5. Does the proposal clearly articulate the goals of the project and expected outcomes in terms of collaboration, partnership, project plan, research products or other artifacts?
   6. Does the proposal clearly articulate a preliminary plan for sustainability and scalability?
   7. To what extent is the proposed approach likely to achieve the goals of the project? What is the likelihood of a successful outcome?

 

Team qualifications:

1. 8. Does the applicant team have the necessary background and capabilities to accomplish the proposed work in the expected funding period?

 

Consortium development/scalability:

1. 9. Does the applicant indicate willingness to engage and collaborate with the AIM-AHEAD community, contribute to documentation and training resources, welcome and empower new users, and help foster a diverse and inclusive community?
   10. Does the proposal demonstrate how the selected dataset(s) and/or AI/ML application(s) will result in knowledge transfer to low-resource organizations and the AIM-AHEAD ecosystem as a whole?

 

Programmatic Review Considerations

In accord with the objectives of the AIM-AHEAD Program, consideration may be given to:

• • The appropriateness and balance of health disparity populations for which the research is being conducted considering geographical region and other social determinants of health
  • Qualifications of leadership within the project (e.g., MPIs, Co-I, other key personnel)
  • Commitment to open source (e.g., Creative Commons license) of inventions and work products
  • Personnel capacity including administrative staffing, and project management (e.g., financial management, program management)
  • Proposed infrastructure required to execute stated research goals and objectives
  • Shared governance in decision-making by hosting institution
  • Equitable data sharing plan by hosting institution

 

At the programmatic review stage, prioritization of the applications may consider the overarching human concern with respect to the ethical, legal, and social implications of AI/ML including but not limited to:

• • Demographic data points that allow disparity analysis (e.g., gender, race/ethnicity, rurality) must be present in the data an applicant proposes to analyze in addressing the North Star research questions
  • Biases in datasets, algorithms, and applications
  • Issues related to identifiability and privacy
  • Impacts on disadvantaged or marginalized groups
  • Health disparities
  • Unintended, adverse social, individual, and community consequences of research and development.

 

Review Process

A Review Committee composed of AIM-AHEAD Consortium members will oversee the following steps and criteria to evaluate proposals and recommend award recipients to NIH, which makes the final decision on awards.

 

Compliance review: Program staff will do an initial check of applications for compliance with requirements of this Call for Proposals. This process may result in request for remediation and/or rejection of a proposal prior to Scientific Review and Programmatic Evaluation.

 

Scientific evaluation: A panel of reviewers will apply standard NIH scoring ranges to each application on each of the eight scientific merit criteria listed above.

 

Programmatic evaluation: It is expected that approximately three of the anticipated six awards will be assigned to the highest-scoring applications based on Scientific Merit and that the rest of the awards could be, but not necessarily will be, recommended for NIH consideration based on both Scientific Merit scores and Programmatic Evaluation considerations. The consortium will take steps to avoid any conflicts of interest with reviewers or voting MPIs. This is particularly important because this CFP allows for participation of AIM-AHEAD Hub or Core partners in the projects.

Applicants will receive their scores and notes from the Scientific Merit reviewers.

Please note that consistent with NIH practice and applicable law, funded programs may not use the race, ethnicity, or sex of prospective program participants or faculty as an eligibility or selection criteria. The race, ethnicity, or sex of candidates will not be considered by NIH in the application review process or when making funding decisions.

 

Awardee Expectations

Awardees are expected to develop novel AI/ML approaches that incorporate community knowledge (e.g., patients, advocates, health care professionals) to ethically address health disparities and inequities in populations that experience health disparities in the US by embracing the contributions across diverse constituencies, particularly those historically underrepresented in this emerging area of research. Therefore, it is expected of the applicants to provide evidence of the following:

 

Participation and Reports

• • Awardees will participate in monthly awardee meetings (via Zoom).
  • Awards will submit monthly reports and budget reports.
  • Awardees will participate in meetings with the AIM-AHEAD Coordinating Center to inform overall AIM-AHEAD data sharing / data access strategies.
  • Awardees will participate in AIM-AHEAD annual meetings.
  • Awardees will act as AIM-AHEAD our community ambassadors to help onboard colleagues.
  • Awardees must be willing to attend and present the results of their work at future AIM-AHEAD events as well as volunteer to review for future AIM-AHEAD programs.
  • Awardees agree to have AIM-AHEAD promote the project online through websites, social media, and other communication channels.
  • All awardees will be expected to participate in AIM-AHEAD activities during the year, including attending two annual program-wide meetings.
  • Awardees must provide a monthly summary of research status about milestones listed in the proposal, challenges faced and plans to overcome those challenges, usage of funds, and next steps.
  • One month following the project end date, awardees must provide a final report of research findings, usage of funds, and a list of publications, grant applications, articles, and conference talks emerging from the research.

 

Compliance and Governance

• • For all research projects involving human subjects research (including secondary data analysis), awardees are expected to submit their study for review to an IRB and obtain a determination letter/response (See section Application Components section, subsection 10). **Even if the projects do not involve human subjects, a “letter of determination” is required. When a local IRB is not available, alternative options should be used.
  • Awardees are required to obtain any required Data Use/Sharing or Regulatory/Contractual Agreements for data needed for their research.
  • Any current or future data sharing must follow relevant governance documents and agreements.
  • Awardees must comply with all applicable Federal statutes (such as those included in appropriations acts) regulations and policies in addition to their institutional and state policies to receive research funding.

 

Note: No funds can be drawn down from NIH payment system and no obligations may be made for research involving human subjects by any site of AIM-AHEAD coordinating center engaged in such research for any period not covered by both an OHRP-approved Federal Wide Assurance and approval from the IRB, as required, consistent with 45 CFR Part 46 and any NIH required policies.

 

Ethical Use of Data

The Applied Ethical AI (AEAI) Subcore was established to provide support in surfacing, reasoning about, and resolving ethical issues in the AIM-AHEAD program. It is expected that all projects will work with the AEAI on the development of an initial ethics review and plan for their project within the first 4-6 weeks of support and will commit to participating in two AEAI-supported ethics forums during the course of their sponsorship. These forums are designed to highlight and address ethical challenges in the development and implementation of AI and machine learning and range from open office hours to moderated discussions on hot topics in ethics and AI/ML. Topics that may be addressed during these forums include but are not limited to:

1. 1. Investigation into the ethical grounding of the project and the plans for embedding ethical reasoning into the project
   2. Consultation on how to test for algorithmic fairness and data bias with respect to potentially sensitive variables (e.g., socio-demographics)
   3. Detection of potential data privacy concerns and alternatives for their resolution

 

Required Training/Certifications

Awardees will be expected to complete Human Subjects Research training (e.g., CITI) and any other training required by datasets they intend to use in their projects.

 

Appendix: Awardee Resources

AIM-AHEAD consists of its Leadership Core made up of Regional Hubs and the following Cores:

• • Data Science Training Core (DSTC)
  • Data and Research Core (DRC),
  • Infrastructure Core (IC) and its Applied Ethical AI (AEAI) Subcore

 

Data sources and accessibility

Proposed projects may either identify their own datasets and/or be approved for use of existing AIM-AHEAD resources, including the OCHIN Community Health Equity Database on AIM-AHEAD Service Workbench or MedStar Health EHR through the AIM-AHEAD Data Bridge (AADB).

 

Post-award, there are four key requirements for obtaining access to AIM-AHEAD Curated Data (OCHIN/AADB), with target timelines for awardees below:

1. 1. Mandatory Human Subjects Research Training such as CITI “Human Research (Protection of Human Subjects)” and “Responsible Conduct of Research.”
   2. Data consultation with the data source (e.g., OCHIN or MedStar) and submission of data specifications form within 30 days of award.
   3. Submission for IRB approval/determination within 60 days of award.
   4. Signed data use agreement and IRB approval/determination within 90 days of award.

 

Dataset options (more information available on below datasets/cohorts):

Data set	Brief Description	Data Allowed	Size	Analysis platform tools
A customized subset from OCHIN Community Health Equity Database	EHR data from Underserved communities	HIPAA Limited dataset, individual-patient level data with dates and geographic indicators if needed for research	A customized subset will be created for the research question of awarded fellow from over 6 million records	AIM-AHEAD Service Workbench
Curated data from the MedStar Health EHR	EHR data from hospital system network with 31% African American patient representation	Multiple curated dataset options (further detail on website) pre-curated or custom curated de-identified EHR, Limited Dataset, Full PHI EHR dataset, Imaging, Select clinical notes, select genomics data, synthetic data	Pre-curated datasets and custom curated datasets of varying sizes   Curated from the EHR with over 5 million patient records	AIM-AHEAD Data Bridge
60+ studies from NHLBI BioData Catalyst	Selected large-scale cohorts related to heart, lung, blood and sleep disorders. Includes both prospective clinical studies and associated genomic TOPMED data.	De-identified dataset. Including individual level genomic (TOPMED full genomes) and clinical datasets.	Additional description   List of studies: 60+ studies are available to choose from	NHLBI BioData Catalyst PIC-SURE and Seven Bridges Platforms
Selected 15 Open datasets on AWS	A variety of datasets available including clinical and genomic data	Public data, and controlled access data (depends on dataset)	Selected 15 Open datasets on AWS	AIM-AHEAD Service Workbench
NIH All of Us	The All of Us Research Program is building one of the largest biomedical data resources of its kind.	The All of Us Research Hub stores health data from a diverse group of participants from across the United States.	Additional descriptions 616,000+ Participants   360,000+ Electronic Health Records   444,000+ Biosamples Received	All of Us Researcher Workbench
The ScHARe Data Ecosystem	ScHARe is a cloud-based research collaboration platform developed by the National Institute on Minority Health and Health Disparities and the National Institute of Nursing Research	Google-hosted Public Datasets   ScHARe-hosted Public Datasets   ScHARe-hosted Project Datasets	The ScHARe Data set list	ScHARe Community Workbench

 

Dataset Infrastructure

Infrastructure of 1) f 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.

 

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

 

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

 

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

 

OCHIN Community Health Equity Database

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 OCHIN i2b2 End User Agreement.

 

AIM-AHEAD DataBridge

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 for custom dataset requests.

 

Available EHR data including (but 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, social determinants of health, device implants

 

NHLBI BioData Catalyst studies

Over the last several decades, NHLBI has invested in creating a significant resource for research and development by supporting the creation of many observational, epidemiological, and longitudinal datasets related to heart, lung, blood and sleep phenotypes, with the aim of uncovering insights that may be leveraged toward novel therapeutic, interventional, or preventive strategies resulting in improved patient outcomes. New technologies and favorable cost trajectories have enabled detailed characterization of these study participants, including whole genome sequencing (and other omics) and imaging on hundreds of thousands of participants. Together, this data coupled with animal and cellular models, increase opportunities for data-driven translational science. We have fully entered the “Big Data” arena, in which we encounter both unprecedented opportunities as well as challenges. Current paradigms for analyzing and combining these datasets are limited by both practical and conceptual constraints. The NHLBI BioData Catalyst, is a novel ecosystem of platforms and tools to enable and accelerate the mining of these rich datasets from diverse populations.

 

Full Description (including patient, variables, and sample counts) is available here

 

Acronym	Name	Study Focus	Study Design
FHS	Framingham Cohort	Cardiovascular Disease	Prospective Longitudinal Cohort
JHS	Jackson Heart Study (JHS) Cohort	Cardiovascular Disease	Prospective Longitudinal Cohort
CARDIA	Coronary Artery Risk Development in Young Adults (CARDIA)	Cardiovascular Disease	Prospective Longitudinal Cohort
ARIC	NHLBI TOPMed - NHGRI CCDG: Atherosclerosis Risk in Communities (ARIC)	Cardiovascular Disease	Case-Control
WHI	Women's Health Initiative Clinical Trial and Observational Study	Women's Health	Prospective Longitudinal Cohort
ACTIV4a	A Multicenter, Adaptive, Randomized Controlled Platform Trial of the Safety and Efficacy of Antithrombotic Strategies in Hospitalized Adults with COVID-19 (ACTIV4A)	COVID-19	Interventional
ACTIV4b	COVID-19 Positive Outpatient Thrombosis Prevention in Adults Aged 40-80	COVID-19	Interventional
AMISH	NHLBI TOPMed: Genetics of Cardiometabolic Health in the Amish	Cardiovascular Disease	Family/Twin/Trios
BABYHUG	Hydroxyurea to Prevent Organ Damage in Children with Sickle Cell Anemia (BABY HUG) Phase III Clinical Trial and Follow-Up Observational Studies I and II	Sickle Cell Anemia	Clinical Trial
BAGS	NHLBI TOPMed: The Genetics and Epidemiology of Asthma in Barbados	Asthma	Family/Twin/Trios
C3PO	Clinical-trial of COVID-19 Convalescent Plasma in Outpatients	COVID-19	Clinical Trial
CATHGEN	CATHeterization GENetics (CATHGEN)	Coronary Disease	Cross-Sectional
CCAF	The Cleveland Clinic Foundation's Lone Atrial Fibrillation GWAS Study	Atrial Fibrillation	Case Set
CFS	NHLBI Cleveland Family Study (CFS) Candidate Gene Association Resource (CARe)	Sleep Apnea Syndromes	Prospective Longitudinal Cohort
COPDGENE	Genetic Epidemiology of COPD (COPDGene)	Pulmonary Disease, Chronic Obstructive	Case-Copntrol
CRA	NHLBI TOPMed: The Genetic Epidemiology of Asthma in Costa Rica	Asthma	Family/Twin/Trios
CSSCD	Cooperative Study of Sickle Cell Disease (CSSCD)	Sickle Cell Disease	Clinical Trial
DHS	NHLBI TOPMed: Diabetes Heart Study (DHS) African American Coronary Artery Calcification (AA CAC)	Cardiovascular Disease	Cross-Sectional
ECLIPSE	Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE)	Pulmonary Disease, Chronic Obstructive	Case-Control
EOCOPD	NHLBI TOPMed: Boston Early-Onset COPD Study	Chronic Obstructive Pulmonary Disease	Family/Twin/Trios
GALAII	Genes-Environments and Admixture in Latino Asthmatics (GALA II) Study	Lung Diseases	Case-Control
GENESTAR	GeneSTAR (Genetic Study of Atherosclerosis Risk) NextGen Consortium: Functional Genomics of Platelet Aggregation Using iPS and Derived Megakaryocytes	Platelet Aggregation	Prospective Longitudinal Cohort
GENOA	Genetic Epidemiology Network of Arteriopathy (GENOA)	Hypertension	Prospective Longitudinal Cohort
GENSALT	Genetic Epidemiology Network of Salt Sensitivity (GenSalt)	Arterial Pressure, Mean	Interventional
GOLDN	NHLBI TOPMed: GOLDN Epigenetic Determinants of Lipid Response to Dietary Fat and Fenofibrate	Lipids	Prospective Longitudinal Cohort
HCHSSOL	Hispanic Community Health Study /Study of Latinos (HCHS/SOL)	Cardiovascular Disease	Prospective Longitudinal Cohort
HCT_for_SCD	Hematopoietic Cell Transplant for Sickle Cell Disease (HCT for SCD)	Sickle Cell Disease	Prospective Longitudinal Cohort
HVH	Heart and Vascular Health Study (HVH)	Cardiovascular Disease	Case-Control
HYPERGEN	NHLBI TOPMed: HyperGEN - Genetics of Left Ventricular (LV) Hypertrophy	Hypertrophy, Left Ventricular	Family/Twin/Trios
MAYOVTE	NHLBI TOPMed: Whole Genome Sequencing of Venous Thromboembolism (WGS of VTE)	Venous Thromboembolism	Case Set
MESA	Multi-Ethnic Study of Atherosclerosis (MESA) SHARe	Cardiovascular Disease	Prospective Longitudinal Cohort
MGHAF	Massachusetts General Hospital (MGH) Atrial Fibrillation Study	Atrial Fibrillation	Case Set
MSH	Multicenter Study of Hydroxyurea (MSH)	Sickle Cell Disease	Clinical Trial
NSRR-CFS	National Sleep Research Resource (NSRR): Cleveland Family Study (CFS)	Sleep Apnea Syndromes	Prospective Longitudinal Cohort
ORCHID	COVID19-ORCHID	COVID-19	Clinical Trial
PARTNERS	NHLBI TOPMed: Partners HealthCare Biobank	Atrial Fibrillation	Case Set
PCGC	The Pediatric Cardiac Genetics Consortium (PCGC) Study	Heart Defects, Congenital	Prospective Longitudinal Cohort
RED_CORAL	PETAL Repository of Electronic Data COVID-19 Observational Study (RED CORAL)	COVID-19	Control Set
SAFHS	NHLBI TOPMed: San Antonio Family Heart Study (SAFHS)	Cardiovascular Disease	Family/Twin/Trios
SAGE	NHLBI TOPMed: Study of African Americans, Asthma, Genes and Environment (SAGE) Study	Lung Diseases	Case Set
SARCOIDOSIS	NHLBI TOPMed: African American Sarcoidosis Genetics Resource	Sarcoidosis	Family/Twin/Trios
SARP	NHLBI GO-ESP: Lung Cohorts Exome Sequencing Project (Asthma): Genetic variants affecting susceptibility and severity	Asthma	Case Set
SAS	Genome-Wide Association Study of Adiposity in Samoans	Obesity	Cross-Sectional
SHARP	SNP Health Association Asthma Resource Project	Lung Diseases
STOP-II	Optimizing Primary Stroke Prevention in Children with Sickle Cell Anemia (STOP II)	Sickle Cell Disease	Clinical Trial
THRV	NHLBI TOPMed: Rare Variants for Hypertension in Taiwan Chinese (THRV)	Blood Pressure	Prospective Longitudinal Cohort
VAFAR	NHLBI TOPMed - NHGRI CCDG: The Vanderbilt AF Ablation Registry	Atrial Fibrillation	Case Set
VUAF	NHLBI TOPMed: The Vanderbilt Atrial Fibrillation Registry (VU_AF)	Atrial Fibrillation	Case Set
Walk-PHaSST	Treatment of Pulmonary Hypertension and Sickle Cell Disease with Sildenafil Therapy (Walk-PHaSST)	Sickle Cell Anemia	Clinical Trial
WGHS	NHLBI TOPMed: Novel Risk Factors for the Development of Atrial Fibrillation in Women	Atrial Fibrillation	Case Set

 

AWS Open Datasets

More information (description and links) is available here

Acronym	Name of AWS open dataset
EMBED	EMory BrEast Imaging Dataset
1000-genomes	1000 Genomes
tcga	The Cancer Genome Atlas
broad-gnomad	Genome Aggregation Database (gnomAD)c
broad-pan-ukb	UK Biobank Pan-Ancestry Summary Statistics
kids-first	Gabriella Miller Kids First Pediatric Research Program (Kids First)
target	Therapeutically Applicable Research to Generate Effective Treatments (TARGET)
hcmi-cmdc	Human Cancer Models Initiative (HCMI) Cancer Model Development Center
cgci	Cancer Genome Characterization Initiatives - Burkitt Lymphoma, HIV+ Cervical Cancer
organoid-pancreatic	Pancreatic Cancer Organoid Profiling
nciccr-dlbcl	National Cancer Institute Center for Cancer Research - Diffuse Large B Cell Lymphoma (DLBCL) Genomics and Expression
mmrf-commpass	CoMMpass from the Multiple Myeloma Research Foundation
hcp-openaccess	The Human Connectome Project
cptac-2	Clinical Proteomic Tumor Analysis Consortium 2 (CPTAC-2)
cptac-3	Clinical Proteomic Tumor Analysis Consortium 3 (CPTAC-3)

 

NIH All of Us

The All of Us Research Program’s centralized, secure, cloud-based platform allows researchers across a wide range of settings and institutions and at all stages of their careers (e.g., students, early-stage investigators) to execute rapid, hypothesis-driven research with just a computer and an Internet connection.

 

All of Us facilitates equity in access in a deliberately inclusive way, creating a demographically diverse researcher cohort.

 

Currently, academic, not-for-profit and healthcare organizations are eligible to apply for an All of Us Data Use and Registration Agreement (DURA). This is the first step to accessing the All of Us Researcher Workbench.

 

Within the Researcher Workbench's Controlled Tier, data from nearly 250,000 whole genome sequences and more than 312,900 genotyping arrays are integrated alongside data from surveys, physical measurements, EHRs, and wearables. The data and tools are available only to registered researchers who have taken additional steps and training to access these data.