Issued by

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

 

Overview

The AIM-AHEAD Public-Private Partnerships to Improve Population Health Using AI/ML Program is designed to support mutually beneficial triadic partnerships among a 1) local, state, and tribal accredited health departments; 2) limited resource higher education institution; and 3) a data-science oriented organization with an accessible data library to collaboratively conduct health equity-related AI/ML studies that align with the overall goal of AIM-AHEAD. Over 370 health departments exist in the United States (U.S.), many of whom are accredited and have research units that conduct and manage grant applications. Public health departments work to achieve health equity and serve as a force in their communities to shape the conditions for population health. These organizations are critical and trusted organizations that can benefit from enhancing their AI/ML capabilities to advance various aspects of public health, from early detection and monitoring, predictive analytics, disease surveillance and monitoring, and outbreak detection to healthcare resource allocation, and personalized interventions. Partnerships among public health department professionals, academic researchers, and data-science/AI/ML experts can further leverage data-driven insights that contribute to more effective and efficient public health strategies to improve community health outcomes. These approaches align with the overarching Healthy People 2030 goal “to eliminate health disparities, achieve health equity, and attain health literacy to improve the health and well-being of all”.

 

Mutually beneficial partnerships comprised of health departments, academic institutions, and private data science-oriented organizations can play a crucial role in addressing barriers and promoting the successful implementation of AI/ML capabilities.

 

AIM-AHEAD Consortium

The overarching goal of AIM-AHEAD is to build trusted partnerships to increase and enhance the engagement of researchers and communities underrepresented in the development of AI/ML models, and to expand the capabilities of this emerging technology to address health disparities and advance health equity. Underrepresented and underserved communities are often disproportionately burdened by infectious and chronic diseases and related complications. Engaging community stakeholders in an effort to address these conditions is essential to capitalize on their expertise, sources of data, and unique perspectives which can result in innovative research questions, the insightful interpretation of findings, and novel analytic strategies. The benefits of including underrepresented individuals, institutions, and communities can be significant; however, historical inequities have often limited the infrastructural and data science training capacity of these groups and organizations to engage in research applying AI/ML approaches to study topics relevant to these communities.

 

The AIM-AHEAD Consortium seeks to support multidisciplinary research projects that use artificial intelligence/machine learning (AI/ML) to develop novel algorithms and approaches to address health disparities and inequities in populations that experience health disparities. Research projects that use new or real-world synthetic data or existing datasets, including but not limited to electronic health records (EHR), images, and social determinants of health (SDoH) variables to develop and enhance AI/ML algorithms and applications have the potential to reduce health disparities while improving healthcare and outcomes of particular interest. The AIM-AHEAD Public-Private Partnerships to Improve Population Health Using AI/ML Program is designed to support mutually beneficial partnerships among local, state, and tribal accredited health departments, minority-serving institutions, and data-science oriented organizations to collaboratively conduct health equity-related studies using AI/ML that align with the goals of AIM-AHEAD.

 

The AIM-AHEAD Coordinating Center (A-CC) is comprised of four cores: 1) Leadership/Administrative Core; 2) Data Science Training Core; 3) Data and Research Core; and 4) Infrastructure Core. Each core provides distinct resources that are essential to expand and support education, training, and implementation of AI/ML models and research that addresses health disparities and advances health equity. Brief descriptions of the cores are provided below.

 

Leadership/Administrative Core: Leads the overall A-CC, recruits and coordinates consortium members, project management, partnerships, stakeholder engagement, and outreach to enhance the diversity of researchers in AI/ML related research, with an emphasis on health disparities research, and establishes and maintains trusted relationships with groups experiencing health disparities to enhance the diversity of data used in AI/ML research. The Leadership/Administrative Core is comprised of seven regional hubs (i.e., Central, Northeast, North and Midwest, South Central, Southeast (2), and West) that are designed to engage partners and stakeholders across the United States and its territories. This core operates as a Pass-Through Entity (PTE) for AIM-AHEAD.

 

Data Science Training Core: Develops, implements, and assesses data science training curricula to enhance capacity among diverse populations, including underrepresented or underserved groups impacted by health disparities.

 

Data and Research Core: Determines and addresses research priorities and needs in linking and preparing multiple sources and types of research data to form an inclusive basis for AI/ML use cases that will illuminate strategies and approaches to ameliorate health disparities. This may include facilitating the extraction and transformation of data from EHR for research use and consideration of SDoH as crucial contributors to health outcomes.

 

Infrastructure Core: Conducts the assessment of data, computing, and software infrastructure models, tools, resources, data science policies, ethical AI, and AI/ML computing models that will facilitate AI/ML and health disparities research; and establishes pilot data and analytic environments to accelerate overall A-CC aims.

 

 

The proposal guidelines and application review criteria are provided below.

 

Eligibility

 

Eligible Individuals (Program Director/Principal Investigator)

Any individual(s) with the skills, knowledge, and resources necessary to carry out the proposed research as the Program Director(s)/Principal Investigator(s) is(are) invited to work with the partner organizations to develop an application for support. Individuals from diverse backgrounds, including underrepresented racial and ethnic groups, individuals with disabilities, and women are encouraged to apply. See the Notice of NIH's Encouragement of Applications Supporting Individuals from Underrepresented Ethnic and Racial Groups as well as Individuals with Disabilities, NOT-OD-22-019.

 

Eligible Organizations

Applicant teams must be comprised of three entities: 1) a local, state, and/or tribal health department; 2) a limited resource higher education institution; and 3) a data science-oriented organization with a library of sharable data. Any of these three types of institutions are eligible to be the primary applicant organization, however, the primary applicant must be a domestic institution located in the United States and its territories. Applications that do not include these three types of organizations will be considered non-responsive to the Call for Proposals and returned without review. Applicant institutions must meet the respective eligibility criteria below:

 

1. Public Health Departments may include the following:

• • Current or pending accreditation by the Public Health Accreditation Board (PHAB), with the infrastructure to manage research grants
  • Tribal health services, departments, or equivalents
  • Special district governments
  • County governments
  • State governments
  • City or township governments

Unrestricted (i.e., open to any type of entity above), subject to any clarification in text field entitled "Additional Information on Eligibility"

 

2. Higher Education Institutions

Consistent with the goals of the AIM-AHEAD Coordinating Center, the following types of Higher Education Institutions are highly encouraged to apply for support:  

• • 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 College 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)

 

3. Data Science-Oriented Organizations may include the following:

• • Small businesses
    • Eligible to receive and manage federal funding and the associated required funding
    • Ability and willingness to share data with the applicant partner organizations
  • For-profit organizations (other than small businesses)
    • Eligible to receive and manage federal funding and the associated required funding
    • Ability and willingness to share data with the applicant partner organizations
  • 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)

 

To be eligible for this call for proposals, the limited resource higher education 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, that have 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

 

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.

 

Research Topics of Interest

Studies that can help advance public health research in high priority areas are strongly encouraged. Topics of interest include, but are not limited to:

• • Use of predictive modeling in enhancing the accuracy, efficiency, and actionable insights in public health decision-making
  • Studies that employ AI-driven disease forecasting
  • Novel studies that develop new analytical tools and methods 
  • Studies that use AI methods, including machine learning and natural language processing to enhance risk prediction approaches
  • Machine learning algorithms for EHRs in public health departments
  • Use of AI in public health diagnostics to increase the speed and precision of diagnostic procedures

 

Required Registrations

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. Registrations are required to received federal grant awards. Registration can take 6 weeks or more, so applicants should begin the registration process as soon as possible.

 

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). Federally recognized tribes and their derivatives are exempt from this requirement.

• • 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 to submit an application.
  • Grants.gov Applicants must have an active SAM registration to complete the Grants.gov registration.

 

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.

 

Eligible Applicants

Applicants who have previously received AIM-AHEAD funding are eligible; however, the research question(s) must be distinct from the previously funded application.  

 

Budget Estimates, Number of Awards, and Duration of the Program

The AIM-AHEAD Consortium intends to select no more than seven (7) projects to recommend for funding. The budget limit for each application is $525,000 in total costs. Funded applications must be completed within one year from the start date (September 9, 2024).

 

Potential risks/challenges and mitigation measures

The risks and challenges for the Public-Private Partnerships to Improve Population Health Using AI/ML Program include submission of timely invoices and monthly reports as well as the completion of monthly project management and evaluation documents. Potential mitigation strategies involve the development of standardized reporting tools, the use of easily accessible systems for information exchange, applicant organizations sufficiently staffed to facilitate compliance, and appreciation of the heterogeneity of organizational infrastructures. 

 

Expected Outcomes, Impact, and Anticipated Benefits of the Program

The Public-Private Partnerships to Improve Population Health Using AI/ML Program is expected to expand the AIM-AHEAD consortium, lead to consortium pilot grant applications, and enhance the professional networks of AIM-AHEAD stakeholders, particularly those from groups and organizations underrepresented in AI/ML research. Findings from the funded projects are expected to generate strong proof-of-concept data that can serve as the empirical foundation for subsequent NIH grant applications. Expected outcomes include the submission of a peer-reviewed manuscript and the development of plans for a subsequent grant application from the awardee teams.

 

Anticipated benefits of the Public-Private Partnerships to Improve Population Health Using AI/ML program will include:

• • Enhancement of the data infrastructure of accredited health departments
  • Strengthening the public health infrastructure
  • Creation of AIM-AHEAD new stakeholders
  • Enhancement of the public health workforce
  • Expanded literacy in public health, population health, health disparities, and minority health

 

Review Criteria

Applications submitted to the AIM-AHEAD Coordinating Center will be reviewed for scientific and technical merit through a peer-review process. Peer reviewers will use the following criteria to evaluate the applications.  Based on the scores, AIM-AHEAD Multiple Principal Investigators (MPIs) will make recommendations to NIH for grant awards.

• • To what extent does the proposal align with the overall goals of the Consortium? To what extent is the project likely to advance these goals?
  • Are the application’s specific aims sufficiently independent?
  • What is the likelihood of accomplishing the specific aims within the period of the award?
  • Does the proposal develop novel algorithms or methods for addressing a health disparity, or generate novel insights into a health disparity?
  • To what extent is the proposed approach reasonable to achieve the goals of the project? What is the likelihood of a successful outcome?
  • Does the proposal include a description of the historical collaboration among the partners?
  • To what extent is the proposed project mutually beneficial for each partner?
  • To what extent does the proposal appropriately consider ethical, legal, and privacy considerations?
  • Is this project likely to generate sufficient preliminary data that can lead to a larger NIH grant application?
  • How likely are the proposed partnerships to be sustainable beyond the duration of the award?

 

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.

 

Application Submission Guidelines, Components, and Review Process

 

Which Consortium hub should I connect with?

Be sure to direct your application to the hub that represents your geographic location.

 

Hub Name	States Represented
Central Hub	American Samoa, Hawaii, Guam
North and Midwest Hub	Alaska, Colorado, Idaho, Iowa, Kansas, Minnesota, Montana, Nebraska, North Dakota, Oregon, South Dakota, Utah, Washington, Wisconsin, Wyoming
Northeast Hub	Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, Washington DC
South-Central Hub	Louisiana, Mississippi, Oklahoma, Texas
Southeast Hub-Meharry	Arkansas, Illinois, Indiana, Kentucky, Michigan, Missouri, North Carolina, Ohio, Tennessee, Virginia, West Virginia
Southeast Hub-Morehouse	Alabama, Georgia, Florida, Puerto Rico, South Carolina, U.S. Virgin Islands
West Hub	Arizona, California, Nevada, New Mexico, Northern Mariana Islands

 

Application Components

1. Applicant Information (Principal Investigator(s)/Program Director(s))

• • Provide names, institution/organizations, department, position titles, research areas, and email addresses

 

2. Biosketch in NIH (https://grants.nih.gov/grants/forms/biosketch.htm) or other format, a curriculum vitae, or a professional resume (Maximum 5 pages). Biosketches are required for key personnel.

 

3. Proposal Summary (limit 2000 characters/approx. 300 words)

 

• The Research Description should consist of the following sections:

1. Title and Specific Aims (1 page)

• • • • • Provide a clear, concise summary of the aims of the proposed work and its relationship to your long-term goals. State the hypothesis to be tested.

2.  Significance (1 page)

• • • • • State the premise for the research grant.
        • State the importance of the problem, the critical issue to be addressed and the significance and relevance for public health departments.
        • Explain how the proposed project will advance scientific knowledge or technical capability.
        • Describe how the proposed project will contribute to the production of a subsequent extramural grant application.

3.  Approach and Timeline (3 pages)

• • • • • Demonstrate the feasibility of accomplishing the proposed research within one year.
        • Describe a community-engaged and/or collaborative research study design and strategies for inclusive participation of partners and partner organizations.
        • Description of the specific data source, primary data collection or a
        • Outline the processes for establishing a data use agreement and obtaining IRB approval within the first 90 days of the award
        • Clearly specify anticipated outcomes and deliverables of the proposed effort

4.  Investigative Team (1 page maximum)

• • • • • Provide a description of the investigative team and their experience related to the proposed study
        • Must clearly identify the plan for collaboration among partners and the contribution of each member of the research team

5. Partnership Plan (1 page maximum)

• • • • • See the Sample Partnering Agreement Template from the NIH Office of the Ombudsman in the Center for Collaborative Resolution for elements to address in this section https://ombudsman.nih.gov/partnerAgree)

6. References Cited (1 page maximum)

• • • • • List only references cited in the project description or supplementary documents of the proposal.

7. The Budget and Budget Justification (2 pages)

• • • • • Use the NIH Detailed Budget for Initial Budget Period for page one.
        • Justification should be included under the separate fields for itemizing costs (consultants, equipment, supplies, travel, etc.) for page 2. The total budget cannot exceed $525,000 and must include direct and indirect costs. Applicants should budget for travel to the AIM-AHEAD Consortium Annual Meeting.

8. Responsible Conduct of Research, Human Subjects, and Animal Training

• • • • • NIH funding requires that investigators and all key personnel MUST comply with the Responsible Conduct of Research Additionally, projects that involve human subjects or data from living humans are required to submit the study protocol for review from an Institutional Review Board (IRB) and provide documentation of the determination from the IRB.

 

There is flexibility in assigning page limits among the 5 components, but the proposal should not exceed 11 pages (11 pt. Arial font and 0.5-inch margins).

 

Review Process

An internal pre-review of applications for completeness and adherence to proposal guidelines will be conducted. Applicants will have 48 hours to respond with identified corrections. After that time, incomplete and non-adherent applications will be returned unreviewed. All eligible applications will be reviewed following a modified NIH peer review process. The standard NIH scoring range (1-9) will be used to assess the strengths and weakness of the criteria:

 

• • Overall Impact
  • Significance
  • Innovation
  • Approach

 

Additional Review Criteria. The investigative team and the institutional environment will be evaluated by the reviewers and considered when ranking applications but will not be assigned a priority score.

 

Reviewers will identify a subset of proposals to be considered for funding. The NIH AIM-AHEAD program staff will review the final pool of applications recommended for funding and provide the final approval. PIs of successful proposals will be notified by email. Brief feedback from the reviewers will be provided to all applicants via email.

 

Grantee Expectations

The Applied Ethical AI (AEAI) Sub-core 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 and will commit to participating in two AEAI-supported ethics forums during their sponsorship. These forums are designed to highlight and address ethical challenges in the development and implementation of AI/ML and range from open office hours to moderated discussions on hot topics in ethics and AI/ML.

All grantees will also be expected to comply with AIM-AHEAD program guidelines which include:

 

1. 1. Participation in monthly awardee meetings (via Zoom)
   2. Timely submission of monthly reports, invoices, and surveys
   3. Participation in annual hub and AIM-AHEAD Consortium meetings
   4. Delivery of presentations of project results in AIM-AHEAD meetings
   5. Agreement for AIM-AHEAD to disseminate study findings through online websites, social media, and other communication channels
   6. Provision of a summary of research status about milestones listed in the proposal, challenges faced and plans to overcome those challenges, usage of funds, and next steps
   7. By the project end date, submission of a 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

 

Data Resources

Applicants may apply to use 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). Alternatively, applicants may propose using existing, large-scale databases – such as those typically housed in state or tribal health departments – appropriate to their applications. In such instances, applicants are expected to document that said databases can speak to the proposed research questions.

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

 

OCHIN Community Health Equity Database

OCHIN, a nonprofit health care innovation center with a core mission to advance health equity, operates the most comprehensive database on primary healthcare and outcomes of traditionally underserved patients. The OCHIN Epic EHR data warehouse aggregates electronic health record (EHR) and social determinants of health (SDH) data representing >6 million patients from 170 health systems and 1,600 clinic sites across 33 states (4.6 million patients are ‘active,’ with a visit in the last 3 years).

 

Approved AIM-AHEAD projects can obtain access to up to 10 years of longitudinal OCHIN Epic ambulatory EHR data, which is research-ready on the PCORnet Common Data Model (CDM). Contributing health systems are outpatient community-based health centers, which deliver comprehensive, culturally responsive, high-quality primary care health care services for communities most impacted by health disparities. This includes individuals and families experiencing poverty, houselessness, migrant agricultural workers and veterans. Community-based health centers often provide on-site services such as dental, pharmacy, mental health, substance abuse treatment, and social work regardless of patients’ ability to pay.

 

The OCHIN Community Health Equity Database will be accessed on the AIM-AHEAD Service Workbench.

 

See Data Dictionary of the OCHIN Community Health Equity Database on AIM-AHEAD Service Workbench.

 

MedStar Health AADB Data

MedStar Health Research Institute hosts a robust database of Electronic Health Records which can be made available to approved applications. The MedStar Health System includes an extensive network of clinical facilities in the mid-Atlantic region, including 10 hospitals (33% rural hospitals) and includes over 300 points-of-care connected by MedStar’s EHR system, built on the Cerner Millennium platform. The system includes 5 million unique patients with approximately 31% African American patients. Project-specific datasets can be curated and made available for pilot use. Additionally, the AADB has curated six AI/ML ready datasets which are fully de-identified and ready for access upon regulatory clearance.

 

Six AADB available curated datasets:

• • Cardiometabolic correlates and maternal health
  • COVID-19 pandemic: Cardiometabolic, cancer, and behavioral health
  • Opioid use and misuse
  • Schizophrenia data
  • Voice-Assisted Personal Assistance in Heart Failure
  • Breast and Lung Cancer Images

 

To learn more about these data, visit the AADB website.

 

Requirements Prior to Obtaining Access to AIM-AHEAD Curated Data (AADB):

 

• • Mandatory Human Subjects Research Trainings such as CITI “Human Research (Protection of Human Subjects)” and “Responsible Conduct of Research”.
  • Initial data consult with the MedStar Health AADP personnel to refine data request 30 days of award.
  • Submission for IRB approval/determination within 60 days of award.
  • Signed data use agreement and IRB approval/determination within 90 days of award.

 

ScHaRe

As described on the NIMHD website, https://www.nimhd.nih.gov/resources/schare/

the Science Collaborative for Health disparities and Artificial intelligence bias REduction (ScHARe) is a cloud-based platform for population science including social determinants of health (SDoH), and data sets designed to accelerate research in health disparities, health and healthcare delivery outcomes, and artificial intelligence (AI) bias mitigation strategies.

 

ScHARe aims to fill three critical gaps:

• Increase participation of women and underrepresented populations with health disparities in data science through data science skills training, cross-discipline mentoring, and multi-career level collaborating on research.
• Leverage population science, SDoH, and behavioral Big Data and cloud computing tools to foster a paradigm shift in health disparity, and health and healthcare delivery outcomes research.
• Advance AI bias mitigation and ethical inquiry by developing innovative strategies and securing diverse perspectives.

 

Questions

Questions regarding the Partnerships to Improve Population Health Using AI/ML Program may be directed to AIM-AHEAD Connect HelpDesk.

 

*Calendar dates may be adjusted according to timeline dictated by award.