Call for Applications

AIM-AHEAD ScHARe Training Program

Equity in Population Health AI: Beyond EHR 

This 8-month collaborative training program between AIM-AHEAD and ScHARe (Science Collaborative for Health Disparities and Artificial Intelligence Bias Reduction) will equip trainees with health equity research and skills to navigate the unique challenges associated with applying AI/ML to population health datasets.

Funding Cycle 2024-2025
Release Date October 18, 2024
Application Due Date

Monday, November 18, 2024. Applications must be received by 11:59 p.m. Eastern Time
Notification of Award January 6, 2025
Program Start Date January 15, 2025
Informational Webinar Schedule

More details to come
Informational Webinar Recording

Check back for links to webinar recordings
Application Link

Step 1: Click here to register as a “mentee/learner” on AIM-AHEAD Connect (our Community Building Platform)

Step 2: Click here to submit a traineeship application for review using InfoReady platform
Project Period

8-month training program
Stipend

$10,000 stipend and $2,000 allowance to attend the AIM-AHEAD Annual Meeting 2025
Mentor(s)

Trainees will receive direct 1:1 support and guidance from an experienced AIM-AHEAD mentor
NIH Biosketch

Applicant NIH biosketch or Curriculum Vitae (CV) is required
Letters of Support

One or two letters from the applicant’s supervisor(s) and faculty
Data Usage Agreement

N/A

Issued by

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

Program Description

Artificial Intelligence (AI)-driven population health initiatives offer invaluable insights into disease trends, risk factors, and treatment effectiveness, facilitating more precise interventions and enhancing patient outcomes. Yet, a critical gap exists in the need for a proficient workforce capable of effectively understanding and leveraging these tools. As such, it is essential to provide training not only to identify and address health disparities, but also to foster a more inclusive and equitable healthcare system. Investing in training in AI-driven population health fosters interdisciplinary collaboration, enabling the development of sustainable solutions to enhance population health.

The central goal of the Equity in Population Health AI: Beyond EHR training program is to expose and train a diverse cohort of individuals (from academia, industry, and community-based organizations) to utilize AI for population health research. We envision empowering participants to contribute meaningfully to the field of AI-driven population health by engaging diverse stakeholders including those from the communities.

To accomplish this objective, diverse professionals committed to utilizing AI for population health research, will complete a 8-month training program that will equip trainees with the health equity research and skills to navigate the unique challenges associated with applying AI including Machine Learning (ML) to population health datasets that are collected using complex sample designs such as clustering, stratification, and weighting. Recognizing the growing emphasis on community engagement in AI/ML, the training program will also include training in community engaged science methods. The training will be done in collaboration with the National Institute of Health (NIH) Science Collaborative for Health Disparities and Artificial intelligence bias Reduction (ScHARe).

Traineeship Objectives

Upon completion, trainees will:

    • Utilize AI/ML tools and methodologies to analyze population health
    • Generate research questions that are best suited to a specific dataset
    • Enhance skills in using cloud computing using Terra Platform
    • Understand the challenges of applying AI/ML to datasets with complex survey design
    • Employ critical thinking and problem-solving in AI to address health disparities
    • Foundational AI skills to promote community engagement in AI/ML
    • Lay the groundwork to collaborate effectively in interdisciplinary teams
    • Apply ethical principles in AI development to promote equity in health outcomes
    • Disseminate AI-driven population health findings through presentations and manuscript publications

The AIM-AHEAD program is partnering with Science Collaborative for Health Disparities and Artificial Intelligence Bias Reduction (ScHARe) to offer AIM-AHEAD stakeholders, trainees, mentees, and consortium partners a training opportunity designed to increase the diversity of researchers skilled in AI/ML by providing training on the utilization AI for population health research, leveraging the ScHARe) Terra workspaces.

ScHARe is powered by Terra, an open-source data analysis platform based on Google Cloud Platform. Terra was developed by the Broad Institute of MIT and Harvard in collaboration with Microsoft and Verily. Using ScHARe’s Terra resources, researchers and their collaborators can access and cross-link the same publicly available or controlled-access data. They can also create secure online spaces for collaboratively running large-scale analyses and sharing reproducible results and resources.

This 8-month training program will engage a cohort of 25 individuals from academia, industry, and community-based organizations in utilizing AI for population health research.  Trainees will develop foundational AI skills for community engagement and applying ethical principles to promote health equity. Specifically, trainees will develop skills to:

    • Formulate population health research questions using publicly available datasets
    • Identify appropriate data sources to answer population health research questions
    • Identify the impact of social determinants of health (SDOH) on population health outcomes, including health disparities
    • Utilize AI and ML tools for analyzing population health and emerging population research methods tailored to specific datasets
    • Use cloud computing, via the Terra Platform

The training is directed particularly toward social scientists conducting research at the intersection of health disparities, to foster skills for interdisciplinary collaborations with basic and clinical scientists. In essence, laying the groundwork for seamless integration of basic, clinical, social, and behavioral science.

Topics that may be addressed using AI/ML applications include areas of health disparities and equity, social determinants of health (SDOH), health behaviors, chronic disease management, health access and utilization, population health trends, and health policy in cancer, cardiovascular, metabolic, and behavioral health but are not limited to the following areas.

    • Examine the impact of SDOH on healthcare access, treatment outcomes, quality, utilization, and costs using AI/ML applications
    • Use AI to detect, predict, and manage physical and mental health conditions in high-risk populations
    • Analyze geographic disparities in healthcare outcomes using AI/ML models to detect and address regional differences
    • Evaluate biases in AI/ML algorithms applied to population health datasets and assess strategies to mitigate these biases for equitable health outcomes
    • Assess the impact of health policies (e.g., the Affordable Care Act, Inflation Reduction Act, Infrastructure Investment and Jobs Act) on population health outcomes by sex, race/ethnicity, age, disability, and rural populations using AI/ML models
    • Use ML methods to predict healthcare utilization and costs
    • Identify low-value care and variations across sex, race/ethnicity, age, disability, and rural populations using AI/ML models
    • Evaluate the role of AI/ML in predicting cancer treatment outcomes
    • Examine how AI/ML models can improve early prediction of cardiometabolic diseases (e.g., diabetes, hypertension) using population-level health data
    • Examine how AI/ML models can identify and mitigate biases in mental health diagnosis including substance use and treatment.

Program Expectations and Benefits

All training offerings will be virtual with the exception of the AIM-AHEAD Annual Meeting 2025. Trainees are expected to:

    • Attend all virtual training sessions, both synchronous and asynchronous, including webinars and seminars
    • Work on the program an average of at least 6 hours per week
    • Engage with an AIM-AHEAD Mentor (to be assigned through the program)
    • Engage in learning communities and peer networking
    • Participate in ScHARe “thinkathon” webinars and training related to Terra Platform
    • Complete Hands-on Data Analysis exercises designed related to data manipulation, statistical analysis, and machine learning algorithms using ScHARe datasets
    • Participate in scheduled workshops (e.g. data sets; answering research questions, study designs, and how to handle complex survey designs with clustering, stratification, and weighting in AI/ML analysis; mentoring; other professional development)
    • Utilize AIM-AHEAD HelpDesk support
    • Work on a team-based research project to present a work-in-progress research poster at the AIM-AHEAD Annual Meeting 2025
    • Play an active part in the AIM-AHEAD community

Each trainee will receive:

    • A stipend of $10,000 which will be disbursed in installments based on trainee completion of required milestones*
    • An allowance of $2,000 to cover the cost of airfare, hotel accommodations, local transportation and per diem to attend the AIM-AHEAD Annual Meeting 2025
    • Support and guidance from an experienced AIM-AHEAD mentor
    • Support from the AIM-AHEAD Data Science Training Core
    • Direct 1:1 guidance, virtual office hours, HelpDesk support, and concierge services supporting users
    • Training on:
      • Leveraging AI for population health research and decision making
      • Advancing AI bias mitigation and ethical inquiry
      • Use of Terra platform for data analysis
      • AI and Cloud Computing
      • Approaches to complex survey designs with clustering, stratification, and weighting in AI/ML analysis
      • Approaches to effectively engaging community stakeholders
      • Empowerment, Knowledge, and skills, including mentorship, to engage in utilizing AI for population health research
      • Hypothesis development for population health
      • Developing a team-based project on the use of AI/ML to Research projects will focus on the use of AI/ML to address disparities and minority health in areas such as behavioral health, cardiometabolic health, and cancer

*Trainees may apply to multiple AIM-AHEAD training programs and fellowships, but if accepted into more than one, they must select only one AIM-AHEAD training program to participate in per funding cycle. Additionally, AIM-AHEAD-funded Coordinating Center Personnel (MPIs, PIs, Co-Investigators, other personnel) are eligible to apply, but if accepted, they will not receive the stipend and allowance.*

Trainee Mentorship

Each awarded trainee will receive career and research mentorship from experienced, skilled investigators selected from AIM-AHEAD core members. The online mentoring platform AIM-AHEAD Connect (https://connect.aim-ahead.net) will be used to match mentors with awarded trainees and for mentor/trainee engagement and progress tracking.

Eligibility Criteria

  1. Applicants must be:
    1. U.S. Citizens, Permanent Residents, or Non-Citizen U.S. Nationals
    2. Able to submit Form W-9 (Request for Taxpayer Identification)
    3. Affiliated with one of the following entities:
      • Higher Education Institutions
        • Public/State Controlled Institutions of Higher Education8
        • Private Institutions of Higher Education
        Individuals affiliated with the following types of Higher Education Institutions are always encouraged to apply:
        • Hispanic-serving Institution
        • 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 (Other than Institutions of Higher Education)
        • Nonprofits without 501(c)(3) IRS Status (Other than Institutions of Higher Education)
      • For-Profit Organizations
        • Small Businesses
        • For-Profit Organizations (Other than Small Businesses)
  2. Education: Applicants must hold at a minimum a bachelor’s degree from an accredited institution in one of the following or related fields:
      • Public health (example: epidemiology, biostatistics, health administration, clinical implementation specialists)
      • Social and Behavioral Sciences (example: economics, sociology, business administration, psychology, health services/administration, political science, demography)
      • Health sciences (example: pharmacy, psychology, health information technology, nurses, therapists, social workers, physicians’ assistants)
      • Population Health
      • Community Health Workers
      • Biological or life sciences (example: biology, zoology, biochemistry, microbiology)
      • Mathematics or statistics
      • Data Science and Engineering
  3. Recommended Applicant Knowledge, Skills, and Experience:

    To ensure success in the training program, applicants must possess certain prior knowledge, skills, and experience These include:

    • A working command of English, as all training will be conducted in English

    Additionally, it is strongly recommended that applicants have some of the following skills and experiences to optimize their learning experience and better prepare them for the challenges of the program.

    • Successfully completed an undergraduate or graduate course in probability and statistics

    Introductory or refresher courses on these topics will be available to successful applicants at the start of the traineeship, via the AIM-AHEAD Connect platform.

AIM-AHEAD Interest in Promoting Diversity

The goal of the AIM-AHEAD Coordinating Center is to promote diversity in the research workforce in AI/ML. Research shows that diverse teams working together and capitalizing on innovative ideas and distinct perspectives outperform homogeneous teams. Scientists and trainees from diverse backgrounds and life experiences bring different perspectives, creativity, and individual enterprise to address complex scientific problems. There are many benefits that flow from a diverse NIH-supported scientific workforce, including: fostering scientific innovation, enhancing global competitiveness, contributing to robust learning environments, improving the quality of research, advancing the likelihood that underserved populations and those that experience health disparities and inequities participate in and benefit from health research, and enhancing public trust. See the NIH Interest in Diversity (NOT-OD-20-031: Notice of NIH's Interest in Diversity). Individuals from diverse backgrounds, including those from the following underrepresented groups are encouraged to apply for the traineeship:

A. Individuals from racial and ethnic groups that have been shown by the National Science Foundation to be underrepresented in health-related sciences on a national basis (see data at http://www.nsf.gov/statistics/showpub.cfm?TopID=2&SubID=27 and the report Women, Minorities, and Persons with Disabilities in Science and Engineering). The following racial and ethnic groups have been shown to be underrepresented in biomedical research:

  1. Blacks or African Americans
  2. Hispanics or Latinos,
  3. American Indians or Alaska Natives,
  4. Native Hawaiians, and other Pacific Islanders.
  5. In addition, it is recognized that underrepresentation can vary from setting to setting; individuals from racial or ethnic groups that can be demonstrated convincingly to be underrepresented by the grantee institution should be encouraged to participate in NIH programs to enhance diversity. For more information on racial and ethnic categories and definitions, see the OMB Revisions to the Standards for Classification of Federal Data on Race and Ethnicity (https://www.govinfo.gov/content/pkg/FR-1997-10-30/html/97-28653.htm).

B. Individuals with disabilities, who are defined as those with a physical or mental impairment that substantially limits one or more major life activities, as described in the Americans with Disabilities Act of 1990, as amended. See NSF data at https://www.nsf.gov/statistics/2017/nsf17310/static/data/tab7-5.pdf.

C. Individuals from disadvantaged backgrounds, defined as those who meet two or more of the following criteria:

  1. Were or currently are homeless, as defined by the McKinney-Vento Homeless Assistance Act (Definition: https://nche.ed.gov/mckinney-vento/);
  2. Were or currently are in the foster care system, as defined by the Administration for Children and Families (Definition: https://www.acf.hhs.gov/cb/focus-areas/foster-care);
  3. Were eligible for the Federal Free and Reduced Lunch Program for two or more years (Definition: https://www.fns.usda.gov/school-meals/income-eligibility-guidelines);
  4. Have/had no parents or legal guardians who completed a bachelor’s degree (see https://nces.ed.gov/pubs2018/2018009.pdf);
  5. Were or currently are eligible for Federal Pell grants (Definition: https://studentaid.gov/understand-aid/types/grants/pell) ;
  6. Received support from the Special Supplemental Nutrition Program for Women, Infants and Children (WIC) as a parent or child (Definition: https://www.fns.usda.gov/wic/wic-eligibility-requirements).
  7. Grew up in one of the following areas:
    1. A US rural area, as designated by the Health Resources and Services Administration (HRSA) Rural Health Grants Eligibility Analyzer (https://data.hrsa.gov/tools/rural-health), or
    2. A Centers for Medicare and Medicaid Services-designated Low-Income and Health Professional Shortage Area (qualifying zip codes are included in HRSA file link above). Either of these two criteria can serve as a criterion for the disadvantaged background designation.

Note: Only one of the two areas under #vii can be used as a criterion for the disadvantaged background definition.

Students from low socioeconomic (SES) status backgrounds have been shown to obtain bachelor’s and advanced degrees at significantly lower rates than students from middle and high SES groups (see https://nces.ed.gov/programs/coe), and are subsequently less likely to be represented in biomedical research. For background see Department of Education data at, https://nces.ed.gov/; https://www2.ed.gov/rschstat/research/pubs/advancing-diversity-inclusion.pdf.

D. Literature shows that women from the above backgrounds (A, B, and C) face particular challenges in scientific fields at the graduate level and beyond. (See, e.g., From the NIH: A Systems Approach to Increasing the Diversity of Biomedical Research Workforce https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5008902/).

Women are known to be underrepresented in doctorate-granting research institutions at senior faculty levels in most biomedical-relevant disciplines and may also be underrepresented at other faculty levels in some scientific disciplines (See data from the National Science Foundation National Center for Science and Engineering Statistics: Women, Minorities, and Persons with Disabilities in Science and Engineering, special report available at https://www.nsf.gov/statistics/2017/nsf17310/, especially Table 9-23, describing science, engineering, and health doctorate holders employed in universities and 4-year colleges, by broad occupation, sex, years since doctorate, and faculty rank).

Upon review of NSF data, and scientific discipline or field related data, NIH encourages institutions to consider women for faculty-level, diversity-targeted programs to address faculty recruitment, appointment, retention or advancement.

Application Process

Applications must be submitted during the open application period (10/18/24 - 11/18/24). Applications should address the requirements below and any additional questions via the Traineeship Application Form. The application should be understandable to readers from outside the applicant’s field of study and must clearly present the project aims, applicable studies already completed, methods, materials, and AIM-AHEAD engagement plan.

Requirements

Profile Information

    • Provide your name, organization, department, position title, research area, email address, and profile web page
    • Please address on InfoReady the profile and prior experience questions

 Letters of Support 

    • One signed letter of support from the applicant’s supervisor/mentor is required. Letter of support must include the referee’s contact information (full name, position title, organization, email/phone number, and signature)
    • Minimum of one letter of recommendation (one additional letter may be provided) is required from faculty who taught the applicant and who can attest to the applicant’s preparedness, aptitude, and rationale for advanced data analysis training. Reference(s) should highlight relevant skills and accomplishments

The support letter and the letter of recommendation can be from the same person. In this case, only one letter is needed.

Academic Transcript

Academic transcripts (official or a photocopy) must be included from the applicant’s undergraduate and, if applicable, graduate programs for current students and postgraduates.

Biographical Sketch of the Applicant

Statement of Rationale for Pursuing Training

Provide a personal statement of not more than 500 words addressing the following:

    • Describe what you hope to accomplish through the Equity in Population Health AI: Beyond EHR Training Program.
    • Provide your rationale and need for training on utilizing AI/ML tools and methodologies to analyze population health and acquire these skills.
    • Describe your familiarity with, and/or interest, in AI/ML analysis, programming, community engagement, public or population health, and cloud-based analytics.
    • Explain how you plan to apply the training to achieve your long-term career advancement and work in population health.

Application Submission using AIM-AHEAD Connect and InfoReady platform

Step 1: Click here to register as a “mentee/learner” on AIM-AHEAD Connect (our Community Building Platform). Your AIM-AHEAD Connect profile will allow you to log in to submit your application on the InfoReady platform.

Step 2: Click here to submit an application for review using the InfoReady platform*. (Insert application link)

* To submit your application in InfoReady, please use Chrome, Firefox, or Edge. If you're using Safari, make sure to clear your cache before logging in.

Please note, both of the above steps must be completed for consideration.

All applications must be received by November 18, 2024, at 11:59 p.m. Eastern Time

Trainee Selection

Trainee Selection

A review committee comprised of AIM-AHEAD Consortium members and other experts will evaluate and prioritize applications using the following criteria:

Review Criteria

In assigning priority scores, reviewers will apply the standard NIH 1-9 scoring range to Criteria 1, where a score of 1 indicates highest enthusiasm, and a score of 9 indicates lowest enthusiasm, based on NIH Simplified Review Framework - https://grants.nih.gov/grants/guide/notice-files/NOT-OD-24-010.html

Criteria

The applicant:

    • Clearly articulates expectations and reasons for participating in the program
    • The applicant also demonstrates the need for and importance of acquiring the training to address population health, health disparities and equity, social determinants of health (SDOH), health behaviors, chronic disease management, health access and utilization, population health trends, and health policy
    • Has the background and motivation to participate in and benefit from the training
    • Demonstrates a 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
    • Describes specific plans for long-term application of the training to his/her research program and/or professional development

Notification of Awards

Applicants should expect notification of their acceptance status by Monday, January 6, 2025. Accepted applicants will receive an invite from PaymentWorks requesting:

Informational Webinar

More details to come.

Inquiries

AIM-AHEAD ScHARe Training Program Directors

Co-Directors: Usha Sambamoorthi, PhD, Damaris Javier, Jamboor K. Vishwanatha, PhD

MPIs: Bettina Beech, PhD, Evelinn Borrayo, PhD, Alejandra Casillas, PhD, Anil Shanker, PhD

Frequently Asked Questions

Please refer to the FAQs below before submitting a help desk ticket:

AIM-AHEAD ScHARe Training Program FAQs

Please feel free to submit a help desk ticket if you have any questions:

AIM-AHEAD Training Programs HelpDesk

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