DBMI Catalyzing Informatics Innovation (CI2) Program

The DBMI Catalyzing Informatics Innovation (CI2) Program supports the development of creative, feasible informatics-based ideas that have the potential to improve health, healthcare, or discovery.   CI2 Program awardees receive not only modest funding to pursue their proposed project, but also join a cohort of CI2 Scholars who work closely with faculty and VUMC leadership. Cohorts learn about using real-world data and evidence to advance innovation, evaluate methods and impact change. CI2 awardees work closely with their mentor and the CI2 Advisors to ensure that their project is moving forward and leading to publications and extramural funding, as appropriate.

Application for the CI2 Program for 2021-2022 are due by July 23, 2021. See the CI2 Application and Selection Information page here.

  • wael Wael Alrifai, MD, MS

    Assistant Professor, Department of Pediatrics
    Assistant Professor, Department of Biomedical Informatics

    Project Summary:

    Our Workload Research Program aims to design, implement and evaluate patient severity of illness and team workload models. We had developed and validated the Vanderbilt Score for Acuity and Resources in Neonates (VSARN), which assesses the workload imposed by individual patients. Supported by the Catalyzing Informatics Innovation, we integrated the VSARN within the electronic health records and we initiated the validation of its use in assessing provider's workload in real-time. We are also evaluating the association between higher VSARN scores and provider-reported burnout symptoms. Our research team is planning on expansion to the VSARN variables to account for patient, provider and health system factors to improve the predictive accuracy. The overall goal of our program is to create a workload dashboard that aids providers and their leaders in making workload distribution fair and equitable. 


     legner Victor Legner, MD, MS

    Associate Professor of Medicine, Division of Geriatric Medicine
    Executive Medical Director, Primary Care Patient Care Center

    Project Summary: Hospitals across the county are challenged with a high inpatient census resulting in issues with capacity. This in turn causes delays in the ability to hospitalize patients in a timely fashion, high emergency room boarding numbers, and diverting patients who require specialized care to other facilities. Fragmentation of care results, which leads to worse clinical outcomes, higher rates of re-hospitalization and increased resource utilization, and decreased patient satisfaction. Developing a system that allows us to care for acutely ill patients at home will allow us to improve capacity in the ED and hospital for more critically ill patients with specialized care needs. It will also improve patient satisfaction as research has shown that people prefer to receive care in their homes.

    This project will allow us to explore how to utilize automated technology to improve the care we provide patients in their homes. We will learn how to monitor at home patients, how to communicate the results of that monitoring in real time, and how to respond to acute issues that arise. Regardless of success or lack thereof, information on how to care for patients in a home hospital setting will be gathered, and this in turn will allow for further and continued improvement in our home delivery of care.


    colinColin Walsh, MD, MA, FAMIA

    Assistant Professor, Department of Biomedical Informatics
    Assistant Professor, Department of Medicine
    Assistant Professor, Department of Psychiatry

    Project Summary:

    Our multidisciplinary team of healthcare providers and behavioral health experts, informaticians, information technology experts, ethicists, statisticians, and more have operationalized our published, validated machine learning approach to predict risk of suicidality following healthcare encounters. We intend to add to VUMC’s existing suicide prevention efforts by implementing Vanderbilt SafeCourse, a technology-enabled suicide prevention program, to help compassionate, smart providers best connect with patients who might be at risk of suicidal thoughts or suicidal self-harm. We hope to seamlessly summarize and share potential risk and protective factors from medical records to support shared decision-making and to direct attention during and between busy clinical encounters through decision support and technology-augmented outreach. Our program emphasizes transparency for patients and providers, that all models are imperfect and smart providers are in charge, and ethical implementation of predictive technology.

     

  • carr thumbnailJeffrey Carr, MD, MSc

    Professor, Department of Radiology
    Professor, Department of Biomedical Informatics

    Project Summary:

    Healthy Liver will identify individuals at risk for liver failure earlier and intervene earlier when prevention is more effective. Obesity affects 40% of US adults. Fatty liver disease is a consequence of obesity, drives hepatic inflammation and fibrosis, a leading cause of liver transplant and can be reversed or delayed with early intervention. Our project utilizes machine learning techniques to measure liver fat and obesity patterns from CT scans of the chest and abdomen. Combine the imaging markers with EMR data and provide a risk estimate for individuals and their healthcare providers. 


    yaaYaa Kumah-Crystal, MD, MPH, MS

    Assistant Professor, Department of Biomedical Informatics
    Assistant Professor, Department of Pediatric Endocrinology

    Project Summary:
    The Vanderbilt EHR Voice Assistant (VEVA) works to enhance the usability of the Electronic Health Record (EHR) through Natural language communication. This initiative allows providers to use spoken commands to query patient information, and order laboratory test on their patients. The VEVA voice user interface provides both visual and spoken summarizations back to the user. 


    landmanBennett A. Landman, PhD

    Professor of Electrical Engineering, Computer Engineering and Computer Science

    Project Summary:

    The RadX project creates the infrastructure necessary for evaluating Radiology AI on n-of-1 trial. RadX leverages VUMC’s ImageVU data archive and secure REDCap interfaces to identify, extract, and process specific imaging sessions of individual subjects within a feasible clinical research workflow. The initial pilot use cases include body lesion detection, liver fat assessment, lung nodule localization, coronary calcium assessment, and functional neuroimaging. 

    See here for more information.


    colinColin Walsh, MD, MA, FAMIA

    Assistant Professor, Department of Biomedical Informatics
    Assistant Professor, Department of Medicine
    Assistant Professor, Department of Psychiatry

    Project Summary:

    Our multidisciplinary team of healthcare providers and behavioral health experts, informaticians, information technology experts, ethicists, statisticians, and more have operationalized our published, validated machine learning approach to predict risk of suicidality following healthcare encounters. We intend to add to VUMC’s existing suicide prevention efforts by implementing Vanderbilt SafeCourse, a technology-enabled suicide prevention program, to help compassionate, smart providers best connect with patients who might be at risk of suicidal thoughts or suicidal self-harm. We hope to seamlessly summarize and share potential risk and protective factors from medical records to support shared decision-making and to direct attention during and between busy clinical encounters through decision support and technology-augmented outreach. Our program emphasizes transparency for patients and providers, that all models are imperfect and smart providers are in charge, and ethical implementation of predictive technology.


     

  • Three CI2 scholars are appointed each year. Eligible faculty members (or advanced postdocs with mentor support) should have a primary or secondary appointment in DBMI.  Preference will be given to more junior faculty members, given the goals of the program.

  • Faculty members will be appointed CI2 Scholars for up to two (2) renewable one (1) year terms. As a part of this program, CI2 scholars will receive $20,000 in supplemental financial support during each term to foster career development and scholarly activity (funds may be used to support research, program costs, or to support participation in courses or meetings that will enhance the scholar's career development). In addition, scholars will identify and work with a mentor for CI2 project development, complementing their existing mentor they have selected for career development. 

  • Appropriate mentors are faculty who have a history of individual success and a track record of successful mentoring. Scholars will select mentors at the time of application. Project mentors may be senior faculty with or without a DBMI appointment. Scholars who complete the program will be asked to serve as mentors-in-training for future scholars by serving on mentoring committees for scholars.

    • Participate in monthly scholar activities (skills workshops, works in progress, symposia)
    • Obtain IRB approval for project (as needed)
    • Meet with VUMC experts to validate project aims and hypotheses
    • Develop project protocol and plan
    • Design and pilot the project
    • Collect relevant data and generate project artifacts
    • Evaluate successes and failures of the project
    • Write up a summary of the project
    • Submit a poster or paper to a national meeting on the project