Fall 2022 Project Awards

Remote state anxiety detection and monitoring using multimodal wearable sensors – Phase 2

Manuel Hernandez, PhD, University of Illinois Urbana-Champaign
Elizabeth Hsiao-Wecksler, PhD, University of Illinois Urbana-Champaign
Jean Clore, PhD, University of Illinois Urbana-Champaign
Richard Sowers, PhD, University of Illinois Urbana-Champaign

We seek to create and validate a novel wearable sensor-based machine learning and artificial intelligence framework for identifying and predicting mental health symptoms in young adults and examine the association between state anxiety with increased stress due to potential stressors in a medical environment.

Patient safety learning laboratory: Human-centered, utility-focused alarm design to preserve maternal lives and health

Abigail Wooldridge, PhD, University of Illinois Urbana-Champaign
William Bond, MD, OSF HealthCare
Shruti Chakravarthy, PhD, University of Illinois College of Medicine Peoria
Jonathan Handler, MD, FACEP, FAMIA, OSF HealthCare
Neelam Verma, PhD, University of Illinois College of Medicine Peoria

The proposed prototype project addresses postpartum hemorrhage, a complex healthcare prediction-alarm-response-intervention challenge. This work will define optimal alarm methods, utility, timing, and presentation capped by simulation testing of alarm integration.

Exposomic equity, a new paradigm: What is it, why is it important and how can we communicate its effect on the health of all communities (using human-centered design)?

Scott Barrows, MA, OSF HealthCare
Ruby Mendenhall, PhD, University of Illinois Urbana-Champaign
Rachel Switzky, MFA, University of Illinois Urbana-Champaign
Noël Adams, MBA, OSF HealthCare
Elise Albers, MBA, MPH, OSF HealthCare
Joe Bradley, PhD, University of Illinois Urbana-Champaign
Leslie McKnight, PhD, Peoria City/County Health Department
Mary Stapel, MD, OSF HealthCare

This project suggests an interactive and innovative way to provide “education to empower.” Also learners, especially youth of color experiencing health disparities, will evaluate their own environmental risks and help assist in the development of new methodologies to change their risks exposures.

Toward automated diagnosis and 3D visualization of seizure onset zones from SEEG, MRI and CT clinical data

Brad Sutton, PhD, University of Illinois Urbana-Champaign
Andres Maldonado, MD, OSF HealthCare
Jennifer Amos, PhD, University of Illinois Urbana-Champaign
Matthew Bramlet, MD, OSF HealthCare
Yogatheesan Varatharajah, PhD, University of Illinois Urbana-Champaign
Michael Xu, MD, OSF HealthCare

Using multimodal imaging combined with stereo electroencephalography (SEEG) to enable localization of the seizure foci and identification of nearby critical anatomy to plan the patient-specific intervention. This will transform epilepsy surgical decision making by translating existing medical data into 3D and 4D visuals; significantly increasing surgeon confidence, intervention planning, and improving patient outcomes.

High-fidelity pediatric heart for surgical simulation of mitral valve replacement

Sameh Tawfick, PhD, University of Illinois Urbana-Champaign
Harma Turbendian, MD, OSF HealthCare
Matthew Bramlet, MD, OSF HealthCare

The team will develop a high-fidelity replication of pediatric heart left ventricle (LV) pulse profile and ventricular end-diastolic volume. They will also complete instrumented platform for the high-fidelity beating pediatric left ventricle which will be connected to a blood circulatory system.

The development of non-wearable leg movement monitoring system with a pressure sensor mat

Sasikanth Gorantla, MD, OSF HealthCare
Yuxiong Wang, PhD, University of Illinois Urbana-Champaign
Kyle Formella, MS, OSF HealthCare

Our project aims to create a convenient novel leg movement monitoring system based on pressure sensing technology, primarily to enhance the assessment of motor symptom burden in restless legs syndrome (RLS) patients.

Microwells-based platform for rapid and simultaneous detection of viruses from milliliters of whole blood

Rashid Bashir, PhD, University of Illinois Urbana-Champaign
Enrique Valera, PhD, University of Illinois Urbana-Champaign
John Farrell, MD, OSF HealthCare

This platform will automate the lab testing to minimize handling and allow for simultaneous detection. This will also minimize the amount of sample required for lab testing.

Correlation of mental health screening and accelerated growth trajectories during the pandemic

Amy Christison, MD, OSF HealthCare
Ana Lucic, PhD, University of Illinois Urbana-Champaign
John Wolff, University of Illinois Urbana-Champaign

There is a bidirectional relationship between mental health concerns and childhood obesity. Therefore the team will develop a propensity risk score calculation using machine learning based on the initial analysis in order to inform clinicians or healthcare systems about at-risk pediatric patients for social-emotional health concerns warranting screening and potential referral.

Algorithmic risk screening for and genetic diagnosis of familial hypercholesterolemia

Roopa Foulger, OSF HealthCare
Jimeng Sun, PhD, University of Illinois Urbana-Champaign
Tinoy Kizhakekuttu, MD, OSF HealthCare
Brent Cross, MS, OSF HealthCare
Jonathan Handler, MD, FACEP, FAMIA, OSF HealthCare
Mauro Montevecchi, MD, OSF HealthCare

The goals of this project are to build an automated process/tool for identifying patients who are eligible for genetic counseling and genetic testing via an algorithmic approach and to evaluate the downstream services completed following patient identification.

Mixed reality (MR) approach to create cyber physical training simulation environments for neonatal procedures

Avinash Gupta, PhD, University of Illinois Urbana-Champaign
Nicole Rau, MD, OSF HealthCare
Muhammad Javed, MD, OSF HealthCare
Harris Nisar, University of Illinois Urbana-Champaign

The goal is to develop a guided MR neonatal training environment for needle thoracentesis: Based on a consolidated curriculum created the team will develop a prototype of a guided MR simulator for this procedure. The team will also optimize the capabilities of the MR system for real time feedback

Developing an easy-to-use application for combatting health misinformation in health care settings

Kevin Leicht, PhD, University of Illinois Urbana-Champaign
ChengXiang Zhai, PhD, University of Illinois Urbana-Champaign
Mary Stapel, MD, OSF HealthCare
Prasanna Balaprakash, PhD, Argonne National Laboratory
James Evans, PhD, University of Chicago
Brant Houston, University of Illinois Urbana-Champaign
Peter Ondish, PhD, University of Illinois Urbana-Champaign
Joseph Yun, PhD, University of Pittsburgh

The goal is to develop an application that provides seamless access to current, health-related social media misinformation for healthcare professionals, and assess ways to integrate updates about health-related social media misinformation into health care professionals’ workflow.

Proof of concept: Intelligent compliance response support system

ChengXiang Zhai, PhD, University of Illinois Urbana-Champaign
John Evancho, JD, OSF HealthCare
Gail Sheehan, OSF HealthCare
Rachel McKinzie, MMBS, OSF HealthCare
George Heintz, MS, University of Illinois Urbana-Champaign

Health care GRC systems are challenged by high regulation density in clinical and administrative environments and frequent regulation amendments. We envision that Natural Language Processing (NLP) techniques can be used to monitor, mine and prioritize reported amendments, make inferences to advance knowledge, and create additional methodologies for automation to accelerate auditing processes to improve Quality, Safety, Privacy Risk Management, and Efficiency.

FlightPath and NeuroDNA: Creating a new interoperability standard for the evaluation of neurocognitive impairment

Adam Cross, MD, OSF HealthCare
Inki Kim, PhD, University of Illinois Urbana-Champaign
Jessica Saw, MD, University of Illinois Urbana-Champaign
Shenlong Wang, PhD, University of Illinois Urbana-Champaign

The current traumatic brain injury testing batteries are primarily paper-based, dependent on language and education, suffer from learning bias, and must be administered by a healthcare professional. This project seeks to address these limitations by developing a new interoperability standard for NCI based on an individual’s ability to track an object within a mixed reality (MR) space and will first test this paradigm as a novel method for the detection and characterization of concussion.

High-impact nurse education and care planning with multiuser, multi-agent virtual reality through a digital-twin ICU

Inki Kim, PhD, University of Illinois Urbana-Champaign
Paul Jeziorczak, MD, OSF HealthCare
Celeste Schultz, PhD, RN-BC, CPNP-PC, University of Illinois Chicago
Samantha Bothwell, MSN, RN, University of Illinois Chicago

Our goal for this project is to establish a new paradigm for nursing education and patient care in intensive care units (ICUs) using a novel digital-twin (DT) platform implemented for multiuser and multi-agent interactions in a virtual world. The resultant DT platform for nursing care in an ICU and new DT-based frameworks for nurse education and care management will provide the first multiuser, multi-agent simulation solution in healthcare to objectively capture, analyze, and understand the dynamic complexity of the ICU work systems.

Telerehabilitation of stroke patients through an adaptive multirobot architecture

Dusan Stipanovic, PhD, University of Illinois Urbana-Champaign
Anne Horowitz, OTR/L, OSF HealthCare
Harris Nisar, University of Illinois Urbana-Champaign

A tele-rehabilitation framework is proposed in this project to facilitate home-based rehabilitation for stoke patients. Both the therapist and the patient would be provided with a robotic device and can perceive a virtual robot being operated in a virtual environment on a screen.

Re-engineering employee onboarding for health care in the digital age

Lisa Barker, MD, OSF HealthCare
George Heintz, MS, University of Illinois Urbana-Champaign
Kyle Formella, MS, OSF HealthCare

Due to the “Great Resignation,” there is opportunity to approach the challenge more broadly by creating a framework that may be used to optimize roles from entry level to leadership. This tool will provide generalizable data collection and analysis that summarizes current state and provides value-based recommendations for improving onboarding process and proof of concept prototype onboarding program to target a high need areas across the Ministry.