Recently Funded Projects - ARCHES
Jump ARCHES Awards Grants for Spring 2023
Twelve research projects are sharing more than $1,192,000 in funding through the Jump ARCHES research and development program to focus on the following areas:
- Develop novel technologies and new automations which adopt and incorporate individual health data, diagnostic results and data analytics to provide individuals with tailored roadmap to drive improved health, and where appropriate, help them navigate the healthcare system.
- Addressing the evolving standards of care to incorporate personalized precision medicine and genomic best practices.
- Address accuracy and completeness of diagnosis, treatment, and health literacy of historically underserved populations.
- Alleviate administrative burden at the bedside to increase the quality of patient interactions.
- Improve the ease of digital connection to healthcare.
- Assist in diagnosis and treatment of Neurological Disorders through collaborative efforts with the OSF Children’s Hospital of Illinois and Illinois Neurological Institute. Emphasis will be on early diagnosis of neurological disorders, enhancement of social skills associated with chronic neurological disorders, support of children and adults with chronic neurological disorders as they navigate everyday life at home, in school, and in the workplace, and exploration of technologies to utilize unique skills of individuals with neurological disorders.
- Explore the needs of people with visible and non-visible disabilities
- Special attention will be given to proposals addressing
- Social and behavioral sciences topics, especially related to issues involving inequity and poverty, social and behavioral health, and the digital revolution (for more information on these topics, see the grand challenges at the Center of Social and Behavioral Sciences).
- Neurological sciences-particularly assistive technologies
- Autism spectrum disorders
- Social and behavior influences on the cancer continuum
- Outstanding proposals in additional areas affecting health and wellness may be considered for funding.
The Jump ARCHES program is a partnership between OSF HealthCare, the University of Illinois Urbana-Champaign (UIUC) and the University of Illinois College of Medicine in Peoria (UICOMP).
The funding supports research involving clinicians, engineers and social scientists to rapidly develop technologies and devices that could revolutionize medical training and health care delivery. A requirement of the grant applications was for solutions that could be deployed quickly, within four to six weeks. Investigators were also encouraged to consider how to best mitigate the impact of age, location, and social barriers in delivering quality health care to vulnerable populations.
Spring 2023 Project Awards
STREAM-ED: Simulation to Refine, Enhance and Adapt Management of Emergency
- Hyojung Kang, PhD, University of Illinois Urbana-Champaign
- William Bond, MD, OSF HealthCare
This study aims to develop practical models combining machine learning, discrete event simulation, and optimization techniques to improve emergency department (ED) resource utilization and address ED overcrowding, which is exacerbated by the COVID-19 pandemic and staffing shortages.
Prototype: Intelligent Regulatory Change Management System
- ChengXiang Zhai, PhD, University of Illinois Urbana-Champaign
- Scott Lowry, MHA, CHC, CCEP, OSF HealthCare
This study proposes an Intelligent Regulatory Change Management (IRCM) System that uses natural language processing and artificial intelligence to track and evaluate public policy actions governing OSF HealthCare. This will enable compliance professionals to identify critical changes and determine appropriate courses of action, reducing manual review and improving quality, safety, privacy risk management and efficiency.
Machine Learning of Standardized DICOM Metadata from Imaging Datasets
- Matthew Bramlet, MD, OSF HealthCare
- Brad Sutton, PhD, University of Illinois Urbana-Champaign
The project aims to develop a machine learning-based algorithm that can categorize image parameters directly from signal intensity variations of 2D medical images to enable efficient pipelines for medical image segmentation. The proposed algorithm is expected to estimate patient and image-acquisition information by utilizing machine learning methods in situations where the DICOM header fields are incomplete or unreliable, ultimately allowing for automated characterization of unknown 3D DICOM imaging datasets.
Machine-Guided Staging of Neuroblastic Tumors of Patient Specific 3D Models
- Daniel Robertson, MD, OSF HealthCare
- Brad Sutton, PhD, University of Illinois Urbana-Champaign
The OSF HealthCare Children’s Hospital of Illinois is using segmentation services to create 3D models of neuroblastic tumors for pre-surgical planning. The hospital aims to transition from 2D imaging to 3D modeling to increase the reproducibility of staging analysis, establish a new standard for segmented models of neuroblastic tumors and develop machine-guided tools that can improve upon and automate current recommended image-defined risk factors staging.
Toward Machine-Learned Aortic Arch Measured Diameters
- Matthew Bramlet, MD, OSF HealthCare
- Brad Sutton, PhD, University of Illinois Urbana-Champaign
The original project aims to automate the segmentation and clinical measurement of aortic arch diameters from MRI imaging. The researchers leading this project have successfully completed several steps, including de-identification and curation of datasets, manual segmentation and the development of a novel method for automatically analyzing each aortic arch with promising results, indicating correlation between the automated and clinically derived measurements.
A Field Experiment to Evaluate the Efficacy of Convenient Health Kiosks
- Ann Willemsen-Dunlap, CRNA, PhD, OSF HealthCare
- Ujjal Mukherjee, PhD, University of Illinois Urbana-Champaign
This proposal outlines a field experiment to evaluate the efficacy of health kiosks supported by community health workers (CHWs) in delivering first line preventive health screenings to rural and underserved communities. The project is intended to lead to large-scale development and deployment of health kiosks with the goal of positively impacting social determinants of health and long-term health status of those served.
Contextualizing Nursing Needs for Development of Retention-Support App
- Ann-Perry Witmer, PhD, University of Illinois Urbana-Champaign
- Sheryl Emmerling, PhD, Rn, NEA-BC, OSF HealthCare
The goal of this project is to address the high turnover rate of new nurses by providing a digital app that offers personalized nursing support. The Contextual Engineering (CE) paradigm will be used to assess the needs and values of first-year nurses, including those who have left their positions, to inform the development of the app in the first phase of the project, with the goal of stabilizing the nursing staff, improving the quality of service and reducing operating costs.
Community Health Café: Engaging Digital Innovation and Community-Based Resources to Enhance Health Equities in Underserved Communities
- Scott Barrows, MA, OSF HealthCare
- Joe Bradley, PhD, MA, University of Illinois Urbana-Champaign
The purpose of the community health café is to provide digital access to health and health care resources, including links for assistance with the social determinants of health, health education and connections to public health in underserved communities. The eventual goal is a Medicaid telemedicine option with OSF OnCall. This proposal aims to address critical needs of underserved residents in vulnerable communities and is crucial for their health.
AI-Powered Brain Tumor Segmentation
- Zhi-Pei Liang, PhD, University of Illinois Urbana-Champaign
- Matthew Bramlet, MD, OSF HealthCare
This project aims to enhance the detection and monitoring of brain diseases. Phase 1 of the project focuses on accurate delineation and segmentation of brain tumors using a combination of structural and molecular multimodal brain imaging data and deep learning. The proposed work includes developing brain atlases for AI-powered brain image analysis, computational tools for automated tumor detection and segmentation and evaluating potential clinical applications.
Optimizing Pharmacologic Management of Behaviors in Patients with Autism
- Adam Cross, MD, FAAP, OSF HealthCare
- Ravishankar Iyer, PhD, University of Illinois Urbana-Champaign
This proposal aims to provide physicians with a machine learning model that assists in selecting appropriate medication and dosage strategies for patients with Autism Spectrum Disorder (ASD). By incorporating patient history, genetic information and clinician notes, the model will dynamically adapt the treatment protocol as the patient progresses, ensuring optimal choices for improved behavioral symptoms with a high degree of confidence.
Predicting Medication Non-Adherence in Type 2 Diabetes
- Hyojung Kang, PhD, University of Illinois Urbana-Champaign
- Mary Stapel, MD, OSF HealthCare
Medication adherence is crucial for managing diabetes, but disparities exist, particularly among racial/ethnic minorities and those with lower socioeconomic status. This proposal aims to use data-driven models to identify high-risk individuals and areas for non-adherence to diabetes medication, develop and validate prediction models and implement and evaluate them in clinical practice.
Knowledge Graph Construction with Large Language Models to Predict DKA Occurrence and Severity
- Adam Cross, MD, FAAP, OSF HealthCare
- Jimeng Sun, PhD, University of Illinois Urbana-Champaign
Diabetic ketoacidosis (DKA) hospitalizes over 50,000 American children annually, with underprivileged and underserved children at higher risk. This proposal aims to develop a predictive model using patient-specific knowledge graphs generated from clinical data extracted through name entity recognition and language modeling. Clinicians can use the model to identify high-risk diabetic patients and prevent DKA.
Spring 2024
Generative AI-Based Simulation of Electroencephalogram (EEG) to Formulate Personalized Migraine Therapy
- Christopher Gondi, PhD, OSF HealthCare
- Inki Kim, PhD, University of Illinois Urbana-Champaign
This project aims to improve migraine management through personalized neuromodulation therapy by generating patient-specific EEG signals. Key objectives include simulating EEG signals of migraine patients using generative AI and EEG-fMRI data for epilepsy, which will be validated against known biomarkers. The project will also create a diverse pool of EEG signals reflecting individual migraine patients’ profiles for predictive signal generation and clinical validation. Additionally, an adaptive neuromodulation protocol for electrical brain stimulation will be developed, reviewed by clinical experts and simulated to assess post-treatment effects. Ultimately, the project will create the first AI neurovascular research database, enabling personalized treatment and more accurate migraine diagnosis, which will improve patient care and reduce treatment disparities.
4D Interpolation of Cardiac MRI Imaging Sequences
- Matthew Bramlet, MD, OSF HealthCare
- Brad Sutton, PhD, University of Illinois Urbana-Champaign
This project aims to develop a 4D beating heart visualization using standard cardiac magnetic resonance (CMR) imaging data, addressing the current limitations of 2D imaging in congenital cardiac conditions. The goal is to create a 4D heart representation, which could be reviewed in virtual reality (VR) to enhance diagnostic and analytical capabilities. By interpolating time-sequential 3D data from CMR scans, the project seeks to automatically segment the heart’s anatomy and generate a dynamic 4D model. This could improve cardiac assessments, automate analysis of ventricular volumes, and enable more accurate evaluation of complex heart structures like valves, transforming current diagnostic approaches and enabling more precise, patient-specific care.
Tailored Diabetic Wound Care Educational Tool
- Ann Willemsen-Dunlap, PhD, CRNA, OSF HealthCare
- Mae Vogel, MS, University of Illinois Urbana-Champaign
The goal of this project is to create a personalized educational tool for diabetes patients in outpatient and home care settings, with an emphasis on comprehensive wound care. The project aims to:
- Identify challenges in proper wound care at home and explore patients' preferred educational methods.
- Design and develop personalized educational materials delivered via preferred platforms.
- Evaluate prototype tools through usability studies.
A patient-centered approach will guide development, involving stakeholder interviews, participatory design and iterative feedback. Phase 2 funding will scale the tool for rural care settings. This project aligns with Jump ARCHES goals to improve patient outcomes and set new standards in diabetes education.
Mixed Reality Simulator for Neonatal Needle Thoracentesis
- Nicole Rau, MD, MS, OSF HealthCare
- Avinash Gupta, PhD, University of Illinois Urbana-Champaign
This project aims to develop a Mixed Reality (MR) simulation platform for neonatal procedures using the Microsoft HoloLens 2. It addresses the need for accessible training in community hospitals where general practitioners or emergency providers often lack experience in high-risk neonatal procedures. The MR platform offers realistic training by combining haptic feedback from physical mannequins with real-time guidance and feedback from the MR system. The initial focus is on needle thoracentesis for treating pneumothorax in neonates, a procedure with a high mortality risk. Phase 1 will evaluate usability and engagement with experts, refine the simulator for newer, lower-cost devices and create a video database for neonatal procedures, with plans to transition to marker-free tracking.
Empowering the Diagnosis of Rare Diseases in Primary Care Settings
- Adam Cross, MD, OSF HealthCare
- Jimeng Sun, PhD, University of Illinois Urbana-Champaign
Undiagnosed rare diseases present a significant challenge in health care, often leading to delayed treatment and worse patient outcomes. This project aims to develop an innovative system that integrates machine learning and knowledge graphs to detect these diseases through electronic medical records (EMR). By identifying patterns and correlations within EMRs, the system will enable the early detection of rare diseases, directly addressing the operational challenges in health care. This aligns with the Jump ARCHES program’s goal of supporting projects that design, implement and evaluate innovative health solutions. Ultimately, the project aims to enhance diagnostic precision and improve patient outcomes by leveraging advanced technologies to tackle undiagnosed rare diseases.
Development of an Active Wheelchair Seat Cushion to Address Pressure Ulcers
- Britny Coyle, OSF HealthCare
- Holly Golecki, PhD, University of Illinois Urbana-Champaign
This project aims to address pressure ulcer prevention and treatment in wheelchair users, particularly those with limited mobility due to conditions like ALS, aging or injury. Pressure ulcers, which affect millions annually, are linked to serious health complications and can lead to life-threatening infections. Current seat cushions offer limited solutions, and users often struggle to maintain recommended repositioning schedules. By applying human-centered design, the project seeks to develop an innovative, soft robotic cushion with custom pneumatic bladders and lightweight electronics for real-time sensing and control. The goal is to create a prototype that enables users to autonomously alleviate pressure, reducing ulcer occurrence, pain and treatment time.
From Angiograms to Simulated Flow in Cerebral Vasculature
- Matthew Bramlet, MD, OSF HealthCare
- Brad Sutton, PhD, University of Illinois Urbana-Champaign
This project aims to develop a fully automated approach for detecting and assessing intracerebral aneurysms using 3D vascular models derived from MR and CT angiography. By simulating blood flow through these models, the project seeks to identify areas of physiological disruption that could indicate a higher risk of rupture, particularly in small aneurysms that fall below current intervention thresholds. The project aims to:
- Create an automated algorithm for generating 3D vascular models.
- Simulate blood flow and detect regions requiring further monitoring.
The outcome will improve surgical planning and enable long-term follow-up through precise, patient-specific simulations, potentially enhancing current risk assessment methods.
Low-Cost Haptic Enhancement for Surgery via Stochastic Resonance
- Richard Anderson, MD, OSF HealthCare
- Caroline Cao, PhD, University of Illinois Urbana-Champaign
The aim of this project is to develop a wearable device that enhances surgeons' haptic sensitivity during remote surgery. The device will utilize subthreshold vibration signals through an armband to activate the surgeon's haptic system, leveraging the phenomenon of stochastic resonance (SR) to improve tactile sensitivity. The primary goal is to assess how long the SR after-effect persists after vibration stimulation, potentially allowing the device to be removed before surgery. If successful, the project could lead to a cost-effective, compact device that improves surgeon performance, with applications in both minimally invasive surgery and telemedicine.
Optimizing the Utilization of Rural Health Clinics
- Ann Willemsen-Dunlap, CRNA, PhD, OSF HealthCare
- Yanfeng Ouyang, PhD, University of Illinois Urbana-Champaign
This project aims to optimize the utilization of rural clinics, including mobile and stationary microsites, by addressing key challenges in transportation, service knowledge, trust and accessibility. The focus is on creating a transportation service concept that enhances clinic access by considering factors like clinical service availability, population distribution and resource limitations. The project will categorize populations based on health care needs and tailor clinic visit behaviors accordingly. Deliverables include operational plans, mobility services for patient access, cost estimations and an implementation guide. A pilot in Cuba, Illinois will assess effectiveness, with future scalability in mind for other regions and health organizations.
Sudden Cardiac Arrest AI ECG Recognition
- Sunita Ferns, MD, MRCPCH, FHRS, CEPS-P, OSF HealthCare
- Jim Rehg, PhD, University of Illinois Urbana-Champaign
This project aims to develop an AI-based approach to improve risk stratification for sudden cardiac arrest (SCA) in young individuals, a leading cause of mortality in otherwise healthy individuals. Current electrocardiogram (ECG) screenings, which rely on highly trained experts, are limited in scalability and accuracy. Automated ECG software lacks the necessary sensitivity and specificity, often resulting in missed diagnoses or unnecessary tests. This project will address two key challenges: the lack of appropriate datasets for young individuals (ages 12-25) and the difficulty of applying deep learning to rare conditions with limited data. The research will leverage diverse datasets, particularly from OSF Children's Hospital of Illinois and OSF HealthCare hospitals, to develop and validate AI models. Recent advances in self-supervised learning will allow the use of large-scale ECG data to detect rare conditions, improving the model’s performance. This project represents the first application of AI for pediatric ECG analysis and aims to identify key patterns related to SCA risk in young people. The outcomes will have global implications, especially in low-resource settings with limited access to expert cardiologists, potentially saving lives through more accurate ECG diagnoses.
Detection of E. coli Bypassing Blood Culture
- John J. Farrell, MD, OSF HealthCare
- Rashid Bashir, PhD, University of Illinois Urbana-Champaign
This project aims to develop a rapid, nucleic acid amplification-free assay for detecting low E. coli counts in whole blood in under two hours. Currently, detecting small numbers of pathogens in blood requires time-consuming blood cultures, which can delay diagnosis and treatment of bacteremia and sepsis. Existing non-culture detection methods are costly and slow, limiting their use in resource-limited settings. The assay combines CRISPR-cascade amplification with a biphasic reaction to enhance detection sensitivity. In Aim 1, the project will optimize the assay for whole blood. Aim 2 involves testing the assay with clinical E. coli samples from UIUC. Aim 3 will focus on a pilot clinical study to further validate the method.
Multi-Modality Super-Resolution Brain Imaging for Neonates
- Daniel J. Robertson, MD, OSF HealthCare
- Yun-Sheng Chen, PhD, University of Illinois Urbana-Champaign
This proposal focuses on addressing maternal and child health by developing an innovative transcranial super-resolution Doppler imaging technique for diagnosing neonatal encephalopathy. Engineers from University of Illinois Urbana-Champaign ECE and doctors from OSF neonatology will collaborate to create a new imaging method aimed at improving early diagnosis and management of hypoxic-ischemic neonatal encephalopathy (HIE), a severe condition caused by brain oxygen deprivation during birth. While current imaging methods like MRI and CT provide valuable diagnostic information, ultrasound, despite its benefits, has limitations in detecting subtle brain injuries and assessing brain function. The goal of this research is to develop a portable, non-ionizing, multi-modality photoacoustic (PA)/super-resolution ultrasound-localization (UL)/ultrasound imaging technique with enhanced resolution to identify early brain abnormalities caused by HIE.
The project aims to:
- develop a deep-learning workflow for transcranial super-resolution cerebral blood flow imaging.
- investigate the feasibility of using this imaging method for early HIE diagnosis in a rat model.
Ultimately, this technology will provide a safer, more accessible diagnostic tool for HIE, enabling early intervention and improved management of affected infants.
Development of Virtual Nasendoscopy from 3D Dynamic MRI
- Matthew Bramlet, MD, OSF HealthCare
- Brad Sutton, PhD, University of Illinois Urbana-Champaign
This project aims to improve the diagnosis and treatment of pediatric oropharyngeal dysphagia by developing a virtual nasoendoscope using advanced dynamic MRI. Traditional diagnostic tools like nasoendoscopy and videofluoroscopy have limitations, such as discomfort, invasiveness and radiation exposure. The goal is to use MRI to generate 4D imaging data of swallowing dynamics, automating segmentation of relevant anatomical structures and translating it into a VR format. This would allow clinicians to visualize swallowing in 3D, offering better diagnostic insight and potentially eliminating the need for radiation-heavy or invasive procedures. If successful, this technique could revolutionize dysphagia care with safer, more comprehensive imaging methods.
Real-Time Biomarker Assessment Platform for Building Trust in Health Care and Research
- Sameer Ansar, MD, OSF HealthCare
- Jacinda K. Dariotis, PhD, MAS, University of Illinois Urbana-Champaign
This project focuses on developing a palm-sized, point-of-care device for rapid, enzyme-free salivary biomarker testing, providing real-time, highly sensitive results during a single visit. The device aims to improve participant engagement in both clinical and research settings by delivering immediate results, minimizing concerns about sample handling and promoting greater trust in biosocial and health research. The project has three main objectives:
- Develop single-step, enzyme-free assays for quantifying salivary biomarkers, enhancing point-of-care assessments in social, behavioral and health research.
- Create and validate a real-time assay for biomarkers like oxytocin, C-reactive protein and inflammatory cytokines, which will support community-based interventions targeting behaviors such as mother-child attachment and sexual risk-taking.
- Evaluate the acceptability and uptake of the device in community and point-of-care settings, aiming to build trust and foster curiosity about science and health care among underserved populations.
Ultimately, this technology seeks to improve health care experiences, increase participant engagement and enable personalized, timely interventions through real-time biomarker testing.
Implementation of Health Access Points Within Rural Health Ecosystem of OSF HealthCare
- Ann Willemsen-Dunlap, CRNA, PhD, OSF HealthCare
- Ujjal Kumar Mukherjee, PhD, University of Illinois Urbana-Champaign
This proposal addresses key health care challenges faced by underserved rural communities, including limited access to health care infrastructure, low awareness of preventative care and decreased health literacy. Despite efforts like telemedicine and policy interventions, rural areas continue to struggle with health care access, partly due to poor connectivity and low technology adoption. Community health workers (CHWs) have proven effective in improving telehealth usage and health literacy but are resource-intensive in geographically dispersed areas. This project proposes a low-cost solution by creating small-scale health access points, or kiosks, located in community hubs like churches, food pantries and libraries. These kiosks will provide basic health services, such as screenings, health literacy materials and telehealth access, bridging the technology gap and connecting underserved populations to OSF services. The project will develop and integrate kiosks with existing OSF technology, focusing on affordability, compliance with health regulations and usability for diverse populations. The work involves these steps:
- Developing a prototype kiosk integrated with OSF technology.
- Creating documentation and a comprehensive implementation playbook
- Conducting field trials at two locations to evaluate the kiosk's effectiveness and functionality.
The expected outcomes include the prototype, documentation, playbook and results from the trials, aiming to improve healthcare access and outcomes in rural communities.
