Jump ARCHES Fall 2021 Funded Grants
Latest Jump ARCHES Awards Focus on Medical Tech Solutions to Meet Vexing Health Care Challenges
Twenty research projects are sharing slightly more than $1.4 million in funding through the Jump ARCHES research and development program to address a variety of vexing medical challenges including neurological testing for children and athletes (such as concussions), migraines, and stress among nurses enduring pandemic challenges at home and at work. The Jump ARCHES program is a partnership between OSF HealthCare and the University of Illinois Urbana-Champaign (U of I) and its 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. Many of the awards represent new projects, while some will build on previously-funded efforts.
“These grants reflect areas of tremendous research success on the U of I campus at the intersection of engineering and medicine, which began at our very own Health Care Engineering Systems Center," said T. Kesh Kesavadas, director of the Health Care Engineering Systems Center at U of I Urbana-Champaign. "The Center has established itself as a leading innovator on campus where researchers solve problems faced by real clinicians in hospital settings with cutting-edge technology, such as AI-based intubation and IoT sensor-based masks."
“These projects highlight areas where OSF HealthCare and our partners are committed to making advancements, including COVID-19 recovery, personalized medicine, data security, health literacy to underserved populations, support for those giving bedside care, and improvements in neurological diagnosis and treatment," said Dr. John Vozenilek, VP and Chief Medical Officer at OSF Innovation & Digital Health. "We know digitally connected health systems, powerful data analytics, and innovative approaches offer the promise of a universal standard of care and health knowledge for everyone we serve."
One such project reflects the innovative approaches to common problems, Facial pressure ulcer detection using a wearable sensor patch (WSP), led by simulation engineer Anusha Muralidharan at the Health Care Engineering Systems Center at U of I. "Medical device-related pressure ulcers (PUs) are one of the most common problems in hospital settings. It causes patient discomfort and places a large economic burden on health systems. Our project's objective is to design a wearable sensor patch to aid in early detection and reduce the incidence of PUs in all patients," said Muralidharan.
The sensor will wirelessly transmit data to a software application, which will calculate a risk assessment score based on the sensor's data and patient info. Clinicians will be alerted via text or alarm when the capillary parameter exceeds the threshold value. Muralidharan's co-primary investigator is Deborah McCarter, VP and Chief Nursing Officer at OSF Heart of Mary Medical Center. The prototype will be tested at the Jump Simulation Center on the U of I campus by Shandra Jamison, manager of the simulation center and co-investigator on the project.
"I'd like to thank Jump ARCHES for funding this proposal and providing a wonderful platform to work alongside clinicians to address this critical healthcare problem. This project will lead the way toward a new technology designed specifically to minimize medical device-related PUs," added Muralidharan.
Neurological technology is a primary focus of this grant cycle, as well as health accessibility. Digitized Neurological Exams (DNE) with Smartphones/Tablets – A Clinical Recording Pilot Study is a project that combines both focus areas. Primary investigator Minh Do is a professor in the department of Electrical and Computer Engineering at U of I, working with Christopher Zallek, M.D., of OSF HealthCare and Illinois Neurological Institute.
DNEs are exams that visually quantify and aid in the diagnosis of neurological impairments. This project proposes the use of smartphones to record these exams, showing the potential of an accessible, easy-to-use, and accurate digital solution for conducting the exams both in person and through tele-health.
“We are excited for the opportunity to combine our complementary expertise in clinical and engineering fields from OSF and U of I to develop a widely-accessible and reproducible tools, smartphones with cameras, to exam, quantify, and monitor neurological conditions that affect one in every three people across the US and worldwide,” said Do.
The Fall 2021 grants focused on the following areas:
- Promoting recovery post-COVID or similar health crises, both from a patient level and broader perspective of public health as well as the social and economic impact on health care.
- Addressing evolving standards of care to incorporate personalized precision medicine and genomic best practices.
- Advancing data security and privacy, and serving to increase institutional and patient confidence in sharing sensitive health data.
- Addressing treatment and the health literacy of historically underserved populations.
- Reducing the administrative burden at the bedside to increase the quality of patient interactions.
- Assisting in diagnosis and treatment of neurological disorders through collaborative efforts with the OSF HealthCare Children's Hospital of Illinois and OSF HealthCare Illinois Neurological Institute
The awarded grants are as follows:
High Trust Patient Outreach
Gang Wang, UIUC; Jonathan Handler, OSF HealthCare; Nick Heuermann, OSF HealthCare; Cody Zevnik, OSF HealthCare
This project aims to survey, design, and potentially prototype feasible solutions to enable secure patient outreach for patients across all levels of socioeconomic status. We also want to provide patients and doctors with a list of best practices to use the solution to communicate securely.
Point-cloud segmentation for daily adaptive prostate therapeutic planning
Angela Di Fulvio, UIUC; Gregory M. Hermann, UICOMP, OSF HealthCare
We propose to develop and demonstrate deep-learning-based point cloud models for the registration and segmentation of planning target volumes (PTV) and organs at risk, enabling daily adaptive planning of prostate cancer (PCa) radiation therapy.
Improving the Lives of Children with Asthma by Individualizing the Asthma Care Plan Based on Children's Home Exposure to Asthma Triggers
Elise Albers, OSF HealthCare; Sotiria Koloutsou-Vakakis, UIUC; Margarita Guerin, UICOMP
The project team proposes a pilot study with indoor air monitoring devices (sensors) that can be deployed in homes and schools of a small cohort of OSF pediatric patients with asthma. The air quality data collected by these sensors will be used to individualize the asthma care plan, taking into account the environmental allergens and pollutants that are present in the patient’s home and providing education on how to mitigate these environmental exposures.
Development of a Trusted Execution Enclave to Securely Link Computational Modeling to a Medical Imaging Database
Matthew Bramlet, UICOMP, OSF HealthCare; Brad Sutton, UIUC; Andrew Miller, UIUC
The primary objective of this project is to create a Picture Archiving and Communication System (PACS) plug-in tool that will allow researchers to run various algorithms on these large imaging datasets without exposing protected health information (PHI). This proof of concept project requires solving several problems to bridge the gap between research algorithms and access to an imaging database while ensuring data security and privacy.
Smart phone App for Migraine referral Optimization using MIG-RO (Migraine Referral Optimization)
Hrachya Nersesyan, OSF HealthCare, UICOMP; Lusine Demirkhanyan, UICOMP; Yelena Nersesyan, UICOMP; Christopher Gondi, UIUC; Inki Kim, UIUC
The goal of this project is to streamline diagnosis of migraine at the patient intake level to reduce patient engagement time and improve appropriate and timely referrals to headache specialists. To address the problem of underdiagnoses we plan to develop a Migraine Referral Optimization (MIG-RO) smartphone application, which can be installed on any smartphone or tablet-like device to enable expedited diagnosis at the patient intake level, recommend first steps in management, and facilitate appropriate referrals to headache specialists.
Digitized Neurological Exams (DNE) with Smartphones/Tablets - A Clinical Recording Pilot Study
Minh Do, UIUC; Christopher Zallek, OSF HealthCare, George Heintz, UIUC
DNE has shown the potential as an accessible, easy-to-use and accurate digital solution for in-person and tele-health.
Physiological and anatomical biomarkers for epilepsy antiepileptic drug therapy
Hua Li, UIUC; Michael Xu, UICOMP, OSF HealthCare; Fan Lam, UIUC; Yogatheesan Varatharajah, UIUC
This study aims to develop a comprehensive and robust computational model for the prognosis of AED treatment response. Prognosis models will be developed based on advanced belief function theory (BFT) and deep learning (DL)techniques and utilizing a large cohort of retrospective patient cases. Our preliminary studies havedemonstrated the promising performance of the resulting prognosis models.
Development of a Coordinated and Community-Focused Network of Antibiotic Use and Resistance Data
Ellen Moodie, UIUC; Thanh (Helen) Nguyen, UIUC; Rebecca Smith, UIUC; Rachel Whitaker, UIUC; Brian Laird, OSF HealthCare
In order to understand the human context in which antimicrobial resistance evolves, we need to be able to collect and coordinate data on the relationship of people and health care providers in a diverse community that has been identified as a health care desert. This must include both qualitative data in particular vulnerable communities and aggregated and comprehensive but local across health care providers (metadata on prescription practices and diagnostic results) which is uncoordinated amongst the many organizations working in this community. Therefore, we will also create a data coordination platform for the secure and anonymized sharing of data related to antimicrobial use and resistance within the Champaign County community as an exemplar of dynamics in a multi-cultural agricultural landscape with substantial human mobility.
Healing Health Care Disparities among BIPOC Patients through Virtual Reality Cultural Competency Training
Charee M. Thompson, UIUC; Mardia Bishop, UIUC; Krishan Kataria, OSF HealthCare; Chrysafis Vogiatzis, UIUC
The proposed project is a virtual reality (VR) cultural competency training for health care providers (hereafter “providers”) to reduce health disparities among Black, Indigenous, People of Color (BIPOC) patients. By the end of the training, participants will be able to:
(a) Recognize the health inequities experienced by BIPOC patients
(b) Identify their own implicit biases and utilize strategies for managing them
(c) Communicate with BIPOC patients in a culture-centered manner that demonstrates respect and builds trust.
Hands Down: Empowering Children and Families through CPR Education
Paul Jeziorczak, UICOMP; Inki Kim, UIUC
The purpose of this grant proposal is to create an educational program in mobile app for the family of children admitted to the Children’s Hospital of Illinois surgical service, which will particularly address a significant gap for the families in desperate need of safe and effective CPR skill acquisition, by incorporating a hand-only augmented reality (AR) simulation module. The proposed smartphone-based AR will integrate novel feedback mechanisms to guide the user to a desired range of chest compression with proper hand placement.
Tri-Wave: Inverse Wave Signal Processing for Non-Invasive, Non-Pharmaceutical Migraine Therapy
Christopher Gondi, UIUC, UICOMP; Hrachya Nersesyan, OSF HealthCare, UICOMP; Lusine Demirkhanyan, UICOMP; Yelena Nersesyan, UICOMP; Inki Kim, UIUC
In this proposal we address the imbalance between excitatory and inhibitory cortical-subcortical neurotransmission using the inverse wave approach to manage migraine-associated pain (see figure). Our approach cancels anomalous EEG wave patterns in migraine patients at the pre-, intra- and post phases of migraine.
A Deep-Learning Augmented Point-of-Care Device for Antibody Quantification
Yang Zhao, UIUC; Yun-Shung Chen, UIUC; John Farrell, UICOMP, OSF HealthCare
In this proposal, we will address the unmet need for point-of-care serological tests with quantifiable and improved accuracies. Our goal is to develop a cost-effective SARS-CoV-2 serological testing mechanism that minimizes false-positive rate and is ready for scaling up for large-scale screening. The objective of this proposal is that the team will work together to develop a machine-learning-enabled detection mechanism that can quantify the antibody responses due to SARS-CoV-2 in minutes with pg/mL sensitivity using a cost-effective chiral fluorescent sensor and handheld readout devices.
Low pathogen counts in whole blood samples
Rashid Bashir, UIUC; Enrique Valera, UIUC; John Farrell, UICOMP, OSF HealthCare
This project will demonstrate the feasibility of a new platform to achieve the detection of low bacteria and fungi counts (1-3 CFU/mL), in less than 2 hours, analyzing large volumes of whole blood (up to 5 mL) from clinical samples. Likewise, we would like to advance our understanding of the reaction mechanisms and fundamental questions regarding the bi-phasic reaction.
Virtual reality simulation training for neonatal procedures
Nicole Rau, UICOMP; M. Jawad Javed, UICOMP; Harris Nisar, UIUC
Through a combined effort between engineers and artists from the University of Illinois at Urbana-Champaign (UIUC) and physicians from the University of Illinois, College of Medicine in Peoria (UICOMP) division of neonatology, we aim to develop an innovative VR platform on which to provide simulation training in neonatal procedures for community providers. This software will be based on a curriculum developed by neonatal experts.
Monitoring the Health of the Hospital: Using Wearable Sensors to Monitor Nursing Stress
Abigail Wooldridge, UIUC; Deborah McCarter; Alexandra Chronopoulou, UIUC
Medical errors are estimated to cause more than 250,000 deaths per year in the U.S. and could be by caused human factors/ergonomics (HFE) issues, including provider stress and fatigue. Our long-term goal is to develop a system to monitor provider stress in real time, allowing health care organizations to reduce the risk of burnout and medical error. The overall objectives in this proposal are to develop a scalable data stream of physiological data and validate knowledge extracted from the data stream.
Facial pressure ulcer detection using a wearable sensor patch (WSP)
Anusha Muralidharan, UIUC; Placid Ferreira, UIUC; Shandra Jamison, UIUC; Deborah McCarter, OSF HealthCare
Our proposal seeks to develop a wireless sensor patch system for continuous monitoring of facial pressure ulcers. We will integrate force, temperature and relative humidity sensors into a flexible printed circuit design (FPCB).
Early Detection and Prediction of Facial Expression for Parkinsonism Powered by Few-Shot Learning
Yuxiong Wang, UIUC; Christopher Zallek, OSF HealthCare
Neurological disorders are among the most frequent causes of morbidity and mortality in the US, the most common being Parkinson’s and Alzheimer’s. The insidious and heterogeneous onset of neurodegenerative diseases challenges the abilities of the primary care systems to appropriately diagnose and manage these diseases. We propose an AI supported system that tracks facial expressions of neurological patients and reports findings to the neurologists. In this project we focus on discriminating facial expressions that are associated with Parkinsonism.
Augmented reality assisted endotracheal intubation (ETI) trainer
Anusha Muralidharan, UIUC; Praveen Kumar, UICOMP; Kesh Kesavadas, UIUC; Neil Mehta, UICOMP
This proposal aims to develop a high fidelity training simulator to train health professionals, medical students and residents on endotracheal intubation (ETI) and provide feedback through
- The design and develop a high fidelity ETI smart trainer to teach endotracheal intubation
- Collecting data on health care providers proficient in the procedure of ETI to establish normativedata to create a performance trajectory model
- Development of an augmented reality (AR) application for visualization and feedback
- Validation of the developed augmented reality based simulation trainer by comparing theperformance of novice and proficient health care providers in the procedure of ETI tounderstand differences in technique between these groups
FlightPath and Neuro DNA: Creating a New Interoperability Standard for the Evaluation of Neuro cognitive Impairment
Adam Cross, UICOMP, OSF HealthCare; Inki Kim, UIUC
Conditions associated with neurocognitive impairment (NCI) often present heterogeneously through various combinations of physical and cognitive impairments, posing a challenge to diagnosis. Common etiologies, such as traumatic brain injury (TBI) and dementia, are not yet routinely identified through objective lab or imaging results but instead rely on a combination of physical and cognitive evaluations as well as symptom reporting. The testing batteries are primarily paper-based, dependent on language and education, suffer from learning bias, and must be administered by a health care 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.
Prospective Observational Study: Identification of Brain Micrometastatic Disease Using Ultra-High Field Magnetic Resonance Imaging
Wael Mostafa, UIUC; Aaron Anderson, UIUC; Paul Arnold, Carle; Anant Naik, UIUC; Annabelle Shaffer, UIUC; Sinisa Stanic, Carle; Brad Sutton, UIUC; Charee Thompson, UIUC; Andrew Tsung, OSF HealthCare; Vamsi Vasireddy, Carle; Blake Weis, Carle; Tracey Mencio Wszalek, UIUC
The project outlined here will provide new information about the frequency and prognosis of micrometastases. Comparisons will also be drawn regarding treatment efficacy. Additionally, we include rich quality of life data for brain metastases of all sizes. Combined, this data will support the usage of innovative ultra-high-field imaging in clinical practice and better inform clinicians treating metastatic brain disease.