Miami Dolphins quarterback Tua Tagovailoa recently suffered his third confirmed concussion in 25 months – just another reminder that football and other contact sports pose the threat of a traumatic brain injury.  Research indicates it’s important to accurately and immediately diagnose a concussion because the consequences of misdiagnosis or faulty management can lead to major disability or death.

Illinois State University senior Cyerra Hibbert knows about concussions. She’s had multiple ones while playing soccer in high school and at ISU, the most recent just a month ago. Hibbert is one of the first athletes to test a new FlightPath concussion app. The lead app developer is *Adam Cross, MD, a pediatric hospitalist and clinical informaticist for OSF HealthCare and director of the OSF Children’s Innovation Lab at Jump Simulation & Education Center in Peoria, Illinois. He is working with co-lead Inki Kim, of the Health Care Engineering Systems Center at the Grainger College of Engineering at the University of Illinois Urbana-Champaign.

The concussion assessment app is a grant-funded project through Jump ARCHES, a research collaborative that partners teams of clinicians and engineers working together to improve patient outcomes and reduce health care costs. The FlightPath app is being tested at Illinois State, Illinois Wesleyan and Bradley Universities as part of a clinical trial and research coalition.  Hibbert used the FlightPath app after receiving instructions from her athletic trainer six days after she received a blow to the head during a game.

Hibbert was able to finish the test. It only takes about two minutes to collect more than a million data points as a person tries to catch a hummingbird within a 3D space on a screen.

“After a while it did bring a little confusion or if I wasn’t aware of where the bird flew on or off the screen, that did trick a little bit of my memory and concentration skills, so I do think it’s definitely approachable," Hibbert explains. “It’s definitely doable. I think just based on your outcome will determine where you are in your concussion level.”

Dr. Cross says the single biggest risk factor for prolonged time to recovery is delayed diagnosis, so FlightPath is his answer with its ability to diagnose a concussion within minutes. His research team has done some internal testing.

“Regarding how well it detects differences in impairments with people – how well they can determine if someone is functioning, acting appropriately, or if there's if there's something else going on. The challenge has been really doing that with individuals that have true concussion,” says Dr. Cross.

Athletes with a suspected concussion are challenged by FlightPath to walk in their environment while trying to keep a hummingbird in a bubble on the screen.

“The players themselves are trying out this app while concussed after having been consented earlier on in the year so that we can get data around how they perform with this app while concussed versus not concussed, and comparing that to the normal, typical present-day evaluation techniques that the trainers perform.”

Dr. Cross stresses, right now, the clinical trial does not involve trainers using the app to help make decisions. He says the research is not that far along and data collection needs to happen first. But, Karan Rai, MD, a sports medicine physician with OSF HealthCare who also serves as the team doctor for ISU Athletics, says eventually, the data from FlightPath could help beyond diagnosis.

“It in turn can help us with making prognoses, coming up with recovery timelines, implementing certain vestibular therapy. Other than diagnostic purposes, we can extrapolate that data to help us come up with a better treatment plan as well.”

Takes the pressure off
Both Hibbert and Dr. Rai are excited to be part of what they consider ground-breaking research that has the potential for helping athletes in the future. Hibbert, who plans to go into medical sales, says it’s helpful to have an objective tool for an evaluation. She thinks it takes the pressure off athletes.

“Because as athletes, we’re always eager to be on the field or play the game and to have that mindset to be better and get back on your feet as quickly as you can. I think this app will allow you to really settle and to really take your time with your symptoms.”

The clinical trial is looking at whether FlightPath can do as good or better as traditional methods in diagnosing a concussion.  So far, Dr. Rai says nearly 15 athletes who have suffered a concussion have completed a FlightPath assessment and it’s been performing well.

“Results are early so far but in our sample size we found some associations at least where FlightPath has shown some abnormalities similar to what other examinations have found as well, whether that’s the SCAT test or the ImPACT test.”

There are other concussion applications commercially available, but Dr. Cross suggests none are as robust as FlightPath.

“There aren't any, as far as I've seen, that use this mixed reality approach to gather the kinds of data that we're gathering. This is very rich data in a very short time in our app. One of the things that makes it so unique and sets it apart is that we get so much data about so many different manifestations of concussion in such a short time.”

Testing is expected to take at least two years. After testing, the app will be submitted to the FDA for clinical approval.

Here is a video demonstrating how the app works.

*Dr. Adam Cross also holds these positions:
Assistant Professor, University of Illinois College of Medicine Peoria, Division of Pediatric Hospital Medicine
Adjunct Professor, University of Illinois at Urbana-Champaign, Health Care Engineering Systems Center (HCESC)
Clinical Research Informatician, OSF HealthCare | Jump Trading Simulation & Education Center (JTSEC) 

Dr. Adam Cross, ISU soccer player Cyerra Hibbert and Dr. Karan Rai

B-roll of ISU soccer player Cyerra Hibbert on soccer field

B-roll of FlightPath app demonstration

Rare diseases might seem individually uncommon but collectively 1 in 10 people across the globe have a rare disease according to the World Health Organization. Undiagnosed rare diseases pose a significant challenge in health care, often leading to delayed treatment and poorer patient outcomes.

When specialists or primary care providers are confounded by a patient’s symptoms, it can send them back to their medical books. But, Adam Cross, MD, a pediatric hospitalist and clinical informaticist who leads the Children’s Innovation Lab at the Jump Trading Simulation & Education Center, is collaborating on research to better help primary care providers make a rare disease diagnosis quickly. The goal is to create an AI-backed, point-of-care support tool that can prompt testing for confirmation for the most relevant diagnoses.

Dr. Cross says typically genetic testing is the only way to confirm a rare disease. That testing is expensive and not readily available in primary care settings, particularly in rural communities. Patients have long waits and often must travel in hopes of getting a diagnosis.

A Jump ARCHES grant is funding a collaborative effort with the University of Illinois Urbana-Champaign (UIUC), with co-lead researcher Jimeng Sun, PhD, to use generative AI and medical knowledge graphs to improve diagnosis of rare diseases.

“We are trying to create a model that will take the notes that are already being written by primary care physicians and look for unique sets of signs and symptoms that might suggest a patient has an undiagnosed rare disease.” Dr. Cross adds, “That approach can empower the physician with information regarding those diseases and prompt further testing if they choose to do so.”

Dr. Cross believes rare diseases, despite their complexity, leave identifiable patterns within electronic medical records. The project introduces Automated Rare Disease Mining (AutoRD) to help unearth patterns when analyzed using machine learning models and mapped using knowledge graphs. He believes the Auto RD method can potentially enable the early detection of these diseases. These patterns could be drawn from clinical signs and symptoms and medical history, in addition to demographic features such as race/ethnicity, age and geographic location.

Researchers are making progress

In one year, researchers have been able to develop robust machine learning models to extract signs and symptoms of rare diseases from notes within electronic medical records that have been stripped of personal, identifying information. They’ve also been constructing comprehensive knowledge graphs to visually map the relationships between various medical conditions and symptoms of rare diseases from existing resources. 

“We did this so that we could give people a better visual example of just how complex these diseases are and how they're all connected,” Dr. Cross observes while viewing an early version of a knowledge graph created by researchers that looks like a celestial constellation. “It's kind of beautiful when you see the simplicity and the complexity of all the different symptoms, but the simplicity of the structure. It gives you a sense of just how incredible these new technologies really are in terms of helping us find patterns in the chaos.”

The effort involves sophisticated machine learning approaches that have changed with lightning speed, even since Dr. Cross and his UIUC colleagues began their work.

“Even in that year, there have been more advanced and more powerful methods that have come out that we've since adopted, and we continue to improve our own methods based on what's being developed globally.”

The research is leveraging the abilities of a supercomputer at UIUC which has a federal health privacy compliant section for secure data storage and processing of massive amounts of health care data. The next focus is to work on the predictive ability of the models. Dr. Cross is convinced that dynamically linking patient-specific data with the broader context of medical knowledge on rare diseases can uncover patterns and correlations that might otherwise remain hidden.

As the approach is refined and improved, he also doesn’t rule out the possibility of discovering new rare diseases.

“As we go along, it is certainly possible with the technology that we've created, maybe we'll start to see patterns of signs and symptoms that don't have a known diagnosis. Or maybe there are subtypes of diseases that are out there that really seem to have their own unique progression, and we could also maybe discover those in subsequent phases of the project.”

Dr. Cross, who also serves as an assistant professor at the University of Illinois College of Medicine in Peoria (UICOMP) and is an adjunct professor at UIUC, says he and his fellow researchers have already published one scientific paper, and another has been submitted, showing the results of their research so far.

Video clips with Dr. Adam Cross

B-roll of knowledge graphs for rare disease diagnosis

Sixteen research projects are sharing more than $1.6 million in funding through the Jump ARCHES research and development program. Projects funded focused on novel automation in health data and patient experience, precision medicine, digital tools to enhance patient engagement in health, wellness and treatment, plus improved treatment and diagnosis of neurologic disorders. The latest request for proposals also encouraged supporting underserved populations.

The Jump ARCHES program is a collaboration between OSF HealthCare, the University of Illinois Urbana-Champaign (UIUC), and the University of Illinois College of Medicine Peoria (UICOMP). The funding supports research involving clinicians, engineers and social scientists to rapidly develop technologies, devices, and treatment approaches that improve outcomes and reduce costs.

Spring 2023 Project Awards

STREAM-ED: Simulation to Refine, Enhance and Adapt Management of Emergency
William Bond, MD, OSF HealthCare
Hyojung Kang, PhD, University of Illinois Urbana-Champaign

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
Scott Lowry, MHA, CHC, CCEP, OSF HealthCare
ChengXiang Zhai, PhD, University of Illinois Urbana-Champaign

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

This 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
Sheryl Emmerling, PhD, Rn, NEA-BC, OSF HealthCare
Ann-Perry Witmer, PhD, University of Illinois Urbana-Champaign

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
Matthew Bramlet, MD, OSF HealthCare
Zhi-Pei Liang, PhD, University of Illinois Urbana-Champaign

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
Mary Stapel, MD, OSF HealthCare
Hyojung Kang, PhD, University of Illinois Urbana-Champaign

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.

Jump ARCHES research and development program awarded nearly $1.8 million in funding across 16 projects to address a variety of medical challenges. Many of the grant awards address issues dominating the news, including health care inequity, misinformation on social media, and the need for earlier detection of anxiety and viruses for improved treatment.

“These projects leverage the strengths of OSF HealthCare and our academic partners who are committed to practical solutions that can be deployed throughout health care to make a difference in early diagnosis, personalized treatment and disease prevention,” according to John Vozenilek, MD, vice president/chief medical officer, OSF Innovation & Digital Health. 

Elizabeth Hsiao-Wecksler, PhD, interim director of the UIUC Health Care Engineering Systems Center and professor of mechanical science and engineering, says some of the grants build on
past successful Jump ARCHES research that is driving innovative solutions.

“Those projects will advance efforts to show proof of concept and scalability as a go-to-market solution to benefit partners and many others in the health care industry,” says Hsiao-Weckler. “Other projects lay the foundation for novel initiatives that will take time to develop but that show promise for transforming approaches to prevention, diagnosis, treatment and health outcomes.”

The Jump ARCHES program is a collaboration between OSF HealthCare, the University of Illinois Urbana-Champaign, and the University of Illinois College of Medicine Peoria. The funding supports research across multiple disciplines including clinicians, engineers and social scientists focused on rapidly developing solutions to advance medical training and health care delivery.

SEE THE FULL LIST OF PROJECTS AWARDED GRANTS through the Jump ARCHES program.

Highlight Project: Creating an application for health care providers to counter misinformation on social media

Health experts worry Twitter owner Elon Musk’s decision to drop a ban on misinformation will exacerbate the growing problem of health misinformation circulating on social media. Health providers can’t keep up with the latest misinformation so they’re often left with few options to respond to patients’ assertions or questions. This project will develop and deploy a stream of cost-effective, real-time fact-checking services through an application that provides physicians and other health care professionals with early-warning information tied to a variety of diseases, treatments and remedies. READ THE STORY.