It is also the site of a newly placed telehealth kiosk as part of a continuing grant-funded project through the Community Health Advocacy (CHA) program, a partnership with OSF HealthCare and the University of Illinois Chicago (UIC). The kiosks integrate an iPad with a user-friendly display, which is operated through a touchscreen interface.

The newest version expands an effort that began a couple of years ago. It includes features and functions based on input from OSF HealthCare Faith Community Nurses who provide street medicine to unhoused and vulnerable populations. For example, the kiosks and embedded tablets will use cellular technology, which is important because some areas in vulnerable communities don’t have reliable Wi-Fi.

Scott Barrows, MA, director of OSF Innovation Design Lab at Jump Trading Simulation & Education Center in Peoria, says he worked with other OSF Innovation teams including engineering, medical visualization and OSF OnCall to come up with the best design and user-friendly navigation and apps for the kiosks.

“Right now, we have two basic languages – English and Spanish – on the opening page and then other languages in the findhelp portion but it has to be super easy to use. And, that’s another thing we’ll look at.” Barrows explains, “We’ll see how many times does someone have to tap the screen and get to where they want to go? Are they happy with it?” 

Findhelp is an application that provides a directory of community-based resources within a pre-selected geographic area. Users can type in a ZIP code and then select what they need. Some of the options address social drivers of health, such as food, housing, work, money, transportation and education. These are things outside of direct medical care that significantly impact a person’s health.

Mary Stapel, MD, lead physician of Community Care at OSF HealthCare Saint Francis Medical Center in Peoria, says current community needs assessments show housing, substance abuse and mental health treatment among the biggest needs. But Stapel says the findhelp app will collect data that could help provide concrete, up-to-date information about what services are most in demand.

Data could drive future services

“I don’t know that we yet fully understand all of the maybe unmet needs that are still apparent for the population that are served here. So again, I think this will help us understand by seeing what people are utilizing on the kiosk.” 

Sommerville agrees data collection will help as social service advocates apply for additional grants and government services to improve community health and well-being.

“It could lead to a shift in the community. I always say some programs are created from individuals who feel as though they know what the community needs. This would be the community telling us exactly what they need and now it’s on us to create something specifically for them.”

Sommerville is also looking forward to having the findhelp app as a resource for him to tap when someone reaches out looking for help.

“It’ll be nice to be able to say, ‘Hey the Dream Center provides this, or hey CASA provides this, or The Center for Prevention and Abuse provides X’ and to know what I’m talking about when I’m saying it. That’s the part that I appreciate because in the past, it’s always been a phone call or reference of some sort.” 

In Peoria, the kiosks will also be located at the Dream Center, the Jolt Foundation and the Peoria City-County Health Department. Barrows says staff at each location will be trained to direct visitors to the kiosks and to help them with navigation if they have issues. However, he says the kiosks should be so user-friendly that they could be used independently. Barrows says research for the next six months to a year will include user feedback.

“We’ll do focus groups. We’ll meet with people one-on-one if they prefer or in small group settings but that is critical in user design. The emphasis is on the people that use it and the resources.” 

The same will be occurring in the Chicagoland area where many unhoused Chicagoans also suffer from chronic medical and behavioral health conditions that can make them vulnerable to premature mortality. Under the CHA grant program, kiosks will be located at the Revive Center and Hope’s Table as well as in the outpatient pharmacy at the University of Illinois Chicago.

Faria Munir, PharmD, MS, BCPS is clinical assistant professor of the drug information group at the UIC Retzker College of Pharmacy and is co-lead investigator for the research. She says the secure kiosks will not collect identifying information from users. However, users will be asked if they are willing to fill out follow-up surveys that will include important questions that will inform the second phase of this pilot.

“Like how they're paying for medications. What kind of assistance they need in terms of their health? What kind of are their biggest barriers and receiving both health care and both prescription and OTC (over-the-counter) medications? What kind of concerns do they have about their health? And a lot of questions that are focused on their attitudes toward telehealth. 

Down the road, Munir would like to see more health care providers engaged in designing resources that could be available through the kiosks, including potential telehealth visits. But she knows that effort would require bridging a lot of hurdles including protecting user privacy.

In the next phase of the research, Munir does envision expanding the health kiosk locations to more community pharmacies and emergency department waiting rooms. In the meantime, by providing access to information about preventive care, disease management and available quality health care and community resources, researchers from OSF and UIC believe the kiosks can improve individual and community health.

Scott Barrows

Mary Stapel and Faria Munir

Marcellus Sommerville

Kiosks B-roll

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.