Midwest Healthcare Management Conference

August 23, 2024
Hilton Garden Inn
1501 S Neil St, Champaign, IL 61820

Free conference parking on-site

Register by August 2, 2024

Digital Technology and AI Enabled Healthcare Delivery to Diverse Patient Populations

This conference will address key emerging issues related to delivering healthcare to diverse patient populations, with a special focus on how digital technologies and AI are poised to revolutionize this landscape over the coming years.

Digital technologies include remote monitoring devices, remote monitoring devices, wearable health monitors, and telemedicine and telehealth platforms, Internet of things in Medicine, blockchain in healthcare, virtual reality and augmented reality, and personalized medicine and genomics. AI in healthcare refers to the application of artificial intelligence (AI) technologies, including machine learning, natural language processing, large language models, computer vision, and robotics, to improve various aspects of healthcare delivery.

Digital technologies and AI are intertwined in a complementary manner to deliver improved and inclusive healthcare to a wide section of the population at a relatively lower cost and higher capacity. The integration of the two and their implementation in innovative use-cases are going to open up new application areas in healthcare delivery.

New technologies in healthcare encompass a wide range of applications and tools designed to assist healthcare providers, enhance patient outcomes, provide improved patient experience, monitor patients remotely, deliver healthcare to a wider section of the society, optimize operational efficiency, and drive innovation in the healthcare industry.

Some of the areas where AI in healthcare is likely to make an impact in the future are as follows.

Improve and augment (speed up) diagnostic accuracy and capacity

AI algorithms such as CNNs or Vision Transformers can analyze medical images, such as X-rays, MRIs, and CT scans, with high accuracy. This can lead to earlier and more accurate diagnosis of diseases, increase diagnostic capacity, provide access to diagnostic capabilities to a spatially dispersed population including remote and underserved populations, and reduce healthcare costs.

Personalized Treatment Plans

Identify patters and predict personalized treatment plans for diseases such as behavioral health, cancer, chronic diseases, and cardiovascular diseases.

Patient remote monitoring and home hospital applications

AI-powered devices and sensors (enabled by IoT, remote vision, and other technologies) enable remote monitoring of patients' vital signs and health metrics. This allows healthcare providers to monitor patients outside of traditional clinical settings and intervene early if any issues arise. Additionally, telemedicine platforms powered by AI can provide patients with access to healthcare services from the comfort of their own homes.

Drug Discovery and Development

AI algorithms can analyze vast amounts of biological and chemical data to identify potential drug candidates more quickly and accurately than traditional methods. This can accelerate the drug discovery and development process, leading to the creation of new treatments for a wide range of diseases.

Healthcare Resource Allocation

AI can analyze population health data to identify areas with high healthcare needs and allocate resources accordingly. This can help healthcare systems optimize resource utilization and improve access to care for underserved populations.

Virtual Patient Simulation

Generative AI can create virtual patient models and create digital twins that can accurately represent various medical conditions and responses to treatments. These virtual patients can be used for training healthcare professionals, simulating medical procedures, planning for surgeries and invasive procedures, and testing the effectiveness of new treatments in a risk-free environment.

Clinical Decision Support

Large language models can provide real-time clinical decision support to healthcare providers by analyzing patient data, medical literature, and best practices. They can offer personalized treatment recommendations, flag potential drug interactions or contraindications, and assist with diagnostic reasoning.

Patient Communication and Education

These models can generate personalized educational materials and communication tailored to patients' needs and health literacy levels. They can answer common health-related questions, provide guidance on medication adherence and lifestyle modifications, and offer support for chronic disease management.

Natural Language Processing (NLP) for Electronic Health Records (EHRs)

Large language models can analyze unstructured data in electronic health records (EHRs) to extract valuable insights and facilitate clinical documentation. They can help automate coding, summarization, and extraction of key information from medical records, saving time for healthcare providers and improving documentation accuracy.

Healthcare Chatbots and Virtual Assistants

These models can power chatbots and virtual assistants that interact with patients, schedule appointments, triage symptoms, and provide basic healthcare advice. They can offer 24/7 support, reduce administrative burden on healthcare staff, and improve access to care, especially in underserved areas.

Medical Literature Review and Research Assistance

Large language models can sift through vast amounts of medical literature to identify relevant research papers, clinical trials, and evidence-based guidelines. They can assist researchers and healthcare professionals in staying up-to-date with the latest advancements, conducting literature reviews, and generating hypotheses for further investigation.

Population Health Management and Predictive Analytics

By analyzing aggregated patient data, large language models can identify trends, predict disease outbreaks, and stratify patient populations based on risk factors. They can help healthcare organizations implement targeted interventions, allocate resources efficiently, and improve overall population health outcomes.

Robot-Assisted Surgery

AI-powered robotic systems can assist surgeons during minimally invasive procedures, improving precision, reducing recovery times, and enhancing patient safety.

Conference Agenda

Friday, August 23, 2024 Sessions

  • 8:00 – 8:30 am | Registration & Breakfast
  • 8:30 – 9:00 am | Inauguration & Welcome
  • 9:00 – 10:15 am | Academic Presentations
    Presentation 1:
    Presentation 2:
    Presentation 3:
  • 10:15 – 10:30 am | Break
  • 10:30 am – 12:00 pm | Keynote Presentations
  • 12:00 – 12:30 pm | Lunch
  • 12:30 – 2:00 pm | Industry Presentations
    Presentation 1:
    Presentation 2:
  • 2:00 – 2:15 pm | Break
  • 2:15 – 3:45 pm | Academic Presentations
    Presentation 1:
    Presentation 2:
    Presentation 3:
  • 3:45 – 4:00 pm | Break
  • 4:00 – 5:30 pm | Panel Discussion
  • 5:30 – 6:00 pm | Closing Remarks
  • 6:00 – 8:00 pm | Dinner and Networking

Person working on digital tablet

Sponsored by Gies College of Business, Carle Illinois College of Medicine, and Gies Healthcare Initiative

Carle Illinois - College of Medicine