Join the North Carolina Chapter of HIMSS in a thought-leadership discussion during our annual Summer Solstice educational symposium and dinner.
It’s not sci-fi anymore: Artificial Intelligence (AI) is here, and it’s now. The influence of machine learning and AI is being felt throughout healthcare. Whether through clinical use or payments, it has the power to revitalize the industry. But with this comes challenges with technology, terminology, and, perhaps most daunting, how to introduce this potential transformative solution into existing healthcare workflows.
Join us at the Summer Solstice Symposium to go beyond the hype and learn what’s happening to be prepared for the future.
June 20, 2019
|2:30 pm – 3:15 pm||Is AI Ready for the Limelight? A Look at Overcoming Barriers to AI Implementation for Improving Health Outcomes: Whitley Yi, PharmD, UNC Health Care|
|3:30 pm – 4:15 pm||Improving Healthcare Outcomes Using the Power of AI & Open Standard: Sunil Mishra, Senior Software Engineer, IBM Watson|
|4:30 pm – 5:15 pm||Algorithmic Bias: Challenges and Opportunities for AI in Healthcare: Gregory S. Nelson, VP, Analytics and Strategy – Vidant Health | Adjunct Faculty Duke University|
|5:15 pm – 6:00 pm||Networking Reception|
|6:00 pm – 7:30 pm||Dinner with Keynote Presentation, Utilizing AI for Clinical Trials Matching by Cherry Drulis & Jeffrey Rosowski with IBM Watson|
Embassy Suites by Hilton Wilmington Riverfront
9 Estell Lee Place, Wilmington, NC 28401
A small block of rooms has been reserved at a discounted rate of $159.
Book early to lock in the discount!
- Healthcare Providers (works in a facility that provides direct patient care) & Students – $35
- Vendors/Consultants/Non-Providers – $90
- Qualified Sponsor – Included in sponsorship
*NC HIMSS members receive a 10% discount
Improving Healthcare Outcomes Using the Power of AI & Open Standard
Sunil Mishra, Senior Software Engineer, IBM Watson
Healthcare data is complex and distributed. AI and machine learning algorithms can mine them effectively and by coupling with predictive analytics on a stream of data can produce information that can be used by the patient, provider, and payers. An open standard can share the data across various pillars of the healthcare system.
Objective 1: Provide an overview of AI and machine learning.
Objective 2: Discuss how to use data-streaming methods with AI and machine learning in real-time.
Objective 3: Explore why an open standard is crucial for delivering the AI and machine learning insights across the healthcare system and beyond.
Sunil Mishra has 17+ years of experience in information technology, covering a broad spectrum of roles and disciplines. His expertise includes enterprise-wide architecture, MQ Messaging, SI-Bus, J2EE application deployed on the WebSphere application server, and implementing health standers like FHIR from HL7. Currently, Sunil is a technical lead with IBM Watson Health SurgaIQ Solution development team. He is very passionate about how to improve healthcare for everyone while leveraging big data and analytics to reduce costs.
The promise of Artificial Intelligence (AI) includes the achievement of sustainable analytic solutions beyond what is currently available, providing automation and scalability. However, regarding leveraging AI for improving clinical outcomes, it is not yet clear the extent to which deployment of machine learning algorithms is a feasible option in the ambulatory care setting to identify patients for intervention. Many barriers exist, such as quality and accessibility of data for training, availability of AI expertise for algorithm design and validation, as well as mixed evidence for whether AI can produce predictions to the same or higher degree of accuracy as traditional statistical modeling. This presentation will explore these barriers around AI implementation, utilizing specific examples from a use case for heart failure patients at a cardiology specialist clinic.
Objective 1: Identify three key barriers to AI implementation in ambulatory care.
Objective 2: Describe the opportunities and limitations of current AI methodologies for patient risk stratification.
Objective 3: Describe possible AI implementation strategies given a specific barrier.
Whitley Yi, PharmD, received her Doctor of Pharmacy from the University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences in 2017. She is completing her pharmacy residency training at UNC Hospitals, focusing on ambulatory care and informatics. She has a strong interest in leveraging technology to improve clinical care. Her work has included evaluation of data analytic tools in ambulatory care to leverage the use of patient-generated health data in clinical decision making.
The promise of AI is quickly becoming a reality for a number of industries including healthcare. For example, we have seen early successes in the augmenting clinical intelligence for diagnostic imaging and in early detection of pneumonia and sepsis. But what happens when the algorithms are biased? In this presentation we will outline a framework for AI governance and discuss ways in which we can address algorithmic bias in machine learning.
Objective 1: Illustrate the issues of bias in AI through examples specific to healthcare.
Objective 2: Summarize the growing body of work in the legal, regulatory, and ethical oversight of AI models and the implications for healthcare.
Objective 3: Outline steps that we can take to establish an AI governance strategy for our organizations.
Greg Nelson, MMCi, CPHIMS, recently joined Vidant Health as vice president of analytics and strategy. Before Vidant, Greg was founder and chief executive officer of ThotWave, an analytics advisory firm specializing in healthcare analytics. Greg serves as an expert for the International Institute for Analytics and adjunct faculty at Duke University’s Fuqua School of Business. He teaches analytics in both the School of Nursing and the Fuqua School of Business at Duke University. An author with over 200 papers and publications, Greg is a regular speaker and keynote presenter at national and international events in both technology as well as for private companies and events.
Featured Dinner Presentation: Utilizing AI for Clinical Trials Matching
Cherry Drulis, Executive Clinical Consultant, IBM Watson Health
Jeff Rosowski, Oncology & Genomics Specialist, IBM Watson Health
Cherry Drulis, MBA, BSN, RN, has over 20 years’ experience in the healthcare industry ranging from front-line clinical services and medical unit director to chief nursing officer. She currently focuses on point-of-care efficiencies at IBM Watson Health. Throughout her career, Cherry has helped healthcare organizations overcome operational challenges and take full advantage of solutions to optimize outcomes and transform the delivery of care across the continuum.
Jeff Rosowski, PA-C, is East Region lead of the IBM Watson Oncology Clinical Trial Matching solution. After graduating from D’Youville College Physician Assistant program in Buffalo, NY, Jeff began a career in internal medicine and urgent care in the Raleigh area. His previous work experience includes GlaxoSmithKline and Boston Scientific.