Potential and Pitfalls in Precision Healthcare Delivery

9:00 am

Prediction has the potential to transform health care delivery –“Precision Delivery”. However, there are several hidden challenges in improving upon human clinical decision-making. This talk will use prediction of patients with sepsis as a canonical example to illustrate the potential and pitfalls in Precision Delivery. Amol Navathe | UPenn, Philadelphia VA Medical Center, HealthCare: the Journal of Delivery Science and Innovation

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The Healthcare Path From Hype to Value with AI Requires Trust

10:00 am

AI holds both great promise and priority across healthcare. Move past the hype by following a path to value paved by trust. Understand the hype today around AI in healthcare and learn how organizations are realizing value from AI, including the evidence of that impact. See how you can create trust in AI to deliver even more business value. Laura Craft | Gartner

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Clinical Waypoints for AI: Building Trust in the Clinic

11:00 am

Artificial intelligence (AI) presents many new opportunities to assist a vastly, overstretched healthcare workforce, consisting of less than 1 million practitioners. However, trust in AI remains a major obstacle for widespread adoption in healthcare. Here, we share real-world experience of applied AI in routine and complex decision processes and discuss the significance and role for ‘clinical waypoints for AI’ to accelerate user adoption. Benjamin Yu | Interpreta; UCSD School of Medicine

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Automating the Intensive Care Unit

12:30 pm

When one walks into an intensive care unit (ICU) the first thing that one observes is an abundance of bedside monitors and medical equipment. This is not surprising given that the sickest patients in the hospital are admitted to the ICU, and hence, there is a need for close monitoring and frequent interventions by the doctors and nurses. While almost all bedside monitors in an ICU display critical physiological parameters, virtually all the measured data is lost as soon as the monitor screen is refreshed every few seconds. This talk discusses the application of frameworks predicated on machine learning and feedback control theory to address current challenges in the ICU. We envision an ICU of the future, wherein bedside monitors along with embedded communication, computation, and control platforms will assess the patient’s physiological state and adjust equipment settings to regulate a patient’s physiological state in real time. Behnoon Gholami | Autonomous Healthcare, Inc.

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Data Science in Healthcare: Beyond the Hype

1:30 pm

Abstract: In this talk, I’ll cover the state of the art in predictive healthcare and help you get beyond all the hype. You will leave this talk with a better understanding of how you can apply your skills to make the healthcare system better for everyone! Michael Becker | Penn Medicine

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State of Machine Learning Across Healthcare Payers and Providers

2:30 pm

Machine learning and other advanced analytics techniques hold great promise to transform healthcare, with potential use cases spanning from targeting fraud and waste in healthcare payments to preventing disease using predictive analytics. Several barriers are blocking the rapid growth and adoption of advanced analytics in health care (e.g., lacking critical infrastructure, issues with data quality, inability to “dream big”, etc.). The delivery systems that overcome these challenges, however, will set themselves up for success. This presentation will explore specific use cases for payers to optimize their payment policies with ML and ways that large healthcare institutions (both payers and providers) can surmount existing barriers to realize the potential of machine learning. Manuk Garg | McKinsey & Company

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AI and Precision Medicine: Disrupting Decision Making in Healthcare Delivery

3:30 pm

The digital and AI revolution has affected virtually every aspect of our lives, and healthcare decision making is no exception. This has led to an explosion of big data. The problem is not accessing information. The problem is making sense of information. Drawing on her background and experience in neuroscience, precision medicine, and her research on decision making, Dr. Issa will discuss decision making for this new era of AI…

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Migration of Deadly Brain Cancer Cells: Who’s Smarter

4:15 pm

L1CAM (L1, CD171) is a cell surface immunoglobulin superfamily protein normally involved in axon guidance, differentiation, and cell migration during development. LI also is expressed abnormally in many types of cancers including breast, pancreatic, colon, melanoma and glioma, and has been associated with poor prognosis due to increased proliferation, invasiveness or metastasis. We have shown previously that the soluble L1 ectodomain, which can be proteolyzed from the normal transmembrane form, can stimulate proliferation and motility of glioblastoma (GBM) cells in vitro by acting through integrins and fibroblast growth factor receptors (FGFRs). L1-decorated exosomes were isolated from T98G glioma cell media and evaluated for their effects on glioma cell behavior. The hypothesis being tested was that L1-decorated exosomes increase the proliferation and motility of glioma cells through integrins and FGFRs. This study tested the effect of L1-decorated…

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