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AI in Healthcare

Chair's opening remarks

AI in Healthcare

Chair's opening remarks

Approaches to partnering for data sciences in healthcare

Data sciences innovation is flourishing around the world, but how do we harness that innovation to transform the healthcare industry? How do entrepreneurs, academics, and the healthcare industry work together to help take products and technologies from concept to commercialization, and ultimately, impact the lives of millions worldwide? Both industry and entrepreneurs benefit from partnerships, but they can often be challenging to build and maintain, particularly in novel technology areas such as AI which have not yet fully established trust in their value. At Johnson & Johnson Innovation, we build relationships and create customized collaborations with regional entrepreneurs, universities and institutes developing early- to mid-stage innovations across Pharmaceutical, Medical Device and Consumer Health sectors. I will discuss some of the unique challenges related to data sciences and AI on both sides of the partnering equation, and suggest approaches which facilitate partnering to accelerate the impact of this technology in healthcare.

Emma Huang Director, Data Sciences, External Innovation Johnson & Johnson

Promises & pitfalls: Using ‘big’ medical records data for research

The widespread adoption of electronic health records has facilitated passive collection of large amounts of computerized medical data. Researchers are eager to leverage these data into insights that can meaningfully improve clinical care and patient outcomes. Yet enthusiasm about the impressive size and availability of these datasets should not diminish our awareness of their weaknesses; as with all research, it is essential to draw careful conclusions that are well supported by the data. This talk will give an overview of electronic health record data, review its potential strengths, and outline five common pitfalls, with recommendations on how to mitigate them.

Kathryn Rough Research Scientist Google

Driving AI with Human-Centered Design

The Innovation Force at Cambia Health Solutions will walk through a case study of how they recently used a human-centered design process to drive development of better products using AI and ML. By designing to solve customer problems, they developed a new way to give peace of mind to consumers paying medical bills.

Max Janasik Vice President of Innovation Cambia Health Solutions
Nicole Cathcart Director, Innovation Cambia Health Solutions

Morning coffee and networking

Segmentation of users by health condition and prediction of future diagnoses

Expert systems have long been used in healthcare to classify people into segments based on their medical conditions. However, these approaches can be very brittle, as well as expensive to build and maintain, when used to classify people who are in the early stages of a condition or when the available data is sparse or messy. Machine learning and, in particular, deep learning, can often outperform an expert systems approach in these scenarios in accuracy and cost. This talk will cover the design and results of a system Castlight built that combines deep learning, traditional machine learning, and expert systems to classify users into segments related to their medical conditions using their medical and drug claims, demographic data, application activity, and biometrics. We use the resulting segmentation to deliver personalized recommendations to users directing them to relevant employer-sponsored benefit programs, educational content, and high quality, low-cost medical providers for their condition.

Robert Stewart CTO & Chief Architect Castlight Health

The rise of informatics - A sepsis use case

Healthcare has been transforming from a volume model to value, where patient satisfaction is at the forefront. Data driven decisions and timely information are now becoming a critical component to create value by making patients and consumers the core of their strategies. Data driven approaches can help improve preventative care, drive business decisions, and reduce overall costs of care. This talk will go over why healthcare needs big data and how healthcare institutions like Sutter are using data, specifically Informatics to improve patient care. This talk will address a data driven case on Sepsis and the direction Informatics is heading.

Kriti Bhatia Business Intelligence Analyst Sutter Health

Leveraging Distributed Systems & AI for Multi-Omics Data Analysis

Henry Ines CEO Shivom

Lunch

With innovative launchpad presentations from:
Danielle Deibler, Marvelous.ai
Ted Benson, Instabase
Pat Giblin, 451 degrees

Optimizing marketing data to personalize healthcare

Beachbody LLC is the creator of the nation’s most popular fitness and weight-loss solutions including P90X, Insanity, 21 Day Fix. Customer focus is key for us, and the direct-selling division of Beachbody is powered by our independent distributors, who are called as “Coaches”. Data is critical at Beachbody for making important decisions. In this session, the speaker, Aarthi Sridharan will be doing a deep dive on how Beachbody leveraged its data platforms to build an in-house email marketing system to help marketing team to customize and personalize communication to customer and coaches. In addition to saving millions of dollars, this solution enabled us to better segment & target customer by collecting customer, order transactions, social media, web analytics and email response data. This personalization of emails and communication resulted in better click rate & customer conversion and open rate increased by 10%.

Aarthi Sridharan Senior Director, Data Beachbody

Mobile device data in healthcare

Recent technological advancements make it possible to closely and continuously monitor individuals on multiple scales in real time while also incorporating genetic, environmental, and lifestyle information. We are collecting and using this multi-scale biomedical data to gain a more precise understanding of health and disease at molecular and physiological levels and developing actionable, predictive health models for improving cardiometabolic outcomes. We are simultaneously developing tools for the digital health community, including the Digital Biomarker Discovery Pipeline (DBDP), to facilitate the use of mobile device data in healthcare.

Dr Jessilyn Dunn Assistant Professor, Departments of Biomedical Engineering and Biostatistics & Bioinformatics Duke University

Afternoon coffee and networking

Main Stage - Start-up competition

Main Stage - Start-up competition ends at 5.00pm.