How is artificial intelligence changing the healthcare industry? Does using AI improve health outcomes and reduce costs? To discuss the use of this technology in healthcare, Carol McCall, Chief Health Analytics Officer for ClosedLoop.ai, joined host Kevin Stevenson on MarketScale's I Don't Care podcast.
The CMS AI Health Outcomes Challenge was the largest competition ever for healthcare AI with over 300 entrants and $1.6m in prize money. In this panel at AI Innovation Circle, challenge winner and ClosedLoop.ai CEO, Andrew Eye, will join Graham Brooks, partner at .406 Ventures, to discuss explainability in healthcare AI, the importance of domain expertise for AI project success, and CMS challenge takeaways.
ClosedLoop’s Chief Health Analytics Officer, Carol McCall, was recently featured on the Chief Healthcare Executive™ Data Book Podcast. She discussed the recent AI Health Outcomes Challenge conducted by CMS and how ClosedLoop won the historic contest.
Watch to learn how ClosedLoop won the CMS AI Health Outcomes Challenge, and How Genesis Physicians Group is using ClosedLoop to identify and tackle healthcare challenges facing nearly 30,000 Medicaid patients in Dallas.
In this video interview with Cheddar News, Andrew Eye, CEO & Founder of ClosedLoop, explains the problem ClosedLoop is on a mission to solve.
ClosedLoop’s Chief Health Analytics Officer, Carol McCall, spoke at the AWS Healthcare & Life Sciences Web Day on July 9th. Carol presented on real world deployments of AI and predictive analytics driving tangible ROI in healthcare today.
In this talk, Dave DeCaprio, CTO and co-founder of ClosedLoop will discuss some of the challenges to achieving the promise of machine learning in healthcare, along with some practical approaches he has used to overcome these challenges during his 15 years of data science work in the industry.
Joseph Gartner, PhD, Director of Data Science, walks through a comparison of SQL vs CL Expressions. He outlines how CL Expressions are specifically geared to the types of aggregation and time manipulation that are common in the practice of data science and compares both approaches on mock emergency room utilization data.
What is the impact of COVID-19 on the future of data science and AI in Healthcare? How equipped are your data science and predictive analytics teams to respond to new demands of a rapidly changing landscape?
Hear from healthcare leaders on how they are dealing with the challenges presented by COVID-19 and targeting outreach to those most vulnerable to severe complications from COVID-19 to help them ‘shelter in place'.
Dave DeCaprio, the CTO and Co-Founder of ClosedLoop.ai, gives an inside look at how CV19 Index was developed. He covers several aspects that were interesting about this project from a data science perspective, such as the lack of available data on COVID-19, how the types of models were selected, and the tight timeframes facing ClosedLoop.
Watch for a deep dive into our free model, the C-19 Vulnerability Index, created to help hospitals, federal / state / local public health agencies and other healthcare organizations in their work to identify, plan for, respond to, and reduce the impact of COVID-19 in their communities.
AIMed hosts ClosedLoop CEO, Andrew Eye, and Medical Home Network CMO, Art Jones, to discuss the current application of ClosedLoop's COVID-19 Vulnerability Index.
In this episode of Relentless Health Value, Andrew Eye, CEO of ClosedLoop, delves into how healthcare leaders are leveraging AI and responds to questions about the state of AI and the hype surrounding it.
A follow up interview with Andrew Eye, CEO & Founder, ClosedLoop.ai who presented at the June 2019 Austin HIMSS Lunch & Learn. In his talk at the event, Andrew discussed recent advancements in AI and data storage and how it allow stakeholders in the provider, payer, and life science spaces to overcome traditional challenges with "messy" healthcare data.
Andrew Eye joins the DataPoint podcast and explains how ClosedLoop unpacks the “black box” of AI by allowing data scientists and clinicians to understand why and how factors impact a models prediction, driving faster adoptions and better clinical results.