Interested in learning about how ClosedLoop customers are using AI/ML to improve health outcomes and why AI is key to success in value-based care? ClosedLoop's CTO and Co-founder Dave DeCaprio recently sat down on the Forward podcast to discuss powering healthcare interventions with AI/ML predictions, designing healthcare AI/ML for explainability and bias/fairness, the forthcoming healthcare data revolution, and more.
In this webinar, Carol McCall and Amrita Chadha, share a useful framework that your healthcare organization can use to evaluate programs’ ROI and impact on health equity, especially when introducing an artificial intelligence/machine learning (AI/ML) component.
Traditional program evaluation approaches fall short when it comes to assessing complex interventions, but HCOs must be capable of evaluating whether or not their programs work if they are to succeed in healthcare’s new business model. Without a “gold standard” evaluation approach, how can HCOs measure the sustainability and impact of their interventions?
Health equity has become an industry-wide priority, and organizations are turning to machine learning (ML) algorithms or rules-based systems to allocate healthcare resources to their members. The catch? Algorithms that aren’t adequately evaluated for bias can actually make health disparities worse. In this webinar, AIMed and ClosedLoop sat down to discuss algorithmic bias and how organizations can advance health equity with AI. Watch to learn why it’s important to assess bias prior to deployment and gain insight into ClosedLoop's new platform features that help data science teams evaluate algorithmic bias while training and validating ML models. Additionally, you'll hear about what a customer discovered when evaluating their own predictive models for bias, and what they learned in the process.
ClosedLoop ranked #1 in Healthcare Artificial Intelligence: Data Science Solutions for the second year in a row. ClosedLoop received “A+”s and “A”s across all evaluation categories, scoring 95.2 overall on a 100-point scale based directly on feedback from customer interviews with KLAS.
With CMS’ launch of the ACO REACH program bringing attention to its focus on improving health equity, reducing health disparities has become an industry-wide priority. Many providers and payers use machine learning algorithms or rules-based systems for population health, clinical decision support, and other decisions that affect healthcare resource allocation. But if these tools aren’t evaluated for algorithmic bias, they can unintentionally make health disparities worse. In this webinar, ClosedLoop will discuss the importance of evaluating algorithms for bias prior to deployment as well as metrics for assessing bias. We will also demo new features for algorithmic bias evaluation that enable data science teams using the ClosedLoop platform to assess algorithmic bias as part of process for training and validating machine learning models in healthcare.
In this Millennium Alliance Live podcast, Andrew Eye, CEO at ClosedLoop, breaks down AI's role in driving down the total cost of care and improving clinical decision-making to produce better health outcomes. He addresses historic concerns about the use of AI, explains how organizations can most effectively incorporate AI, and describes how ClosedLoop became the recognized industry leader in healthcare AI.
Beyond Innovation Co-host Anthony Lacavera speaks with Henry Legere, Founder & CEO of Reliant Immune Diagnostics and Andrew Eye, Co-founder & CEO of ClosedLoop AI, about the different ways each company is using artificial intelligence to improve access to and the quality of healthcare for patients.
In this podcast episode, Dave DeCaprio, Co-Founder and CTO at ClosedLoop, sits down with Ian Alrahwan of the The University of Texas at Austin AI Heath Lab. Together, they discuss where AI and ML fit into clinicians' decision-making process and the rationale behind training models on specific populations. Dave shares the story behind ClosedLoop's CMS AI Challenge win and why the ability to rapidly experiment set the company apart.
More than ever before, artificial intelligence/machine learning (AI/ML) models have the potential to improve healthcare and decision-making; and the stakes are high. Considering what’s at stake, can data scientists accept a predictive model deployment rate of only 1 in 10?
Empty feature stores shouldn’t be the obstacle that keeps HCOs from using ML to proactively address preventable, negative health outcomes and reduce costs. Purchasing or building a feature store is essential to scale and utilize ML implementations for predictive modeling.
Andrew Eye, CEO and co-founder of ClosedLoop, and Amanda Goltz, lead for the AWS Accelerator focused on health equity, join the AWS Health Innovation podcast to discuss how startups are addressing health equity. Hear how the AWS Accelerator supports healthcare startups, how the aims of health equity are aligned with value-based care, and why Andrew believes local data is so essential to building powerful, unbiased predictive models in healthcare.