ClosedLoop.ai Raises $34M to Expand Healthcare Data Science Platform

What You Should Know:

– ClosedLoop.ai., an Austin, TX-based healthcare data science platform, today announced it has raised $34 million in Series B financing led by Telstra Ventures with participation from Breyer Capital, Greycroft Ventures, .406 Ventures, and Healthfirst. Notable angel investors Adam Boehler (former director of CMMI & CEO of Landmark Health) and Sam Palmisano (former CEO of IBM) also participated in the round.

– ClosedLoop’s explainable AI reimagines the concept of patient risk profiling by shifting away from legacy risk “scores” to comprehensive, personalized forecasts delivered directly into clinical workflows. Each forecast harnesses patient-specific data and surfaces key variables that explain precisely what risks a patient faces and why. It integrates relevant clinical details and links to specific interventions that clinical teams use to prevent adverse events, improve outcomes, and reduce unnecessary costs.

– The latest round of funding positions ClosedLoop to extend its lead in delivering artificial intelligence (AI) solutions that tackle some of healthcare’s biggest challenges

Empowering The Data Science Team

ClosedLoop.ai Raises $34M to Expand Healthcare Data Science Platform

The push toward value-based care is significant. In 2018, 36% of spending was via alternative payment models and by 2025, CMS has targeted 100% of Medicare and 50% of Commercial and Medicaid spending.4 AI will help providers succeed under these models because it gives them the ability to predict patient-specific outcomes so they can adjust patient care, improve outcomes, and reduce costs.

Founded in 2017, ClosedLoop’s healthcare data science platform is purpose-built and dedicated to healthcare. It combines an intuitive end-to-end machine learning platform with a comprehensive library of healthcare-specific ML features and model templates. The platform integrates several data science workflows (data onboarding and normalization, automated feature engineering, autoML, and MLOps) and includes capabilities that facilitate experimentation, collaboration, oversight, and management.

The platform’s flexibility allows customers to harness AI-based solutions regardless of their in-house data science and machine learning capabilities. It also provides the industry’s largest catalog of healthcare machine learning model templates and prebuilt clinical features. The “Healthcare ML Content Catalog” allows organizations, including those without a data science team, to create and deploy highly customized and…

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Source: hitconsultant.net