Startup, Machine Learning, Healthcare, AI

KenSci Named to the 2019 CB Insights Digital Health 150 — List of Most Innovative Digital Health Startups

Written by Kensci on 02 Oct 2019

New York, NY, October 2, 2019 — CB Insights today named KenSci to the inaugural Digital Health 150 ranking, showcasing the 150 most promising private digital health companies in the world. CB Insights CEO Anand Sanwal revealed the final Digital Health 150 during Future of Health, a gathering of top executives across some of the largest healthcare institutions, startups, and investment firms.

“From pharmaceuticals to patient care, technology is rapidly changing healthcare, and The Digital Health 150 showcases the 150 best startups spearheading innovation and change in the sector,” said CB Insights CEO Anand Sanwal. “It’s a privilege to use CB Insights’ data to identify and shine a light on these companies, and we look forward to tracking their success in 2019 and beyond.”

The Digital Health 150 companies span the globe, from Canada, China, Germany, Israel, the United States, and the United Kingdom, and are supported by more than 850 investors.

“KenSci’s is on a mission to empower healthcare with Machine Learning, and deliver better patient experiences at every touchpoint.”, said Samir Manjure, Co-founder & CEO, KenSci. “We’re thrilled to be named in The Digital Health 150 as it reinforces our commitment to transform healthcare with predictive analytics and AI.”

Through an evidence-based approach, the CB Insights research team selected the Digital Health 150 from hundreds of applications based on several factors including patent activity, investor quality, news sentiment analysis, proprietary Mosaic scores, market potential, partnerships, competitive landscape, team strength, and tech novelty. The Mosaic Score, based on CB Insights’ algorithm, measures the overall health and growth potential of private companies to help predict a company’s momentum.

KenSci’s risk prediction platform is built on Azure ML, SQL and provides insights using Power BI. The platform ingests data from healthcare sources, such as EMR, Claims, Billings, Pharmacy, Wearable, and Psychosocial data, to identify patterns that help uncover opportunities to save costs and lives.

 

Quick facts on the 2019 Digital Health 150:

  • In 2018, these emerging private companies cumulatively raised $5.7B across 117 deals. They’re on track to surpass these numbers in 2019, with $3B raised across 73 deals as of Q2'19.

  • A total of 17 companies have reached a unicorn valuation of $1B or more. GRAIL, Oscar Health, and We Doctor round out the top three most funded companies on the list.

  • 116 of the Digital Health 150 are headquartered in the United States. Those based outside the United States include 17 from Asia, 16 from Europe, and 1 from Canada.

  • Over 850 unique investors have funded the Digital Health 150, with F-Prime Capital, New Enterprise Associates, and Google Ventures taking the top three spots as the most active investors.

About CB Insights

At CB Insights, we believe the most complex strategic business questions are best answered with facts. We are a machine intelligence company that synthesizes, analyzes and visualizes millions of documents to give our clients fast, fact-based insights. Serving the majority of the Fortune 100, we give companies the power to make better decisions, take control of their own future, and capitalize on change.

 

About KenSci

KenSci’s machine learning-powered risk prediction platform helps healthcare providers and payers intervene early by identifying clinical, financial and operational risk to save costs and lives. KenSci’s platform is engineered to ingest, transform and integrate healthcare data across clinical, claims, and patient-generated sources. With a library of pre-built models and modular solutions, KenSci’s machine learning platform integrates into existing workflows allowing health systems to better identify utilization, variation and improve hospital operations. With Explainable AI models for healthcare, KenSci is making risk-based prediction more efficient and accountable.

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