DigitalOwl, the first and only Natural Language Processing (NLP) technology for deeply analyzing and summarizing medical records, announced a $20M Series A round led by New York-based global private equity and venture capital firm Insight Partners, with participation from existing investors Ibex and Fusion VC. This latest round of fundraising will be used for additional hires that will strengthen existing teams, particularly technology development, sales, marketing and client service. The round quickly follows the company’s $6.5M seed announcement in February 2021
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Tens of millions of medical records every year are manually reviewed by highly trained professionals at insurance companies in order to make important decisions for their company and customers. The sheer volume and complexity of information requires a slow and methodical review of every page, often taking days at a time. DigitalOwl addresses this issue by reading, summarizing, and analyzing medical records for insurance and legal companies to help them better combat fraud, underwrite new plans, execute claims, and build strong legal cases.
All customers have to do is upload all scanned medical records to DigitalOwl’s platform. Once uploaded, DigitalOwl generates a focused data set with multiple filtering options and easy-to-use navigation. Every condition, date, and entry is clickable, redirecting users to the source of information in the record. The resulting dataset is formatted in the form of a meaningful summary that puts the most relevant data at the user’s fingertips, all in a fraction of the time and at a fraction of the cost. “For our current and future customers – insurance companies, reinsurers, TPA’s, and others in the insurance and legal industries – this investment will allow us to innovate faster, bringing them the most advanced medical data analysis services available, backed by world-class customer service and a deep bench of domain expertise,” said Yuval Man, CEO and Co-Founder of DigitalOwl. “It will also support a more robust R&D process, allowing us to unlock valuable solutions for other industries that utilize medical data.”
“Medical record reading and analysis for insurance and legal industries has long needed to be modernized. The need only grew through COVID-19, as it became nearly impossible for insurers and law firms to hire nurses, doctors, or other claims analysts to manually read medical records,” said Ryan Hinkle, Managing Director at Insight Partners. “DigitalOwl’s impressive founding team has addressed this need with cutting edge technology that uses AI to digitize an antiquated industry. We look forward to partnering with DigitalOwl as they continue to grow.”
DigitalOwl currently operates in the US, Canada and Israel to help companies get through the tedious process of manual underwriting and claims analysis. The average medical record holds more than 44,000 words entailing more than 2900 medical extractions, data points such as procedures, conditions, and medications. DigitalOwl’s technology solution extracts over 17,000 medical data points from a variety of different conditions, procedures and medications including cancer, heart disease, accidents, orthopedic surgeries, diabetes, arthritis, hypertension and brain injuries, with greater than 95 percent accuracy.
“Our solution is a proprietary Natural Language Processing technology that extracts information from hundreds or thousands of pages of scanned medical records in minutes and presents that data chronologically, allowing users to search and filter by condition, date, body part, body system, and provider,” said Amit Man, CTO and Co-Founder. “The DigitalOwl system is capable of extracting twice as many meaningful medical data points as any human-enabled solution, at a fraction of the cost.”
About DigitalOwl
DigitalOwl offers an innovative solution for analyzing and summarizing medical records for the insurance industry. The Company has introduced the first and only machine learning platform that interprets medical records and assists underwriters and claim adjusters in their work.