emtelligent: How This Company Helps Healthcare Institutions Operate More Efficiently
Pulse 2.0 interviews emtelligent Chief Growth Officer Kim Perry to learn more.
By Amit Chowdhry ● November 15, 2023
Formation of emtelligent
How did the idea for the company come together? Perry said:
“The inspiration for emtelligent came from the frustration of cofounder, Timothy O’Connell, M.D., over the inability of traditional natural language processing (NLP) to provide clinicians accurate and well-organized patient information at the point of care. As an emergency radiologist, Dr. O’Connell always wanted more patient data to guide his clinical recommendations and decision-making, but the existing NLP software couldn’t extract information from unstructured data, which comprises 80% of all medical data. Dr.
O’Connell also had long experience in computers, so he worked with another cofounder, Anoop Sarkar, Ph.D., to create a medical-grade NLP platform capable of extracting and normalizing unstructured data. emtelligent launched in 2016.”
Core Products
What are the emtelligent’s core products and features? Perry explained:
“Our proprietary, enterprise-scale emtelliPro NLP engine is purpose-built to reveal key insights buried within unstructured medical text. Utilizing advanced deep-learning models and a custom medical annotation framework, the emtelliPro engine ensures greater accuracy across healthcare contexts. Built by medical experts to mimic their own understanding of medical language, emtelligent’s models transcend traditional NLP capabilities to enable an in-depth understanding of clinical context and complex relations.”
“emtelliSuite is our collection of medical apps that demonstrate how emtelligent’s medical AI solution improves the efficiency and effectiveness of care providers,
researchers, and administrators. Apps include a specially trained search engine, a smart summary app, a follow-up detector, an EMR data analyzer, a trainee scoreboard, and a tool that highlights abnormal findings and follow-up recommendations in text-based diagnostic reports.”
Challenges Faced
What challenges has the team faced in building the company? Perry acknowledged:
“A major hurdle for us when we started was that the market wasn’t ready for our product, which really is a medical-grade AI platform. That was because customers didn’t
understand that what we were doing was vastly different and superior to what they had seen from traditional NLP in healthcare until then. And what they had seen wasn’t very impressive.”
“As I mentioned, traditional NLP hasn’t been able to extract unstructured data, so most healthcare organizations that tried it were underwhelmed. Convincing them that emtelligent’s medical-grade AI is a quantum leap beyond traditional NLP has been challenging because it requires them to be educated about the capabilities of large language models (LLMs), and that doesn’t happen overnight. Still, we’ve made – and continue to make – great progress in explaining how emtelligent technology can transform healthcare.”
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