Recent advancements in large language models (LLMs) demonstrate their effectiveness in understanding complex contexts and simulating human-like interactions. These capabilities position LLMs as valuable tools for interpreting intricate data and facilitating communication. However, in fields such as health care, where compliance, accuracy, and adherence to regulatory standards are crucial, the integration of symbolic AI remains essential.
To mitigate the limitations of LLMs, a hybrid architecture combining LLMs, reinforcement learning, and structured knowledge bases is being explored. This approach aims to enhance reasoning capabilities and decrease instances of inaccuracies by ensuring that decisions are based on established clinical guidelines.
Ensemble’s approach to agentic AI relies on three fundamental principles:
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High-fidelity datasets: Ensemble manages revenue operations for numerous hospitals, providing access to extensive administrative datasets. Their experience in data aggregation and cleansing has resulted in the harmonization of over 2 petabytes of longitudinal claims data, 80,000 denial audit letters, and 80 million annual transactions, which are essential for powering their end-to-end intelligence engine, EIQ. This system streamlines data pipelines across over 600 revenue operation steps.
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Collaborative domain expertise: Ensemble emphasizes collaboration between AI scientists and revenue cycle management (RCM) experts. This partnership facilitates the development of nuanced applications that accommodate regulatory requirements and the complexities of revenue cycle processes. Feedback from end-users post-deployment aids in early identification of issues, allowing for rapid improvements.
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Elite AI scientists: The company’s research and development team consists of experts with advanced degrees from prestigious institutions, as well as extensive experience in prominent technology companies. This talent allows Ensemble to pursue groundbreaking research in LLMs, reinforcement learning, and neuro-symbolic AI in a focused, mission-driven environment.
The integration of these elements aims to create an AI strategy that is both effective and compliant within the complexities of the health care sector.
Source: https://www.technologyreview.com/2025/08/28/1122623/from-pilot-to-scale-making-agentic-ai-work-in-health-care/