Analytics

Large Language Models (LLMs) are AI systems trained on vast datasets to understand and generate human language. In healthcare, they are used for tasks like summarizing clinical notes, assisting with patient inquiries, and automating documentation. LLMs analyze large volumes of unstructured data to enhance decision-making, improve workflows, and support administrative and clinical processes. Their ability to process complex language makes them valuable tools for improving efficiency and accuracy in various healthcare settings.

Machine Learning encompasses a range of technologies, including Large Language Models (LLMs), classifiers, and various other machine learning models, APIs, services, and infrastructure. These tools are applied to analyze healthcare and life sciences data for purposes such as predictive analytics, patient diagnosis, R&D, treatment personalization, and operational efficiencies.

Risk Modeling products specialize in using data and predictive modeling to assess and manage risks associated with healthcare plans and patient outcomes. They focus on analyzing large datasets to predict healthcare costs, patient health trajectories, and the impact of interventions. These services typically leverage machine learning and statistical algorithms to forecast risk and to help healthcare providers, insurers, and policymakers make informed decisions. Some of the core capabilities include: - Cost and benefits projections across different types of interventions - Risk assessment for self-funded plans to enable budgeting and reserves-building