LLAMA 3.1 8B, 70B, 405B and LLAMA 3.2 3B

Sofya

30%

less time spent on paperwork during patient visits

90%

customer satisfaction (CSAT) score

+Feedback

Healthcare providers report better workflow efficiency and patient care outcomes

projected AI-assisted patient visits a month by 2025

^*All results are self-reported and not identifiably repeatable. Generally expected individual results will differ.

CASE STUDY

Healthcare solution brings Llama-powered clinical reasoning to patient care and record management

At a glance

Industry

Healthcare

Use case

Streamlining patient assessments and automating documentation with AI-powered clinical reasoning

Goal

Reduce administrative tasks and improve patient care

Llama versions

Llama 3.1 8B, 70B and 405B; Llama 3.2 3B

Deployment

Dedicated infrastructure on Oracle Cloud

Download full case study

THEIR STORY

Reimagining healthcare with clinical reasoning AI

Sofya works with cutting-edge healthcare institutions and researchers to push the boundaries of healthcare innovation and bioinformatics. Built on a solid research foundation, Sofya continues to publish academic papers and open source its models and datasets as it commercializes its clinical reasoning technologies.

THEIR GOAL

Help healthcare providers focus on patients instead of paperwork

Digital healthcare administration is overwhelming providers. Physicians spend 2.5 to 8.4 hours documenting care in electronic health record platforms for every eight hours of scheduled patient time. Across specialties, the average is 3.4 hours.Sofya saw an opportunity to give time back to providers by streamlining healthcare record management and providing AI-powered assistants that could enhance clinical reasoning during patient consultations.

THEIR SOLUTION

Use Llama models to rationalize healthcare documentation, improve bedside care

Sofya developers fine-tuned a series of Llama models to create a multi-agent solution that helps structure healthcare data, applies clinical reasoning and assists providers during patient consultations. The Sofya clinical assistant guides providers through taking patient histories, flags concerns, offers diagnoses and creates personalized care plans.The team chose Llama models for their adaptability and capacity to produce high-quality clinical documentation. Open-source Llama freed Sofya developers to customize models and deploy them on their own infrastructure for maximum data control and security, a key requirement for the healthcare industry.

The Sofya clinical assistant helps capture patient information, formulate diagnoses and deliver care.

THEIR APPROACH

Fine-tune expert models for each role and application

Developers generated high-quality synthetic training data for the Sofya solution using Llama 3.1 405B and a combination of knowledge distillation and self-reflection prompts. To deliver clinical assistance from desktop to mobile, the solution uses trained and fine-tuned Llama 3.1 70B and 8B and Llama 3.2 3B models to balance latency, performance and computing resources.Llama models power the heart of the Sofya solution — a clinical reasoning system with humans in the loop that clarifies terms and structures healthcare information into unified, medical protocols and decision-support standards. These standards serve as programmatic rules that human caregivers, automated workflows and other applications can follow.

Fine-tuned Llama models power Sofya’s clinical reasoning system.

THEIR SUCCESS

Time savings for providers, lower costs for organizations

Sofya customers report providers are spending less time on documentation and that overall costs have decreased. Healthcare providers report better, more efficient workflows and, most importantly, improved patient care.