Raising the bar for AI-powered clinical documentation

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A deep dive into Nabla’s evaluation methods, showcasing best practices in AI-driven documentation with a focus on accuracy, adaptability, and transparency.

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Written by : Samuel Humeau, Lead Machine Learning Engineer

A robust evaluation framework

Over three years, Nabla refined its clinical note generation engine through a rigorous evaluation framework built for real-world care.

This whitepaper explores how we assess performance across speech recognition, note generation, and coding to ensure consistent, high-quality outputs clinicians can trust.

Built for clinical complexity

Nabla combines advanced speech-to-text, customizable documentation, and ICD-10 coding into a system designed to adapt to diverse clinical workflows and specialties.

Rigorous evaluation and safe deployment

Every system update undergoes comprehensive testing, combining automated evaluations with expert clinical review. Changes are introduced progressively to ensure safety, reliability, and stability in production environments.

Continuous improvement through clinician feedback

Clinician input plays a central role in how Nabla evolves. Feedback from real-world use is continuously incorporated to refine performance and align outputs with clinical expectations.

Transparency and measurable quality

Nabla maintains detailed monitoring and evaluation metrics to ensure consistent performance. Organizations are equipped with clear visibility into system behavior and output quality.

Reducing documentation burden

By improving the reliability and efficiency of clinical documentation, Nabla helps reduce administrative overhead and supports better clinician experience and patient care.

Setting a higher standard for clinical AI

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Nabla is committed to rigorous, transparent evaluation processes that ensure its AI performs reliably in real clinical environments. This whitepaper outlines the principles and methods that underpin that standard.

Read whitepaper

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Raising the bar for AI-powered clinical documentation

Last updated: January 13, 2025

A deep dive into Nabla’s evaluation methods, showcasing best practices in AI-driven documentation with a focus on accuracy, adaptability, and transparency.

Download the whitepaper

Written by

Samuel Humeau

Lead Machine Learning Engineer

A Robust Evaluation Framework

Our team has spent 3 years refining our AI-powered clinical note generation engine, establishing Nabla as a leading ambient AI assistant.

Discover how our evaluation framework sets new standards for reliable AI-generated clinical notes.

Delivering specialized capabilities

With advanced speech-to-text, customizable note generation, and ICD-10 coding, Nabla remains committed to supporting clinicians with reliable and adaptable solutions.

Ensuring rigorous evaluation and safe rollout

Nabla commits to thorough testing of each system update, combining automated evaluations with expert reviews to ensure accurate, reliable AI-generated notes, introduced progressively and safely.

Prioritizing clinician’s feedback

Nabla actively incorporates clinicians feedback to continually refine and improve its systems.

Maintaining transparency and quality

Through detailed monitoring and real-time metrics, Nabla ensures its systems deliver consistent quality while providing organizations with tools for clear oversight.

Committed to alleviating clinician burnout

Nabla is dedicated to reducing the burden of clinical documentation, supporting clinician well-being and enhancing patient care.

Advancing reliable AI-powered clinical documentation

At Nabla, we are committed to transparent and rigorous evaluation processes to ensure our ambient AI assistant meets the highest standards. Discover how we set the benchmark for reliability and adaptability through meticulous evaluation, progressive rollouts, and clinician feedback.