Reveleer processes 45 million pages of medical records quarterly with AI on AWS
Healthcare data and analytics company Reveleer uses Amazon Textract and Amazon Comprehend Medical to scan medical chart data and identify diagnostic conditions with high accuracy for value-based care programs, highlighting findings for medical coders so they don't have to manually review thousands of pages per patient. The Reveleer platform maintains 100% uptime with 90% of responses served in under 8 seconds, and processed over 45 million pages of medical chart data in the first quarter of 2024. Reveleer plans to add Amazon Bedrock foundation models to its platform.
Overview
Healthcare data and analytics company Reveleer uses Amazon Textract and Amazon Comprehend Medical to scan medical chart data and identify diagnostic conditions with high accuracy for value-based care programs, highlighting findings for medical coders so they don't have to manually review thousands of pages per patient. The Reveleer platform maintains 100% uptime with 90% of responses served in under 8 seconds, and processed over 45 million pages of medical chart data in the first quarter of 2024. Reveleer plans to add Amazon Bedrock foundation models to its platform.
The challenge
Value-based care requires healthcare organizations to analyze vast amounts of medical chart data to provide accurate diagnoses, treatment plans and reimbursement. Reveleer needed to expedite this process, which involves millions of pages of medical records at scale, while maintaining a high degree of accuracy.
The solution
Reveleer uses Amazon Textract to extract text, handwriting, layout elements and data from scanned medical documents, and Amazon Comprehend Medical to extract information from unstructured medical text, letting Reveleer scan medical records and identify diagnostic conditions with high accuracy and highlight findings to medical coders so they don't have to manually review thousands of pages.
Reported business value
The Reveleer platform maintains 100 percent uptime with 90 percent of responses served in under 8 seconds, and processed over 45 million pages of medical chart data in the first quarter of 2024 — numbers Reveleer expects to grow. Reveleer plans to incorporate more AWS services, including Amazon Bedrock foundation models, in the future.
Sources
Open any source and check the claim yourself — that is the point of the register.
This record was researched and written with AI assistance, and its claims were checked against the sources above. (EU AI Act art. 50 transparency notice.)
Other healthcare entries in the register.
Less paperwork, more time for people: Azure OpenAI at Landsberg Hospital
Klinikum Landsberg am Lech, a German acute-care hospital, deployed an AI language solution built on Azure OpenAI with partner Pexon Consulting that transcribes doctor-patient consultations in real time via tablet, recognizes medical terms, structures the content, and transfers it directly into the hospital information system.
Novo Nordisk builds AI drug discovery platform on Azure with Microsoft Research
Novo Nordisk partnered with Microsoft Research to build an AI platform on Azure AI and data stacks spanning regulatory affairs, early research, drug discovery and trial design, using Azure OpenAI Service, Azure Cosmos DB and Azure Kubernetes Service, with Power BI and Power Apps for collaboration. The platform includes a copilot for researchers, shared reasoning-chain templates, and governance/auditing of how data and models are used. The teams published early results on predictive AI models for cardiovascular disease risk detection, including an algorithm that Novo Nordisk says predicts patients' cardiovascular risk better than the best clinical standards, drawing on more than 100 years of insulin research data.
BAYADA Builds a Unified, AI-Ready Platform for Compassionate Care
BAYADA Home Health Care is consolidating three separate legacy data platforms (multiple practice management systems, an on-prem ODS, and Snowflake) and 65+ enterprise data sources into a single Databricks Lakehouse under its Data Modernization program, using medallion tiers, Lakeflow Jobs, Asset Bundles, and Unity Catalog governance. As part of the migration, BAYADA uses an LLM-powered code converter and Databricks Assistant to automate SQL and stored-procedure translation, and applies a machine learning-based data mastering accelerator to create golden Client, Payor, Candidate, and Referrer records. BAYADA reports roughly 30% better workload performance and cost efficiency versus its prior Snowflake environment, and is laying a governed foundation for AI agents supporting payroll validation, compliance monitoring, and operational insights, separately from its roadmap for AI-assisted chart review and risk prediction.
Heidi Health fine-tunes NVIDIA Parakeet/Nemotron ASR, cutting clinical transcription latency 75% and costs 64%
Heidi Health, which processes 2.4 million clinical consultations weekly across 110 languages, fine-tuned the open NVIDIA Parakeet V2 speech recognition model on 1,500 hours of curated clinical audio using the NVIDIA NeMo framework and 8x H100 GPUs, replacing its closed-source vendor ASR. The in-house Nemotron-based ASR stack cut end-to-end transcription latency from about 3.0 seconds to 0.7 seconds (75%+ improvement), reduced word error rate from 13% to 9.4%, and cut operating costs by 64% by eliminating per-minute API pricing, while giving Heidi full ownership of model weights for data sovereignty.
Was this helpful?
Your feedback helps us improve our use case database

