AWS Public Sector Blog

Category: Amazon EC2

Verify software supply chain security using AWS Nitro cryptographic verification

This post demonstrates how to use Amazon Elastic Compute Cloud (Amazon EC2) instance attestation to produce cryptographically verifiable Amazon Machine Images (AMIs) for your DevSecOps platform and pipeline—the software factory that builds and delivers your workloads.

AI-enabled genomic pattern discovery in Epstein-Barr virus on Amazon EC2

This post, which details a collaboration between a national genomics research institute and Temus using Amazon Web Services (AWS), demonstrates the power of combining deep bioinformatics with advanced AI models and machine learning methods in a scalable AWS environment to uncover novel patterns in complex viral genomes. In this post, we explore the details of a scalable proof of concept (POC) pipeline built on AWS that uses an advanced genomic foundation model and unsupervised clustering to transform raw genomic sequences into signals for further research.

$Fewer than 5 percent of radiologists review their own billing codes, compared to roughly 90 percent of primary care physicians. That gap has consequences: Over the past decade, it has contributed to nearly 50 percent reimbursement losses in radiology. In radiology, radiologists' adjacent personnel must translate every image read into standardized International Classification of Diseases, 10th Revision (ICD-10) codes that drive billing, follow-ups, and quality reporting. When those codes are unverified or poorly documented, the results are billing delays, claim denials, and delayed patient care. The University of Miami Health System (UHealth) partnered with AWS and AWS Partner Quantiphi to build a generative AI coding solution on Amazon Bedrock called Hurricode. The solution puts radiologists back in the loop, achieving approximately 92 percent coding accuracy, projecting a 34 percent revenue increase, and reducing manual effort by roughly 40 percent. The University of Miami Leonard M. Miller School of Medicine is Florida's first medical school and home to the Sylvester Comprehensive Cancer Center, the highest-ranked cancer center in Florida for cancer care in 2026. The Department of Radiology performs over 1 million procedures annually across eight specialized divisions. Quantiphi is an AWS Premier Tier Services Partner and AWS 2025 Public Sector Global Generative AI Consulting Partner of the Year. When manual coding can't keep up The UHealth radiology team faced a set of interconnected problems. The sheer volume of manual reporting fueled clinician fatigue. The department's legacy natural language processing (NLP) coding tool reached only 58–77 percent accuracy. It lacked the precision, scalability, and speed that modern diagnostic workflows demand, producing inconsistent ICD-10 code mapping and driving medical necessity denials. A lack of transparency compounded the inconsistency. The team needed defensible code identification in the form of clear, understandable rationales behind every ICD-10 prediction. Without it, clinicians couldn't trust or validate the output, and the department couldn't meet its compliance and audit requirements. UHealth serves a complex oncologic and tertiary care population where actionable incidental findings (AIFs) such as lung nodules, pulmonary emboli, and fractures demand timely follow-up. This makes accurate, well-documented coding a patient safety issue as much as a financial one. Building a radiologist-in-the-loop workflow on AWS To solve these challenges, UHealth and Quantiphi designed a solution with radiologists, studying their exact workflow to understand where AI could reduce friction without disrupting clinical judgment. The result is Hurricode, a generative AI–powered coding assistant built on Amazon Bedrock. Hurricode uses Amazon Bedrock to generate ICD-10 code suggestions with associated reasoning from transcribed radiology reports. Radiologists review and confirm the codes through a custom interface, a self-attestation process that takes less than 30 seconds per study. This radiologist-in-the-loop approach maintains clinical-grade accuracy while reducing manual effort by approximately 40 percent. The solution also accelerates upstream insurer verification (pre-authorization) for recommended further imaging, which occurs in 11–27 percent of advanced imaging studies. By surfacing accurate, well-documented codes earlier in the workflow, Hurricode helps streamline the pre-authorization process. The project began with a strategic assessment, roadmap, and proof of concept (PoC). The pipeline ingests, pre-processes, and passes radiology reports and the ICD-10 code directory to a fine-tuned model. Hurricode is the first solution to flag pertinent negative findings (PNFs)—conditions that have been ruled out, such as bleeds or fractures—improving documentation quality and supporting more complete clinical records. Future plans include surfacing AIFs for tracking, scheduling, and pre-authorization through the University of Miami No Findings Left Behind™ provenance network. Figure 1: ICD-10 Coding Automation Workflow for Radiology Reports How the solution comes together on AWS The following AWS services power the Hurricode solution: Amazon Bedrock — Foundation models including Amazon Titan Text Embeddings and Anthropic Claude for phrase extraction, code generation, and AIF identification Amazon SageMaker — Development and fine-tuning of the embedding model Amazon OpenSearch Service — Stores vectors for semantic search AWS Lambda — Orchestrates data ingestion and RAG (Retrieval Augmented Generation) pipeline Amazon EC2 — Additional compute Amazon S3 — Stores raw inputs, processed text, and web assets Amazon DynamoDB and Amazon RDS — Manage application and structured metadata Amazon CloudFront and Amazon Cognito — Deliver web application more securely, integrated with University of Miami single sign-on Figure 2: AWS Cloud Architecture for AI-Powered Medical Coding System Clinical-grade results at scale Hurricode has delivered measurable improvements across coding accuracy, operational efficiency, and clinical documentation: Approximately 92 percent coding accuracy, up from 58–77 percent Approximately 40 percent reduction in manual effort Radiologists spend less than 30 seconds per advanced imaging study Projected approximately 34 percent revenue increase through optimized revenue cycle management and advanced authorization Additional upside from CMS Quality Payment Program quality metrics Physician attestation improves documentation of PNFs in more than 50 percent of patients Projected 10–25 percent uplift in AIF follow-up through the No Findings Left Behind initiative Aims to reduce findings lost to follow-up from the industry norm of 20–40 percent to under 5 percent Strengthens metrics including length of stay, risk adjustment factor (RAF), and hierarchical condition category (HCC) reporting Scalable beyond 1 million advanced imaging reports (CT, MRI, PET) per year "The first-of-its-kind solution we developed with Quantiphi at the University of Miami, Hurricode, will particularly improve quality and safety, identify actionable findings in at-risk patients, and enable other proactive measures that we feel will profoundly contribute to the rapidly expanding field of preventive radiology." — Dr. Alexander M. McKinney, chair of the Department of Radiology at the University of Miami Figure 3: Hurricode AI Platform: Intelligent Radiology Findings and Clinical Integration Hub What's next In the next phase, UHealth plans to automate the transfer of key EHR data into required documentation for more timely and compliant processes. The team also plans to expand Hurricode beyond radiology into pathology, interventional neurosurgery, and cardiology. To explore how generative AI on Amazon Bedrock can transform your organization's workflows, connect with Quantiphi or visit the AWS Generative AI Innovation Center. About the authors Figure 4: Professional Headshot - Giorgia Rematska Giorgia Rematska, PhD Giorgia Rematska, PhD, is a principal architect and machine learning specialist at Quantiphi with over 7 years of expertise in traditional ML, deep learning, and generative AI. She has led initiatives including automated medical document processing, ICD-10 code prediction, and model distillation for privacy-preserving NLP. Figure 5: Professional Headshot - Rakesh Raghu Rakesh Raghu Rakesh Raghu is a senior partner solutions architect at AWS who helps AWS Partners design and build scalable, more secure cloud solutions for public sector customers. He specializes in cloud networking and connectivity and works on migrating workloads to AWS and architecting generative AI solutions. Figure 6: Professional Headshot - Shane Knisley Shane Knisley Shane Knisley is a partner solutions architect with AWS Worldwide Public Sector (WWPS) who helps partners and public sector customers design more secure, compliant workloads across AWS commercial and government Regions. He has over 20 years of IT and cybersecurity experience with deep expertise in RMF and FedRAMP processes.$

How UMiami and Quantiphi optimized radiology coding with Amazon Bedrock

The University of Miami Health System (UHealth) set out to close that gap in both downstream and upstream directions. Working with Amazon Web Services (AWS) and AWS Partner Quantiphi, the UHealth Department of Radiology built a generative AI coding solution on Amazon Bedrock that pairs AI with a radiologist attestation workflow, delivering clinical-grade accuracy while replacing error-prone manual processes.

Continuous monitoring under FedRAMP 20x: Replacing annual assessments with persistent validation

Under legacy Federal Risk and Authorization Management Program (FedRAMP) Rev5, continuous monitoring meant monthly deliverables and annual assessments. Under FedRAMP 20x, it means persistent, automated validation where the status of your security posture is always known. In this post, we operationalize that model using AWS Security Hub, AWS Config conformance packs, Amazon GuardDuty, Amazon Inspector, and Sigma detection rules to build an always-on monitoring architecture.

A person is holding a cell phone and typing on a laptop. The laptop screen shows a green lock symbol. Concept of security and protection

How to secure communications beyond encryption with AWS Wickr

Read this post to learn about AWS Wickr, a messaging and collaboration service that protects messaging, calling, file sharing, screen sharing, and location sharing with 256-bit end-to-end encryption (E2EE). Wickr combines advanced security for sensitive communications, administrative controls for user and policy management, and data retention for auditing and regulatory needs.

One scan, many findings: Floy’s foundation model platform for opportunistic screening on AWS

This post describes how Floy built an FM-based AI platform on AWS, from training and inference to clinical integration, that scales to meet the demands of modern radiology.

Building resilient government workflows with AWS Lambda durable functions

In this post, we review how AWS Lambda works today, introduce durable functions, walk through a document processing use case for child welfare, and explain when to choose durable functions over AWS Step Functions.

Digital Earth Africa and AWS are making satellite data work for Africa

Amazon Web Services (AWS), through the Open Data on AWS and Amazon Sustainability Data Initiative (ASDI) programs, became a foundational collaborator from the outset, hosting Digital Earth Africa’s data on AWS infrastructure in Cape Town, South Africa, and enabling African users to access and analyze petabytes of satellite data at a speed and scale that would otherwise be out of reach.

Building machine-readable FedRAMP 20x evidence on AWS

In this post, we walk through the evidence pipeline, from Amazon Web Services (AWS) Config evaluations and AWS Security Hub findings through transformation and storage, to producing the dual-format output that satisfies FedRAMP 20x Phase 2 completeness requirements.

Run SAP workloads at DoD Impact Level 5 with SAP NS2 on AWS GovCloud (US)

In this post, we explain what IL5 requires, how AWS GovCloud (US) and SAP NS2 meet those requirements together, and how defense organizations can get started.

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