How UMiami and Quantiphi optimized radiology coding with Amazon Bedrock

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) 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.

UHealth is a natural place for this innovation. The University of Miami Leonard M. Miller School of Medicine is Florida’s first medical school and home to Sylvester Comprehensive Cancer Center, which is the highest-ranked center for cancer care in Florida in 2026. Its Department of Radiology is a national leader in diagnostic and interventional imaging, performing over 1 million procedures annually across eight specialized divisions. Quantiphi, an AWS Premier Tier Services Partner and the AWS 2025 Public Sector Global Generative AI Consulting Partner of the Year, brought AI-first digital engineering expertise to the effort as a founding member of the AWS Generative AI Innovation Center.

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

UHealth wanted to predict ICD-10 codes directly from transcribed reports, but given the potential for hallucinations when using AI to do this task, clinical-grade accuracy was non-negotiable. The team designed the solution with radiologists, studying their exact workflow. Built with Quantiphi and called Hurricode, it uses Amazon Bedrock to generate ICD-10 code suggestions with associated reasoning, which radiologists then review and confirm through a custom interface within their workflow. This self-attestation model takes less than 30 seconds, anchoring accuracy and compliance while cutting manual billing efforts and turnaround time. It also accelerates upstream insurer verification (pre-authorization) for recommended further imaging, which occurs in 11–27 percent of advanced imaging.

The project began with a strategic assessment to identify high-impact use cases and a roadmap, culminating in a proof of concept (PoC) that validated the accuracy and reliability of the large language model (LLM)-driven coding and attestation system.

As shown in Figure 1, the pipeline ingests, pre-processes, and passes radiology reports and the ICD-10 code directory to a fine-tuned model that maps codes to relevant radiology language. Radiologists then review the suggested codes and reasoning, attest to them, and feed corrections back for continuous improvement before the results flow downstream to billing:

Figure 1: The Hurricode coding workflow, from report ingestion through ICD-10 code generation on Amazon Bedrock to radiologist review and attestation, then downstream billing

Beyond generating ICD-10 codes (including Z codes) from clinical narratives, the solution is the first to flag pertinent negative findings (PNFs)—conditions ruled out, such as bleeds or fractures—to help expedite care and potentially reduce length of stay in the hospital for patients.

In the future, the solution will surface AIFs so at-risk patients can be tracked, scheduled, and pre-authorized for follow-up through the University of Miami No Findings Left Behind ™ provenance network.

How the solution comes together on AWS

At the core, Amazon Bedrock provides access to foundation models—including Amazon Titan Text Embeddings and Anthropic Claude models—for phrase extraction, code generation, and AIF identification. Amazon SageMaker supports development and fine-tuning of the embedding model, and Amazon OpenSearch Service stores its vectors to enable the semantic search.

AWS Lambda orchestrates data ingestion and the Retrieval Augmented Generation (RAG) pipeline, with Amazon Elastic Compute Cloud (Amazon EC2) providing additional compute. Amazon Simple Storage Service (Amazon S3) stores raw inputs, processed text, and web assets. Amazon DynamoDB and Amazon Relational Database Service (Amazon RDS) manage application and structured metadata. Amazon CloudFront and Amazon Cognito deliver the web application more securely, integrated with University of Miami single sign-on, as shown in Figure 2:

Figure 2: Technical architecture of the Hurricode solution

Clinical-grade results at scale

In real-world data, Hurricode reached roughly 92 percent coding accuracy, which is up from the 58–77 percent accuracy rate of prior solutions, while cutting manual effort by about 40 percent and alleviating radiologist overload.

The radiology team projects that correct identification and physician attestation of ICD-10 codes will increase revenue by roughly 34 percent through optimized revenue cycle management and advanced authorization, with additional upside from tailored quality metrics under the Centers for Medicare & Medicaid Services (CMS) Quality Payment Program. Radiologists now spend less than 30 seconds per advanced imaging study, freeing time for direct patient care.

The team built the solution to scale beyond the department’s current volume of 1 million advanced imaging reports—including CT, MRI, and PET—per year. Physician attestation now improves documentation of, and legitimate reimbursement for, PNFs in more than 50 percent of patients, and the team projects a 10–25 percent uplift in AIF follow-up through the No Findings Left Behind initiative, which aims to reduce findings lost to follow-up from an industry norm of 20–40 percent to under 5 percent. Because clearly documented findings feed directly into hospital quality and reimbursement measures, the solution also strengthens metrics such as length of stay, risk adjustment factor (RAF), and hierarchical condition category (HCC) reporting.

“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,” says Dr. Alexander M. McKinney, chair of the Department of Radiology at the University of Miami.

What’s next

The UHealth–Quantiphi collaboration shows how a carefully governed, human-in-the-loop generative AI system on Amazon Bedrock can turn inaccurate, delayed, or unverifiable ICD-10 coding into a source of clinical, operational, and financial value, as shown in Figure 3.

The next phase of UHealth will automate the transfer of key data from the electronic health record (EHR) into the required documentation, creating a more timely and compliant process for patients and providers. Beyond the radiology department, UHealth hopes to soon expand Hurricode into pathology, interventional neurosurgery, and cardiology.

Figure 3: Hurricode combines radiologist input with generative AI to support billing, incidental findings tracking, quality reporting, and patient follow-up across all UHealth care pathways

To explore how generative AI on Amazon Bedrock and the AWS Generative AI Innovation Center can help your organization modernize clinical workflows, connect with Quantiphi to start the conversation.