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Guardoc Health deploys Amazon Nova to automate clinical documentation

Guardoc Health is processing over one million clinical documents daily using Amazon Nova models via AWS Bedrock. By integrating multimodal reasoning into its documentation pipeline, the company aims to mitigate the high-stakes financial and safety risks associated with manual data entry errors in long-term care settings.

Guardoc Health deploys Amazon Nova to automate clinical documentation

The healthcare provider’s architecture prioritizes cost-efficiency by funneling data through a multi-stage retrieval pipeline. Amazon Textract initially extracts text and metadata, which is then chunked and indexed in Amazon DynamoDB using Amazon Titan Text Embeddings V2. This tiered approach ensures that expensive, computationally intensive multimodal reasoning is reserved only for the final analysis stage, where Amazon Nova Pro interprets complex layouts, handwritten annotations, and physician signatures.

According to internal data, this system has yielded significant operational improvements. Guardoc reports a 46 percent reduction in documentation errors and a 70 percent decrease in audit fines. In a specific deployment covering seven facilities and 1,618 residents, the system identified over 10,600 documentation issues. These figures highlight the challenge of standardizing clinical records, which frequently arrive as a mix of structured digital tables, handwritten physician notes, and low-quality faxed scans. By automating the classification of these disparate inputs, the company claims to have achieved an annual return of $400,000 per facility, while simultaneously reducing hospital transfers by 74 percent per 100 admissions.

Assaf Amiaz, Director of Product at Guardoc Health, noted that the integration allows healthcare teams to identify high-risk cases earlier. By moving away from manual oversight, the firm seeks to close compliance gaps that previously resulted in denied Medicare claims or missed patient diagnoses. The reliance on Nova models specifically addresses persistent bottlenecks like physician attestation fields and medication lists, where handwritten additions often contradict printed forms, requiring the model to reason across both visual layout and textual content.

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