What challenges did UiPath face before AI implementation?

Healthcare organizations face inefficiencies and high costs in manual summarization of voluminous medical records, lengthening prior authorization turnaround and impacting clinical decisions. Manual processes are time-consuming, inconsistent in quality, and prone to errors, requiring costly rework and delaying clinical workflows.

What was the impact of the AI solution?

Reduced prior authorization processing time by up to 50%. Saved up to 40 minutes per patient referral. Improved accuracy and standardization of summaries, reducing errors. Deployed with a major healthcare payer achieving 23% faster document processing and cost savings.

Source

GCP

UiPath Medical Record Summarization AI Agent Powered by Google Cloud Vertex AI and Gemini Models

Use case typeClinical documentationUpdated Jun 13, 2026

UiPath developed a generative AI-based Medical Record Summarization AI agent to automate the summarization of voluminous medical records, leveraging Google Cloud Vertex AI and Gemini 2.0 Flash model. The agent provides clinician-level, multi-point summaries with traceable citations, using retrieval-augmented generation (RAG) technology to process unstructured medical data. It reduces prior authorization turn-around time by up to 50%, saves up to 40 minutes per referral, and cuts summary completion time from 45 minutes to just minutes. The solution improves accuracy and standardization of medical summaries, reducing errors and enabling better clinical decision making. It is already deployed by a major healthcare payer, reporting 23% faster document processing times and significant cost avoidance.

Organization: UiPath
Industry: Healthcare
Location: United States
Published: April 2025

Reported outcomes

−50%

timeTime & speed

40 minutestime−23%time

Strategic outcomes

New product / capabilityAutomated medical record summarizationSpeed & agilityAccelerated prior authorization processingBetter decisions & insightImproved summary accuracy and standardizationCost efficiencyAchieved cost avoidance in processing

Catalog median for time & speed deployments: −60% across 727 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 2 of 2

1Generative AI summarization
2AI-assisted clinical documentation

Healthcare organizations face inefficiencies and high costs in manual summarization of voluminous medical records, lengthening prior authorization turnaround and impacting clinical decisions.
Manual processes are time-consuming, inconsistent in quality, and prone to errors, requiring costly rework and delaying clinical workflows.

UiPath built a generative AI agent using Google Cloud Vertex AI and Gemini models.
The agent uses RAG technology to handle unstructured medical records and produces structured, standardized summaries quickly and accurately.
The agent was developed with clinical input to ensure quality and relevance in healthcare settings.

Technologies

Google Cloud Vertex AI Google Cloud Gemini 2.0 Flash

Reduced prior authorization processing time by up to 50%.
Saved up to 40 minutes per patient referral.
Improved accuracy and standardization of summaries, reducing errors.
Deployed with a major healthcare payer achieving 23% faster document processing and cost savings.

Architecture

The solution integrates Google Cloud Vertex AI and Gemini 2.0 Flash models with UiPath Agent Builder to provide a generative AI agent that performs multi-point, clinician-level summarization of medical records using retrieval-augmented generation (RAG) technology.

Sources & evidence1

Groundedness: 5/5

Primary source

AI-generated summary. Verify important details with the linked sources before relying on this case.

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Customer evidence