Medical records are long, complicated, and often not organized in a simple way. They include notes from doctors, lab test results, imaging reports, patient history, and billing details. Summarizing this information both correctly and quickly is needed for many tasks like prior authorization reviews, patient referrals, processing orders, and managing usage. Usually, healthcare workers or office staff spend about 45 minutes per referral reviewing and summarizing these records by hand. This work can be slow and inconsistent because record formats vary, and it is easy to make mistakes. All this adds more work for the staff.
Hospital and clinic managers, as well as IT leaders in the U.S., know these problems cause delays in approvals, add extra costs, and may affect patient care. Finding better solutions is important, especially with more patients and fewer staff available.
One important new AI tool to help solve this is the UiPath Medical Record Summarization AI agent, created in partnership with Google Cloud. This software uses powerful machine learning models like Google Cloud’s Vertex AI and Gemini 2.0 Flash to process and summarize many medical records quickly and accurately.
This AI agent can cut the time needed for prior authorization by up to half. It can finish summaries in just a few minutes, saving around 40 minutes for each patient referral compared to doing it by hand. For managers who handle hundreds or thousands of referrals each year, this saves a lot of time and money.
The system uses a method called retrieval-augmented generation (RAG). This helps it deal with unorganized medical data by producing clear, clinician-level summaries with key points. These summaries are consistent and easy to follow, helping doctors make better decisions by giving them organized and reliable information. Automation also reduces errors caused by different writing styles or missing notes, which are common problems in healthcare.
Having correct and fast documentation is very important for good clinical decisions. AI-generated summaries let doctors and staff get all the needed patient information right away without having to spend extra time going through records manually. Leaders in healthcare who use this technology say that it cuts down the time needed for clinical review while improving the quality and accuracy of medical records.
Mark Geene, UiPath’s Senior Vice President of AI Products, said this automation not only makes work faster but also saves a lot of time and money each year for healthcare groups. Google Cloud’s Shweta Maniar also mentioned that the AI agent helps make utilization management, appeals, and clinical trial reviews smoother.
This means treatments and referrals are approved faster, patient needs are identified better, and mistakes that could harm patient safety are reduced. The quicker workflow leads to a better experience for patients because delays related to authorizations and paperwork go down.
AI tools like Natural Language Processing (NLP), machine learning, and deep learning are also being used in other areas of healthcare work in the U.S. NLP helps computers understand human language and is used to automate writing clinical notes, lower doctor burnout, and improve note accuracy.
For example, Microsoft’s Dragon Copilot automates transcribing clinical notes, referral letters, and after-visit summaries. This automation stops doctors from spending too much time typing, so they can spend more time with patients. In many American hospitals where doctor burnout is an issue, these tools help doctors be happier and improve patient care.
More hospitals are also adding AI to Electronic Health Record (EHR) systems. Combining AI transcription and summarization tools with EHR software makes work smoother by lowering errors, keeping documentation uniform, and allowing quick access to patient data. The Mayo Clinic Proceedings: Digital Health showed research saying AI-powered real-time transcription makes medical notes faster and more accurate, which helps clinical work go better.
Administrative tasks like scheduling patients, billing, processing referrals, and managing medical records use a lot of staff time in clinics and hospitals across the U.S. AI workflow automation tools are changing these jobs by speeding up repeated tasks, cutting down data entry by hand, and making data more accurate.
UiPath’s platform shows how this can work. It mixes AI agents, robotic process automation (RPA), and human supervision. This system helps coordinate AI bots and staff to handle difficult workflows well. For example, it can send medical summaries to the right doctors, flag missing records, and send alerts so staff act quickly on needed authorizations.
AI can also help use resources well. Predictive models can guess how many patients will come and help plan staff schedules. Automating authorizations, appeals, and trial screening cuts backlogs and helps clinical work flow more smoothly.
Many healthcare groups in the U.S. want AI tools that are easy to scale, secure, and cost-effective. The UiPath platform fits well with current healthcare systems through Google Cloud’s marketplace. It offers strong security, fast setup, and tools to build custom workflows without much coding, which helps busy clinics adopt AI.
Use of AI in clinical and administrative work is growing in the U.S. According to a 2025 American Medical Association (AMA) survey, 66% of doctors use health AI tools now, up from 38% in 2023. Also, 68% said AI helps patient care, showing trust in AI is rising.
The healthcare AI market size is expected to jump from $11 billion in 2021 to nearly $187 billion by 2030. This rise comes from improvements in AI like generative AI, NLP, and predictive analytics. These technologies are expected to lower administrative work and improve accuracy and patient results across the country.
Rules and regulations are also changing. For example, the U.S. Food and Drug Administration (FDA) watches AI health devices more closely to ensure they work safely and well. Healthcare groups that use AI need to follow laws about data privacy, openness, and responsibility.
Healthcare organizations in the U.S. thinking about AI for medical record summaries and workflow automation should consider several points:
UiPath’s AI platform, supported by Google Cloud, addresses many of these concerns. It offers strong security, fast setup, and easy tools to customize workflows without needing to code a lot. This helps busy clinics bring in AI smoothly.
By using generative AI tools like the UiPath Medical Record Summarization agent, healthcare managers, clinic owners, and IT staff in the U.S. can improve how they work. These tools reduce extra administrative work and help provide better patient care by giving timely, accurate information. As AI keeps improving, it will become more important for hospitals and clinics to manage growing workloads and improve patient care.
The UiPath Medical Record Summarization AI agent is a generative AI-based tool developed in partnership with Google Cloud that automates the summarization of voluminous medical records. It provides clinician-level multi-point summaries quickly and accurately, reducing manual entry time from about 45 minutes to just a few minutes, thus enhancing operational efficiency in healthcare organizations.
The agent improves prior authorization by reducing overall turn-around time by up to 50%. It decreases time spent on patient referral intake, order intake, and utilization management reviews by up to 40 minutes per referral, enabling faster and more accurate processing of prior authorizations for healthcare providers and payers.
The solution leverages Google Cloud Vertex AI with advanced Gemini 2.0 Flash models for generative AI capabilities. It uses state-of-the-art retrieval-augmented generation (RAG) to process unstructured medical records and generate structured, traceable summaries efficiently.
Benefits include significant time and cost savings by reducing manual summarization effort, improved accuracy and quality of medical summaries, consistent standardized documentation, fewer errors, and enhanced clinical decision-making speed and confidence through organized, traceable data presentation.
UiPath’s platform offers agentic automation that models and orchestrates agents, robots, and human-in-the-loop workflows end-to-end. It integrates AI, API, and rules-based tools, enabling healthcare organizations to deploy and manage automation quickly for complex clinical and administrative processes with security and governance.
The partnership allows UiPath to utilize Google Cloud’s Vertex AI and Gemini models to provide powerful machine learning-driven automation solutions tailored for healthcare. It supports seamless, scalable deployment of automation on Google Cloud infrastructure, simplifying and accelerating AI-powered transformation for healthcare customers.
Processes such as utilization management, appeals, referrals, order intake, and clinical trial eligibility checks benefit from faster and more accurate medical record processing, reducing administrative burden across both payer and provider organizations.
By delivering standardized, clinician-level summaries with traceable citations in organized sections, the agent ensures consistent data quality. This reduces variability and human error common in manual summarization, enhancing clinical decision support and documentation fidelity.
The automation reduces the time and effort clinical and non-clinical staff spend on summarizing medical records, alleviating resource constraints. It lowers the need for rework and manual data entry, optimizing staff utilization and allowing focus on higher-value clinical tasks.
UiPath offers an enterprise-grade platform available through the Google Cloud Marketplace that supports quick deployment of automation workflows. With tools like Agent Builder and integration to Google’s AI models, healthcare organizations can build, scale, and manage AI-powered automated solutions without extensive coding.
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