AI agents are computer programs that can work on their own to do tasks that need handling large amounts of data, making choices, or talking to people. In healthcare, especially in hospitals and research centers, AI agents manage many difficult workflows that used to need a lot of human effort.
A big problem in U.S. healthcare is too much paperwork, manual data entry, scheduling, insurance approvals, and following rules. These tasks take almost half of the clinicians’ time. This lowers how well they can work and care for patients. AI agents help by automating many of these regular jobs. This frees doctors and staff to focus on more important tasks like treating patients and making decisions.
Places like Geisinger Health System show how AI can work by using over 110 automations that mostly handle prior authorizations. These automations save hundreds of clinical hours by checking if medical care is necessary, speeding up approvals, and cutting down denials. This lowers staff workload by 20 to 30 percent and helps meet complicated payment rules like those for the Prospective Payment System and Critical Access Hospitals.
Clinical trials help create new drugs and treatments, but they can be slowed by complicated tasks like finding patients, managing data, submitting paperwork, and communication. AI agents now make these tasks easier and faster. This helps get patients into trials quicker, follow rules better, and handle large amounts of clinical data more efficiently.
For example, IQVIA, a top clinical research group, uses AI models powered by NVIDIA’s AI Foundry. They trained AI on over 64 petabytes of healthcare data. This helps them find the right patients and manage regulations faster. This reduces the time it takes for new drugs to reach the market and improves data quality.
The Mayo Clinic also uses AI for diagnostics and data analysis. They use NVIDIA’s DGX Blackwell systems to study millions of pathology slide images linked to patient records. This makes diagnostic work smoother and supports trial data accuracy needed for official approval.
AI agents that can think and adapt, called agentic AI, help manage very complex and large data sets like genomics and patient outcomes. This kind of AI works with little human help and helps clinical research move forward.
AI agents help cut down the amount of paperwork and tasks medical staff do every day. Tasks like clinical notes, billing, scheduling, and insurance approvals get easier with AI tools.
For example, AI assistants like Doctoralia España’s Noa and Microsoft’s Dragon Copilot can write and organize clinical notes while doctors see patients. These tools save doctors 5 to 10 minutes per patient. This means they can see more patients each day, from 13 to 20, without getting more tired. This lowers the backlog of notes and improves the experience for both doctors and patients.
Dragon Copilot also helps reduce burnout among doctors. Surveys show about 70% of users feel less tired doing admin work. Patients say they have a better visit experience, with 93% saying they feel more involved when their doctor uses AI to take notes.
AI also helps nurses. Because there may be a shortage of 4.5 million nurses in the U.S. by 2030, AI can lessen their paperwork so they can spend more time helping patients.
Prior authorization is a slow step in healthcare. It needs checking with insurance before hospital visits, procedures, or treatments get approved. Manual ways cause delays, claim denials, and higher admin costs.
AI systems fix this by automating prior authorization work. For example, AI at Geisinger Health can guess which claims will be denied, check medical necessity against insurance rules, and prepare documents automatically. This lowers admin work by 20 to 30 percent and lets staff focus on harder patient cases.
AI’s real-time checking also helps hospitals keep their income correct. It better matches documentation and coding to make sure hospitals get paid right under Medicare and Medicaid.
AI tools fit smoothly with electronic health records and billing systems. Using standards like FHIR, AI keeps workflows running well and follows laws like HIPAA to protect patient privacy.
Workflow automation with AI agents helps speed up clinical trials and admin tasks in healthcare. It works best on simple, repetitive tasks or complex rules that AI can do as well or better than people.
Common healthcare jobs helped by AI automation are:
In the U.S., AI workflow automation lowers costs. Studies say AI could save $150 billion every year by 2026 by making work more efficient. Big healthcare centers like Cleveland Clinic, Duke Health, and Stanford Health Care use these systems. For example, the University of Rochester Medical Center increased ultrasound billing by 116% with AI imaging. OSF Healthcare saved $1.2 million on call center costs by using AI assistants to help patients.
AI made for clinical research speeds up patient recruitment by quickly checking who fits the trial rules. This finds patients faster and more accurately than manual work.
AI also helps with regulatory paperwork by making data that meets government rules. This lowers mistakes and speeds approvals. AI watches patient safety and trial progress in real time to keep everything following rules. This lowers audit risks.
New “agentic AI” systems do more than just automation. They use reasoning to handle unclear data and keep improving solutions. This helps create personal treatment plans and flexible trial steps. These systems can grow trial work with little human help, making it faster and more accurate.
The Mayo Clinic uses AI for digital pathology. It studies millions of slide images linked to patient files to build models supporting diagnosis and trial results. The Arc Institute uses AI to join gene and protein data to help develop drugs and design early clinical trials.
Using AI agents in healthcare requires careful handling of privacy, security, and ethics. U.S. healthcare groups must follow HIPAA rules and protect patient consent and data.
Good management and teamwork between tech experts, doctors, admins, and legal staff help make sure AI runs fairly and openly. Responsible AI use prevents bias in data or algorithms that might affect patients.
It is also important to keep humans involved where AI helps but doesn’t replace doctors and staff. This keeps clinical judgment strong while lowering routine work and mistakes.
By automating clinical trials, prior authorization, documentation, and many other tasks, AI agents help healthcare run more smoothly. They reduce clinician burnout, improve patient experience, and support better finances for U.S. healthcare providers. Medical practice managers, owners, and IT teams are using these tools to meet growing needs for efficiency and quality in healthcare.
NVIDIA’s partnerships with IQVIA, Illumina, Mayo Clinic, and Arc Institute focus on accelerating biomedical AI in genomics, drug discovery, and clinical diagnostics, highlighting a shift towards AI agents that autonomously streamline workflows and reduce administrative burdens in healthcare.
AI agents are digital workforce systems that reason about missions by breaking tasks down, retrieving data, or using tools to generate quality responses, working autonomously alongside human employees to enhance efficiency.
IQVIA uses NVIDIA AI Foundry services to develop domain-specific AI foundation models and agents trained on vast healthcare datasets, aiming to streamline clinical trial processes such as patient recruitment and regulatory submissions.
Illumina integrates GPU-accelerated computing into its DRAGEN sequencing software to efficiently manage expanding multiomics datasets, accelerating analysis in genomics, transcriptomics, and proteomics for precision medicine.
Mayo Clinic implements NVIDIA DGX Blackwell systems for AI-driven digital pathology, leveraging large-scale correlated slide images and patient records to develop foundation models that enhance pathological analysis accuracy.
Arc Institute develops large-scale biological AI models that integrate DNA, RNA, and protein data using NVIDIA’s BioNeMo and DGX Cloud infrastructure to advance synthetic biology and drug discovery research.
NVIDIA’s foundational architecture includes NIMs—pre-optimized AI microservices, Nemo—a generative AI training framework, and Blueprints—reference implementations for healthcare workflows facilitating rapid, optimized deployment.
AI agents reduce administrative workload by automating complex tasks such as patient recruitment and regulatory compliance, improving operational efficiency, and potentially shortening the drug development timeline.
AI agents are expected to scale inferencing computations massively, enabling multiple AI models to work simultaneously behind the scenes, fundamentally transforming clinical workflows and research through increased automation and intelligence.
AI agents enable faster, scalable multiomics data analysis crucial to precision medicine, facilitating timely, personalized treatment decisions by efficiently interpreting complex biological data sets in genomics and proteomics.
Simbo is using AI agents to automate all phone related workflows. Connect now to know more.
Schedule AI Agents Demo Now© Since 2019, Simbo AI.
