Generative AI conversational agents are AI systems that can understand and reply like a human using everyday language. They are different from simple chatbots that only use set answers. These agents use natural language processing, understanding, and machine learning to have more meaningful talks with patients. They do more than just check symptoms. They connect with Electronic Health Records (EHRs) and medical databases to handle tasks like patient check-in, scheduling appointments, symptom checks, and even writing clinical notes.
A typical AI agent talks to patients before their visit by asking detailed health questions, collecting data in an organized way, scheduling tests, and summarizing health information. This helps both patients and doctors get ready, making appointments more useful and cutting down on long face-to-face questions during visits.
Pre-visit engagement is an important step that affects how good and smooth clinical visits are. AI conversational agents in EHR systems let patients share health details anytime and from anywhere, using voice or text on many types of devices. This flexibility helps patients answer questions carefully, talk with family, or check old health records. This leads to more accurate and complete medical histories.
For example, SOAP Health, a company that makes AI for clinical notes, says its voice AI collects patient data automatically for 85-99% of clinical encounters. This makes visits shorter and records more accurate. The AI creates pre-visit SOAP notes (Subjective, Objective, Assessment, Plan) that doctors can check and change. This saves about 12 minutes per patient and up to four hours a day for documentation.
Another example is DeepCura AI, found in athenahealth’s Marketplace. It works like a virtual nurse, talking naturally with patients to gather data and automating consent and intake tasks. This technology lowers the work before the patient comes, making visits easier and faster.
Using AI before visits also helps find patients who might be at risk. RiskVue™, used with SOAP Health’s AI, looks at family history, lifestyle, mental health, social factors, and preventive care. It finds risks for serious illnesses like cancer and heart disease in about two out of three patients. This early warning helps doctors make care plans before visits, which can improve health results.
One big problem for U.S. medical practices is clinician burnout, partly because of lots of paperwork. Doctors spend many hours after work filling out patient charts and forms. This leads to stress and less free time. Adding AI conversational agents to EHRs helps reduce this burden.
AI tools like Epic’s MyChart AI write about one million patient message replies monthly across 150 health groups. This cuts down the time nurses and doctors spend on regular messages. For example, Mayo Clinic nurses save about 30 seconds per message, freeing them to do other important work.
AI can also make full clinical summaries, like hospital rounding notes, discharge papers, and visit notes suited to each type of care. These notes go directly into the EHR, making workflows smoother and reducing mistakes. Doctors say this AI help lowers mental load and after-hours work. One primary care doctor using Epic’s AI said it “saved my marriage” because less charting was needed at home, improving work-life balance.
Also, the SOAP Health AI assistant in athenaOne Marketplace cuts documentation time by up to 66%. Doctors say they have gotten up to 20% more in Evaluation and Management (E&M) payments, and health systems have seen their income double. This is because documentation is better and patient data is collected more fully before visits.
Besides helping with patient engagement and documentation, AI agents in EHRs also help automate workflows in medical offices. This is important for administrators and IT managers who want to improve efficiency without adding staff or costs.
One example is using AI for front-office phone tasks. Companies like Simbo AI focus on AI phone systems. Their systems manage appointments, patient sorting, common questions, prescription refills, and registration using voice AI. This lowers phone wait times and lets front desk workers focus on harder tasks.
For providers using Oracle Health’s AI, front-office work gets better with smart patient registration and self-scheduling tools that use optical character recognition (OCR) and document reading. These tools pull patient info from different forms quickly and with fewer errors.
On the clinical side, AI systems in athenahealth’s Marketplace can make decisions on their own. They change what they do based on patient answers live, triggering forms, consent, and reminders without human help. This keeps pre-visit engagement smooth and gives the clinical team correct patient data ready to go.
AI also works after visits by following up with patients, sending medicine reminders, and helping patients stick to care plans. This continuous contact keeps patients involved even after leaving the clinic, supporting ongoing care models.
Healthcare providers in the U.S. face pressure to improve patient satisfaction, follow rules, and control costs. Using generative AI conversational agents in EHRs helps with many of these issues.
Even though AI offers clear benefits, bringing in generative AI conversational agents needs careful planning and ongoing checks. Data privacy is very important, with strict HIPAA and GDPR rules to keep patient info safe.
It is also key that the AI stays accurate. The AI must be checked regularly against new medical knowledge to avoid wrong information. That is why AI tools support doctors but do not replace their judgment. They also have clear steps to ask doctors for help in difficult cases.
Adding AI to workflows needs attention to compatibility. Practices should pick AI solutions that have API-first designs and work well with their current EHRs to avoid problems.
Training staff and teaching patients about AI use is also important. This helps people accept the technology and build trust.
The future of AI conversational agents includes going beyond notes and engagement into areas like predicting health outcomes, managing clinical trials, and precision medicine. Big EHR companies like Epic are building AI that can handle voice, video, images, and genetic data to give better clinical information.
AI-assisted enterprise resource planning (ERP) that uses clinical and operational data together will help optimize staffing and supply chains. This will help busy U.S. practices predict what they need and work more efficiently.
Since 56% of healthcare leaders plan to spend on generative AI technologies in the next two to three years, the U.S. health system is ready for a big change driven by AI use.
By adding generative AI conversational agents into EHR systems, U.S. medical practices can improve how patients get ready before visits and make clinical work more efficient. This supports better patient care and more manageable operations. Administrators, practice owners, and IT leaders who learn about and use these technologies will be ready to handle the changing needs of healthcare in America.
Epic is embedding generative AI deeply into its EHR platform, developing AI-powered conversational agents and reusable components that understand chart information to automate tasks, improve documentation, and enhance both clinician and patient experiences.
Epic’s conversational AI agents engage patients by identifying visit goals, conducting pre-visit questionnaires, scheduling missing tests, and summarizing the data for both patients and physicians, making visits more productive and personalized.
Epic’s AI features generate various clinical summaries, such as visit histories and inpatient rounding notes, and assist in drafting documentation including hospital discharge notes, thus reducing clinicians’ administrative burdens and speeding charting workflows.
About two-thirds of providers using Epic have adopted generative AI features, with early adopters like Mayo Clinic reporting measurable time savings and reduced cognitive load for clinicians.
AI-driven documentation saves time on administrative tasks, reduces cognitive load, improves job satisfaction, helps with workforce retention, and alleviates burnout, with clinicians often reporting transformative effects on their work-life balance.
Epic partners with selected vendors such as Nuance, Abridge, Press Ganey, and others through its Workshop and Toolbox programs to rapidly develop and integrate ambient AI, voice recognition, and clinical documentation tools within its ecosystem.
Epic aims to implement native multimodal capabilities, including processing video input, voice synthesis, image recognition, and genomic data analysis, creating richer and more comprehensive documentation workflows.
Epic is expanding AI integration into clinical trials management, life sciences research, medical devices, specialty diagnostics, supply chain, payers, and enterprise resource planning (ERP) to unify operational, financial, and clinical data.
The ERP uses integrated EHR data to predict supply needs for surgeries, analyze staffing patterns including overtime, and forecast future staffing requirements, enabling better resource allocation and operational efficiency.
Epic’s Aura suite and Cosnome platform integrate genomic data with clinical records, providing clinicians with point-of-care insights for personalized treatment and allowing researchers to study genetic variants alongside real-world outcomes.
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