Agentic AI is very different from older chatbots or scripted AI systems used in healthcare. Older chatbots respond based on fixed scripts or keywords. Agentic AI uses large language models to understand complex language, follow instructions, and plan several steps during a conversation.
For example, old chatbots may struggle to reschedule many appointments across different departments. Their scripting limits what they can handle. On the other hand, agentic AI can do this in one conversation by accessing electronic health records (EHR), checking provider schedules, and organizing tests based on connections between them.
This new technology lets healthcare groups automate harder tasks like appointment scheduling, billing questions, prior authorizations, and eligibility checks. Staff no longer have to do repetitive or long work. According to Chris Ingersoll, a Healthcare Solutions Architect at SoundHound AI, agentic AI changes automation by improving patient workflows, making things better for patients and lowering costs.
Large language models, called LLMs, are the main technology behind agentic AI. These are advanced AI models that learn from huge amounts of data. They can understand natural language with good accuracy and understand context. Older chatbots used simple keyword detection. LLMs understand small details, follow complex orders, and give responses that feel more natural and less robotic.
LLMs help agentic AI plan what to do during a conversation instead of using fixed scripts. This means a person calling a clinic can talk in a normal way. Instead of going through many steps to reschedule an appointment, a caller can just say what they need once. Then, the AI does all the scheduling work automatically and right away.
IBM’s watsonx.ai is an example of how LLMs are used in healthcare AI. It helps with workflow automation, clinical decisions, and talking with patients. These models improve over time by learning and remembering, helping doctors serve patients better while cutting human errors and delays.
Agentic AI can do tasks quickly and process information in real time. In healthcare, speed is very important. Patients who want to change appointments, refill prescriptions, or check eligibility expect fast replies. Long waits can frustrate people, especially on front-office phone lines.
Agentic AI uses real-time data to give fast and correct answers. For example, during a phone call, it can verify a patient after one input, check many provider calendars, and reschedule appointments while considering tests and patient wishes—all in the same call.
This speed is important in US medical offices where staff get many calls and need to handle complex schedules. Companies like Simbo AI build AI systems that automate front-office phones. This helps handle calls faster, ease staff shortages, and improve patient happiness.
Agentic AI can manage whole workflows by itself. Once started, the AI plans, schedules, and watches each step without humans watching closely. It can connect with hospital systems like EHRs, billing, and referrals to coordinate many connected activities.
For example, a patient who needs several tests and then a doctor’s visit needs careful scheduling. Traditional systems find this hard. Agentic AI checks connections between appointments—making sure tests are done before doctor visits—and adjusts schedules as needed. Chris Ingersoll calls this a big change from small chatbot improvements to fully independent AI agents that change how patients get care and how clinics run.
From a cost view, this means fewer staff are needed in busy contact centers, lowering employee burnout. Healthcare providers save money and have staff who can spend more time on difficult patient care instead of routine tasks.
Agentic AI helps keep many workflows working together in healthcare places. Medical offices do many tasks that need good timing and communication between departments and systems.
Agentic AI uses hierarchical agent architectures. This means several AI agents work on different small tasks at the same time, while still keeping the whole workflow connected. This method helps healthcare groups use AI better. For example:
This teamwork improves accuracy, cuts delays, and stops mistakes or mixed-up info between systems. Agentic AI is better than old IVRs or chatbots, which need manual setup and often cause errors or upset patients.
Also, these AI agents have plans to send hard or unclear requests to human workers when the AI is unsure. This keeps care quality safe while still using automation benefits.
Agentic AI affects healthcare by matching the Quadruple Aim used in the US health system. This plan focuses on four goals: better patient experience, better work life for providers, lower costs, and better health for more people.
By automating tasks like phone scheduling, prescription renewals, billing, and referrals, agentic AI frees staff to do more valuable jobs with patients. Doctors and nurses get fewer interruptions from routine calls, raising job satisfaction and lowering burnout risk.
Patients get better service from AI-driven answering systems that give clear, fast, and mistake-free replies. They do not need to go through many calls with long waiting or repeat info. Instead, agentic AI offers more natural and faster help in fewer steps.
Agentic AI has many upsides, but US healthcare groups must protect patient privacy and system security. These systems need access to sensitive data like EHRs, scheduling, and billing. Strong cybersecurity is needed.
Agentic AI uses data encryption, secure access, and follows rules like HIPAA. Constant watching and risk checks are needed to keep patient data safe while AI works independently in healthcare.
Even with benefits, using agentic AI has challenges. Medical managers and IT staff must handle technical, organizational, and ethical issues:
Front-office phone automation shows clear benefits of agentic AI. Companies like Simbo AI offer AI answering services and phone systems that handle many patient calls daily. These systems focus on:
By automating front-office tasks, healthcare places reduce waiting times and let staff focus on clinical work that needs human judgment.
Google searches for AI agents have increased almost ten times in the last year. This shows more healthcare workers and managers know about agentic AI. Many US groups see its use for cutting costs and handling staff shortages.
SoundHound AI and IBM’s watsonx.ai are examples of companies leading with LLMs and AI that makes decisions on its own. These tools connect better with EHR and hospital systems, helping agentic AI spread.
Research in areas like rheumatology shows agentic AI helping with personal treatment plans, live data analysis, and clinical decisions. This means agentic AI will do more than just administrative work; it will also improve medical care.
For medical managers, owners, and IT staff in the US, learning about agentic AI’s technology is important when choosing new automation tools. Large language models, real-time processing, and autonomous workflow management show a clear move from old chatbots to systems that handle complex healthcare jobs on their own.
Using agentic AI needs careful planning, system setup, and staff training. But cutting admin work, saving money, better patient satisfaction, and improving provider work life make it a helpful tool in healthcare, especially for front-office phone work.
Organizations wanting to stay effective should watch agentic AI technology closely and think about using systems like Simbo AI to meet their needs now and later.
Healthcare AI agents autonomously perform tasks by dynamically planning workflows in real time using large language models, whereas traditional chatbots rely on predefined scripts, intent recognition, and static flows that do not adapt to complex or novel interactions.
Traditional chatbots use intent recognition powered by keyword matching or machine learning classifiers to route patients to predefined FAQ answers or automation scripts, which are static and deterministic, limiting their ability to manage complex or multi-step tasks and requiring significant manual design and training.
AI agents leverage large language models that understand language context, follow complex instructions, reason through multi-step processes, and plan optimal next steps dynamically, resulting in more natural, efficient, and personalized patient interactions without reliance on hard-coded flows.
Simple tasks like rescheduling an appointment can be completed in one natural conversation turn by AI agents, while complex tasks like coordinating multiple diagnostics with patient-specific constraints require agentic AI to evaluate interdependencies and schedule efficiently, which exceeds traditional chatbot scripting capabilities.
They require clear instructions and SOPs, access to operational tools like EHR systems for authentication, scheduling, and data retrieval, a comprehensive knowledge corpus including FAQs and protocols, and escalation logic to human agents when confidence is low.
Agentic AI targets reducing costs by automating administrative tasks, improving employee experience by alleviating repetitive work, and enhancing patient experience by streamlining interactions like scheduling and billing, complementing clinical AI’s focus on quality of care.
Because it moves from static, scripted automation to dynamic, context-aware decision-making capable of performing autonomous workflows, allowing personalized, real-time solutions instead of following rigid response trees or keyword routing.
Advancements in large language models with capabilities in natural language understanding, reasoning, and real-time processing empower AI agents to simulate human-like task execution and adapt to complex requests without predefined scripting.
By delivering frictionless, empathetic, and personalized conversational experiences that handle multi-step and nuanced requests efficiently, AI agents reduce wait times, misunderstandings, and frustration inherent in traditional IVRs or scripted chatbots.
Organizations may confront learning curves, system integration complexity, defining clear instructions and policies for autonomous agents, ensuring data security, managing escalation protocols, and initial resource investment, but the benefits in cost-saving and patient experience justify these efforts.
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.
