AI in healthcare is no longer just about helping doctors with diagnoses or managing electronic health records (EHRs). Recently, “agentic AI” has gained attention. Agentic AI means AI that can do complex tasks mostly on its own. According to the Global Enterprise AI Survey 2025, about 27% of healthcare organizations in the United States are already using agentic AI, and another 39% plan to start using it within a year. This fast growth shows that many believe AI can help reduce staff burnout, lower patient wait times, and deal with staff shortages common in U.S. healthcare.
Agentic AI works by automating full processes that usually need human help. For example, tasks like patient scheduling, claims processing, pharmacy work, and some parts of clinical decision-making are now done more by AI systems. This allows healthcare workers to focus on more important jobs like direct patient care and complex decisions.
One key lesson from studies and real cases is that technology alone cannot make AI successful in healthcare. A complete approach mixes automation tools, human involvement, and system-wide workflows. In fact, 91% of healthcare organizations say that connecting automation with business processes, people, and systems is needed for AI to work well.
This connection helps people work well with AI tools. For example, Jesse Tutt, Program Director at Alberta Health Services (AHS), shared that adopting an AI-first culture saved over 238 years of work time and also improved patient experiences. This shows AI is meant to help healthcare staff, not replace them. It makes work easier and improves job satisfaction.
Healthcare groups realize that challenges with AI often come from managing human factors. About 31% say success depends more on people and workflows than on just technology. This means training, clear AI rules, and managing changes with employee involvement and career support are important.
In the United States, concerns about patient data security, privacy, and bias in AI remain major. A survey shows 57% of healthcare leaders worry about protecting patient information when using AI. At the same time, 49% worry about bias in AI medical advice. These concerns have slowed unchecked AI growth but have also led to stronger governance rules.
To build trust, healthcare organizations focus on transparency, audit checks, and strong data rules. They work to make sure AI is trained on diverse data and is always tested for fairness. Over the next two years, 44% of healthcare groups expect AI to help improve cybersecurity, while 56% expect AI to improve healthcare data quality, reducing errors and misuse.
AI has made progress in patient scheduling and waitlist management. Now, 55% of healthcare providers either fully use AI in scheduling or are close to it. AI platforms let patients book their own appointments, get reminders, and manage schedule changes immediately. This reduces no-shows and last-minute cancellations, helping clinics run smoothly.
In pharmacy services, AI use is at 47%. Automation helps with calculating medicine doses, finding errors, and making sure medication is delivered on time, which makes medicine use safer. Patients can also report symptom changes by AI communication tools, helping pharmacists and doctors react faster.
Cancer services also use AI more now. About 37% of healthcare groups use AI tools or are getting ready to use them. AI speeds up reading medical images, helps diagnose, and supports personalized treatment plans. This helps doctors make faster and better decisions.
Healthcare leaders in the U.S. are planning more AI use in diagnostics (42% planning), remote patient monitoring (33%), and clinical decision support (32%) in the next two years. This shows a move toward care that is proactive and focused on patients.
AI-powered diagnostics look at patient history and test results to find diseases early, sometimes before symptoms appear. Remote monitoring tools collect constant data from wearables or home devices, sending alerts if health changes. Clinical decision support systems use AI in healthcare workflows to give evidence-based advice during treatment and help lower differences in care.
A key part of using AI well is blending AI and automation into current workflows. Here’s how AI-driven automation can change how healthcare runs in the U.S.
Robotic Process Automation (RPA) is an example of technology that works with AI in healthcare workflows. RPA uses software robots to do repeated, rule-based tasks like data entry, appointment scheduling, claims processing, and linking systems. These robots copy how humans interact with digital systems and work nonstop; this lowers errors and cost.
RPA has developed in three stages:
In healthcare, RPA helps connect old systems with new platforms and keeps data moving smoothly without costly programming. It also helps healthcare groups follow laws by keeping detailed logs of automated work, which is important for rules like HIPAA in the U.S.
Together, RPA and AI reduce admin work a lot. They help cut wait times for appointments, improve patient communication, handle billing better, and support medicine management. By automating these tasks, healthcare workers can spend more time with patients.
Even with new technology, healthcare workers stay at the center of AI plans. Studies show 37% think AI will help their work-life balance. Another 33% say AI will help them do tasks better, and the same number believe AI will bring new career chances.
AI systems help workers instead of replacing them. For example, automated scheduling lets admin staff skip routine calls and appointment tasks so they can do other important work. Clinical AI tools help doctors and nurses by giving data and decision help, lowering mental stress and preventing burnout.
The Institute for Healthcare Improvement’s Care Operating System (IHI CareOS) shows how mixing technology and a human focus can improve clinical workflows. This system links operations, clinical care, analytics, and communication to help ongoing improvement and make care more consistent. Services like safety management, patient flow checks, and feedback collection have led to better patient results and happier staff.
A key lesson from healthcare leaders is that AI works best when part of the larger healthcare system, not by itself. For example, Alberta Health Services credits its AI-first method—using platforms like SS&C Blue Prism—for saving time and improving patient care.
Coordinating automated systems makes sure AI works well with EHRs, pharmacy software, diagnostic tools, and communication platforms. This stops technology silos and makes sure data from various sources is available when needed.
Improving Electronic Health Records (EHR) is still a main focus of AI integration. Consulting that boosts EHR ease and working together helps cut admin tasks for clinicians and better coordinate patient care. Because the U.S. has many different healthcare IT systems, this integration is needed to get the best results from AI.
Healthcare groups in the U.S. are close to big changes thanks to AI adoption. But improving continuously needs more than just new technology. It needs combining AI tools with current workflows, involving healthcare workers, and linking different clinical and operational systems. Using this full approach helps medical practice leaders put AI solutions in place that improve patient care, make staff work better, and run operations smoothly. As more groups use agentic AI and RPA with strong rules and a focus on people, healthcare delivery will be more efficient, secure, and patient-focused.
27% of healthcare organizations report using agentic AI for automation, with an additional 39% planning to adopt it within the next year, indicating rapid adoption in the healthcare sector.
Agentic AI refers to autonomous AI agents that perform complex tasks independently. In healthcare, it aims to reduce burnout and patient wait times by handling routine work and addressing staffing shortages, although currently still requiring some human oversight.
Vertical AI agents are specialized AI systems designed for specific industries or tasks. In healthcare, they use process-specific data to deliver precise and targeted automations tailored to medical workflows.
Key concerns include patient data privacy (57%) and potential biases in medical advice (49%). Governance focuses on ensuring security, transparency, auditability, and appropriate training of AI models to mitigate these risks.
Many believe AI adoption will improve work-life balance (37%), help staff do their jobs better (33%), and offer new career opportunities (33%), positioning AI as a supportive tool rather than a replacement for healthcare workers.
Currently, AI is embedded in patient scheduling (55%), pharmacy (47%), and cancer services (37%). Within two years, it is expected to expand to diagnostics (42%), remote monitoring (33%), and clinical decision support (32%).
AI automates scheduling by providing real-time self-service booking, personalized reminders, and allowing patients to access and update medical records, thus reducing no-shows and administrative burden.
AI supports medication management through dosage calculations, error checking, timely medication delivery, and enabling patients to report symptom changes, enhancing medication safety and efficiency.
AI reduces wait times, assists in diagnosis through machine learning, and offers treatment recommendations, helping clinicians make faster and more accurate decisions for personalized patient care.
91% of healthcare organizations recognize that successful AI implementation requires holistic planning, integrating automation tools to connect processes, people, and systems with centralized management for continuous improvement.
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