Artificial Intelligence (AI) is quickly becoming important in healthcare in the United States. While many focus on AI in clinical diagnosis and treatment, non-clinical AI applications are changing healthcare operations behind the scenes. These tools automate office tasks, improve workflow, and help patient care. For medical practice administrators, practice owners, and IT managers, knowing about these changes is important to run healthcare organizations well.
In healthcare, many everyday office tasks take a lot of staff time. This can cause delays and slow services. Non-clinical AI helps by automating jobs like appointment scheduling, patient registration, claims processing, and medical paperwork. These AI systems reduce mistakes, cut costs, and speed up tasks so health professionals can spend more time with patients.
The American Health Information Management Association (AHIMA) held a Virtual AI Summit on June 6, 2025. It focused on non-clinical AI tools that affect healthcare administration. Experts talked about how AI is changing healthcare work and the need for training staff to use AI well.
Kelly Canter, a speaker at the AHIMA summit, said AI automates routine office tasks. This leads to better efficiency, lower costs, and helps with patient care. For example, AI virtual agents can answer many front-office phone calls, check patient info, confirm appointments, and answer common questions. This reduces work for front desk staff and cuts wait times, making the patient experience better from the start.
Megan Pruente talked about AI using large language models (LLMs) to help with paperwork, making policies, and supporting decisions. These tools help staff by drafting documents, keeping data accurate, and following rules. Clear records reduce claim rejections and help billing run smoothly.
One important AI use in healthcare is workflow automation. AI systems can take over repeated, time-consuming tasks to make work easier.
For example, Simbo AI uses AI-driven virtual agents for phone calls. These agents schedule appointments, answer questions, and update records without needing a person. This helps call wait times and lets staff focus on harder tasks.
AI also improves claims processing and managing money flow. AI systems check medical billing, find errors, and send claims faster than people. Kelly Canter showed how AI reduces denied claims and speeds up payments, which helps medical offices financially.
Health information workers manage AI tools that make notes automatically. Roberta Baranda said these workers make sure AI-made documents follow rules and are good quality. Even though AI writes notes, people still need to check to keep the records legal and correct.
Megan Pruente explained AI helps match patient records better and manages data well. This reduces mistakes with patient info, which is important for safe care.
Using AI well in healthcare depends a lot on staff knowledge. At the AHIMA Virtual AI Summit, experts like David Marc said training and understanding AI is very important for health information workers. As AI grows, workers must learn how AI works, its benefits, limits, and rules.
Training plans focus on critical thinking, ethical AI use, and how to work with AI. This helps people work well with AI, making sure technology supports human skills instead of replacing them. Medical administrators and IT managers should invest in staff education to get the most from AI and follow rules.
Healthcare groups in the U.S. must balance new technology with strict rules. AI tools handling patient data must follow HIPAA and other privacy laws. Ammon Fillmore, a healthcare advisor, talked about ethical AI use at the AHIMA summit. These ideas help health providers handle data privacy, security, and risks as laws catch up with AI.
Using AI responsibly protects healthcare groups from legal trouble and keeps patient trust. It also needs clear rules about how data is collected, stored, and used. As AI tools grow, concerns about bias, fairness, and accountability increase. Healthcare leaders must create policies that deal with these issues early.
Most AI talk is about clinical uses, but non-clinical AI also helps patients and lowers costs. Automating office tasks cuts delays and mistakes, letting patients get services faster. This helps by reducing wait times, lowering lost or wrong information, and letting staff give more personal care.
AI also cuts costs by needing fewer office staff and lowering billing errors and claim denials. A 2025 AMA survey showed 66% of U.S. doctors use AI tools, up from 38% in 2023. Also, 68% think AI improves patient care. This shows health providers trust AI to help both clinical and office work.
AI tools do more than simple automation. Natural Language Processing (NLP) helps understand unstructured clinical data, improving notes and health analysis. Microsoft’s Dragon Copilot and other software help lighten paperwork for clinicians, so they can focus on patients.
But adding AI into current health systems is hard. Many AI products need changes or extra software to work with Electronic Health Records (EHR) systems. It can be tricky to link AI with older systems and keep workflows smooth. Training and clinician acceptance also affect how AI is used. IT managers and administrators must plan carefully to fit AI into their work without upsetting it.
Healthcare in the U.S. has special office and rule challenges. The complicated insurance and payment systems make non-clinical AI very useful. Automating patient intake, insurance checks, and claim decisions cuts errors and office work costs. Also, U.S. groups must follow many state laws about patient data, making compliance more complex.
Practice owners and administrators can use AI tools like Simbo AI’s phone automation to improve patient access and office response in a competitive field. At the same time, IT managers must keep strong cybersecurity to protect sensitive data while supporting AI.
With federal agencies like the FDA updating rules on digital health and AI, healthcare groups need to keep up with changes that affect technology and compliance. Those who make clear rules for AI use can better manage risks and keep stable operations.
Healthcare groups have shown success using AI in examples shared by Rachel Podczervinski. These cases include using AI for coding, better documentation, and reducing denied claims. Organizations report better efficiency and patient results by combining AI tools with human checks.
These examples show AI is not a one-piece fix. It must work together with technology, staff skills, and policies. Administrators, owners, and IT managers can learn from early AI use to plan for steady growth in their practices.
Non-clinical AI offers many chances to improve healthcare operations in the United States. Automated office workflows simplify tasks, cut mistakes, improve money management, and help patient access. Staff training and AI knowledge are needed to make AI work well and follow rules. Healthcare leaders should think about using AI tools like Simbo AI’s phone automation to boost efficiency. Balancing technology with ethical rules and staff involvement helps healthcare groups lower costs and improve patient care quality.
The AHIMA Virtual AI Summit focuses on non-clinical AI applications that are transforming healthcare operations, offering insights into AI workforce development, implementation strategies, and compliance with healthcare laws.
The summit targets health information professionals who are either starting their AI journey or looking to enhance their existing AI implementations.
The sessions cover AI upskilling, workforce training, ambient documentation, digital teammates, AI governance, and real-world use cases of AI in healthcare.
AI enhances healthcare operations by automating routine administrative tasks, leading to improved efficiency, reduced costs, and enhanced patient care.
Health information professionals play a crucial role in ensuring AI systems are effectively integrated, maintaining documentation quality, and supporting compliant reimbursement practices.
Organizations can prepare for evolving AI regulations by mastering responsible AI implementation and establishing frameworks for ethical use and risk management.
Essential skills include AI literacy, data governance, understanding of regulatory frameworks, and practical training for effective collaboration with AI technologies.
Examples of practical AI tools include large language models (LLMs) for documentation, ambient documentation technologies, and systems that automate data review and decision support.
Compliance strategies protect organizations from legal penalties, ensure ethical AI use, and help leverage AI’s operational benefits while navigating the regulatory landscape.
Key presenters include experts in health informatics, legal issues in healthcare technology, AI application, data integrity, and health information management, bringing a wealth of knowledge on AI’s implementation in healthcare.
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