Healthcare has special challenges for automation because medical tasks can be complex and often need people to be involved. Researchers Michael Chui, James Manyika, and Mehdi Miremadi studied more than 2,000 work activities in 800 jobs to find out which healthcare tasks can be automated. They found that about 36% of healthcare work activities in the U.S. could be done by machines with today’s technology. But this number changes depending on the kind of task.
Tasks that need direct patient care, medical decisions, empathy, and careful communication have low chances for automation, usually less than 30%. For example, fewer than 30% of a nurse’s daily tasks can be done by AI because nurses must interact face-to-face, make decisions, and support patients—things AI cannot do yet. Dental hygienists have even fewer tasks suitable for automation, about 13%, showing how hard it is to automate hands-on medical work.
On the other hand, tasks like gathering patient health information, preparing meals, and managing appointments have higher chances for automation. These jobs are more routine and easier for AI systems to handle.
One big advantage of AI in healthcare is its ability to quickly and accurately analyze large amounts of clinical and administrative data. Two important AI tools are machine learning and natural language processing (NLP).
Machine learning finds patterns in patient records. It helps doctors make diagnoses, predict disease risks, and create treatment plans that fit each patient. This can help doctors find health problems sooner and give better care.
Natural language processing helps by pulling out important information from medical notes and documents. This makes diagnoses more accurate and helps with clinical paperwork, which often takes a lot of time.
In the U.S., AI is used to reduce manual work in medical billing and coding. AI checks if patients are eligible, finds billing errors before claims are sent, and automates insurance claims. This reduces claim denials and speeds up payments. It also helps healthcare organizations manage money better and lets staff focus on more important work.
Healthcare providers can use AI to automate front-office tasks like phone calls and patient communication. AI can handle routine questions, schedule appointments, do patient pre-screening, and send reminders. These jobs usually need human receptionists or call centers.
Simbo AI is an example of a company that offers front-office phone automation. Their AI answering service works 24/7, cuts down wait times for patients, and routes calls based on patient needs. For example, patients with urgent symptoms are quickly connected to the right clinical staff.
Healthcare administrators and IT managers find that using these AI tools reduces staffing problems and lowers mistakes in managing appointments. It also makes patients happier by giving instant responses and reducing unanswered calls. Since medical offices get thousands of calls each month, automating these tasks helps things run smoother.
Today, AI helps mostly with routine and administrative healthcare tasks. But in the future, improvements in AI will expand what can be automated. This is because natural language processing, machine learning, and generative AI are getting better.
Right now, automation in healthcare is limited because many tasks are unpredictable and need emotional intelligence and expert judgment. But researchers say that as AI gets better at reasoning and understanding complex situations, automation might grow a lot—going from about 25% now to maybe 67% for tasks in unpredictable settings.
Improved technologies include:
A 2025 survey by the American Medical Association shows that 66% of U.S. doctors already use AI tools, up from 38% in 2023. This shows growing trust and acceptance of AI in healthcare.
Healthcare managers who run medical facilities can use AI-powered workflow automation to change how their offices work. Workflow automation means letting AI do repeated, manual, or time-consuming jobs, both in clinical and administrative areas.
Common examples where AI helps in U.S. healthcare include:
Using these AI tools makes healthcare offices run more smoothly. There are fewer delays, greater accuracy, and better use of staff time. This is important because many healthcare workers face burnout and staff shortages.
Even with AI improvements, it is not meant to replace healthcare workers but to help them. Patient care needs judgment, empathy, and ethics, which machines cannot do fully, especially soon.
Healthcare leaders must keep in mind several things when adding AI automation:
Groups like IBM, Google DeepMind Health, and Imperial College London keep working to improve AI in diagnostics and therapy. This shows that in the long run, AI and healthcare workers will work together, not replace each other.
The United States has a large and complex healthcare system. AI-driven automation can help many parts of it. From big hospitals to small clinics, AI can reduce delays in patient intake, billing, and provider workflows. This helps make operations more efficient.
Market studies show the AI healthcare market in the U.S. was worth $11 billion in 2021. It is expected to grow to nearly $187 billion by 2030. This increase comes from better AI tools, more trust from doctors, and higher demand for solutions that improve care and control costs.
For healthcare managers and owners, knowing how to pick, set up, and run AI systems is very important. It means choosing systems that work with existing electronic health records, follow regulations, and improve patient care without losing the human part of healthcare.
AI is increasing what can be automated in U.S. healthcare. Right now, about one-third of healthcare activities can be automated because clinical tasks are complex. But ongoing technology improvements could raise this number a lot. Front-office tasks like answering phones and scheduling, as well as billing and documentation, are already helped by AI.
Using AI more will need a balance between practical benefits and following rules, social acceptance, and smooth operation. Healthcare leaders who understand these changes and invest in good AI tools will be better able to handle the challenges of modern healthcare. This will help both patients and healthcare workers.
The technical potential for automation in healthcare is about 36%. However, for health professionals engaging directly with patients, it drops significantly, as only around 30% of a nurse’s activities can be automated.
Activities such as preparing food in hospitals and collecting health information have higher automation potential. More complex tasks, like administering anesthesia or reading radiological scans, also show some feasibility but are limited.
Healthcare requires expertise, direct patient contact, and emotional intelligence. Many tasks involve nuanced human interactions, which current AI technologies cannot replicate effectively.
Automation potential varies by occupation. For instance, while some data collection tasks can be automated, the empathetic reasoning and decision-making required in roles like nursing are much less automatable.
Sectors like manufacturing and food service show high automation potential due to the prevalence of predictable physical activities, with feasibilities as high as 73% in food service.
Factors include technical feasibility, cost of development and deployment, workforce supply and demand, benefits beyond labor substitution, and social acceptance within particular sectors.
Nurses can automate less than 30% of their daily activities. Tasks requiring personal interaction, clinical judgment, and empathy remain largely human-run.
As AI technology matures, particularly in natural language processing, the potential for healthcare automation could increase, allowing machines to assist in more complex tasks.
Social acceptance plays a critical role in healthcare. Patients often expect human contact, which affects how and where automation can be successfully implemented in medical settings.
Automation could elevate productivity and efficiency, necessitating shifts in work culture and organizational structures. Leaders must prepare to integrate technology while addressing the human element in the workplace.
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