Artificial Intelligence (AI) is becoming more common in healthcare in the United States. Hospitals and health systems are using AI tools to work better, cut costs, and improve patient care. One new AI technology is generative AI. It can do many tasks automatically, like helping with money management and front-office jobs. But using generative AI in healthcare has some big problems, especially with making sure the AI is fair and that the data is correct. This article looks at these problems and shows how healthcare leaders, practice owners, and IT managers in the U.S. can handle these challenges to improve patient care and office work.
AI use is growing in American healthcare groups. A 2023 survey by the Healthcare Financial Management Association (HFMA) and AKASA showed that almost 46% of hospitals and health systems use AI to manage revenue cycles. Even more, 74%, use some type of automation for revenue tasks, which includes AI and robotic automation.
Healthcare call centers have gotten better by 15% to 30% by using generative AI for common questions and administrative work. For example, Auburn Community Hospital in New York cut cases that were discharged but not billed by 50%. They also raised coder productivity by over 40% by using natural language processing (NLP), machine learning, and robotic automation. Banner Health uses AI bots to check insurance coverage and write appeal letters. A community health network in Fresno, California, cut prior-authorization denials by 22% using AI tools to review claims.
These examples show how AI can help lower paperwork, make billing more accurate, and help staff work better. This is important because many healthcare groups face money and staff problems. But adding generative AI comes with problems, especially bias in results and wrong or unchecked data.
Bias in AI is a big problem for healthcare providers and managers in the U.S. Bias happens if AI results unfairly treat certain groups differently based on race, ethnicity, gender, income, or other factors. This can cause unfair patient care, payment, and office decisions.
Healthcare data often shows inequalities found in society. AI trained on this data may learn and repeat these unfair parts. For example, AI that reads medical images or guesses patient outcomes might be less correct for groups not well represented in the training data.
David B. Olawade and his team said there needs to be strong ethical and legal rules to fight bias in healthcare AI. They advise using different, good-quality data and always checking to find and lower bias. Data experts, doctors, and healthcare managers must work together to keep results fair.
Generative AI is more complex. It makes human-like text and answers, but it can also create biased or wrong info if it’s not watched closely. Healthcare groups must use tools like clear algorithms, regular checks, and bias detection to stop bad AI results. Without this, biased AI could make healthcare inequalities worse, not better.
Data validation matters a lot when using generative AI in healthcare. AI depends on good and correct input data. Mistakes, wrong info, or old data can cause wrong or harmful results, especially in clinical coding, medical billing, or checking patient eligibility.
For example, managing revenue cycles needs correct clinical documents and coding for proper billing and payment. AI using natural language processing (NLP) can assign billing codes automatically from medical records, saving time and cutting human mistakes. But this needs clear data formats and exact documents. If data is missing or wrong, coding errors will rise, hurting money and rule-following.
Hospitals like Auburn Community Hospital said AI help raised coder work by more than 40%, but this works only if data inputs are checked well and output quality is watched all the time. The Fresno community health network used AI for claims review and cut denials by 18%, but this worked because patient data and payer rules were accurate.
Generative AI also needs output checking before using the results in patient care or office tasks. These models use many data sources, but making wrong or unsuitable content is a real risk. It is very important that AI results follow clinical rules, billing rules, and laws to avoid mistakes.
Healthcare groups must have processes like:
Without strong data validation, using AI can cause losses, legal trouble, or safety risks.
Besides bias and data checks, adding generative AI to healthcare workflows brings other chances and problems. AI can take over simple office tasks. This lets staff work on harder, more important activities.
Tasks like checking insurance, setting appointments, handling prior authorizations, and answering patient questions are now done by AI-driven front-office tools. Companies like Simbo AI work in phone automation and AI answering to make office communication faster and more accurate.
Automation tools also help manage revenue cycles by doing repetitive tasks like sending claims, managing denials, and setting payment plans—all powered by AI data analysis. Banner Health uses AI bots to write appeal letters for denials, saving staff 30 to 35 hours per week sometimes.
To succeed with AI in workflows, healthcare managers must ensure that:
AI workflow automation can cut wait times, lower paperwork, and make patients and staff more satisfied. These are important for running busy healthcare places well.
Healthcare organizations in the U.S. must follow complex rules when using AI. Federal agencies like the Food and Drug Administration (FDA) and the Office of the National Coordinator for Health Information Technology (ONC) require AI tools to meet safety and privacy rules.
Ethical concerns include the need for transparency and responsibility. Healthcare managers must make sure AI decisions can be explained and that patient rights are safe. Human oversight is still important when AI guides clinical or billing decisions.
Data security is also important. AI systems must protect patient information following HIPAA laws. AI can help find fraud and unauthorized access, but this depends on strong algorithms and data controls.
Using generative AI in U.S. healthcare can help improve work efficiency, lower paperwork, and improve patient experience. But healthcare providers must focus on reducing bias and checking data quality while using these tools. AI should work with diverse, accurate data and always have human review and ethical checks.
By dealing with these problems early, healthcare groups can use AI’s benefits without risking patient harm or money problems. IT leaders, clinical staff, and managers must work together to set AI rules and make sure AI helps healthcare work better.
Approximately 46% of hospitals and health systems currently use AI in their revenue-cycle management operations.
AI helps streamline tasks in revenue-cycle management, reducing administrative burdens and expenses while enhancing efficiency and productivity.
Generative AI can analyze extensive documentation to identify missing information or potential mistakes, optimizing processes like coding.
AI-driven natural language processing systems automatically assign billing codes from clinical documentation, reducing manual effort and errors.
AI predicts likely denials and their causes, allowing healthcare organizations to resolve issues proactively before they become problematic.
Call centers in healthcare have reported a productivity increase of 15% to 30% through the implementation of generative AI.
Yes, AI can create personalized payment plans based on individual patients’ financial situations, optimizing their payment processes.
AI enhances data security by detecting and preventing fraudulent activities, ensuring compliance with coding standards and guidelines.
Auburn Community Hospital reported a 50% reduction in discharged-not-final-billed cases and over a 40% increase in coder productivity after implementing AI.
Generative AI faces challenges like bias mitigation, validation of outputs, and the need for guardrails in data structuring to prevent inequitable impacts on different populations.
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.
