Artificial Intelligence (AI) is becoming important in healthcare. It helps with diagnostics, makes work easier, and improves patient care. But as AI helps with medical decisions, people worry if it is fair, accurate, and works well for all patients. This is very important in the United States because healthcare serves many different groups, including people with special medical and social needs.
Medical practice leaders, owners, and IT managers in the U.S. need to know why including all patient data and following good policies matter. This helps make sure AI tools do not cause or make health differences worse. This article explains how bias can happen in AI, why using data from all types of healthcare places is important, and what policies can help make AI in healthcare fairer.
AI and machine learning (ML) can study lots of data. They help with things like recognizing images, understanding language, and guessing health results. In medicine, AI is used in special areas like pathology and keeping health records. But these tools have problems, especially bias.
There are three main types of bias in healthcare AI:
Experts writing for the United States & Canadian Academy of Pathology say these biases should be checked at all steps—from building the model, training it, to using it in clinics. This helps keep AI fair and clear.
One way to lower bias in medical AI is to use data from many patient types. This includes safety-net providers. Safety-net providers are places like community clinics and hospitals that help many low-income or uninsured patients. These places are very important, especially for patients often missed in medical studies.
If AI only gets data from well-off patients, it will not work well for vulnerable groups. It could make health differences worse because the AI might not predict outcomes or treatments well for everyone.
In 2024, a focus group by the California Health Care Foundation (CHCF) included 45 safety-net leaders. They saw that AI could help but also found big challenges. These providers do not have the same access to big data networks or IT help that rich hospitals have. This stops them from using AI well and sharing their patient data. This may make racial and ethnic health differences larger.
Kara Carter from CHCF said safety-net groups must be part of data networks. This helps AI learn from different health records and be more accurate and fair for all groups.
Stella Tran from CHCF warned that if safety-net providers miss out on AI, the gap between Medicaid patients and those with private insurance could grow.
Even though using diverse data is important, safety-net providers have problems with AI:
Even so, some small community groups try their own AI tools made for their needs. Katie Heidorn from CHCF shared a story about a small health worker group designing custom AI. This shows promise but needs careful watching to avoid new biases and keep quality.
To fix these problems, policymakers and health leaders should work together to make AI cheaper, easier to get, and fair for all healthcare places:
One clear use of AI is to automate work tasks, especially in the front office and clinical notes. More health systems use AI tools like ambient medical scribing.
Ambient Medical Scribing means AI listens to doctor and patient talks and writes patient notes automatically for electronic health records (EHRs). This can:
But safety-net providers often cannot pay fees charged for each visit by many scribing products. Kara Carter said current price models do not work well in these settings. Different payment plans and group buying may help make these tools affordable.
Small health groups that build their own AI scribing tools can adjust for language and care styles to fix problems like language gaps and cultural needs that commercial products may miss.
AI in U.S. healthcare has good possibilities but also needs careful use. Using data from all patients and having rules about cost, responsibility, and technology are key to avoiding bigger health differences. Medical practice leaders and IT managers can help by learning about these issues and working for fair and ethical AI use. This way, all patients can benefit, not only a few.
Ambient medical scribing AI technologies automatically capture and transcribe physician-patient interactions, reducing the time doctors spend on documentation. This alleviates burnout, improves physician quality of life, and increases face time with patients, enhancing care delivery.
Safety-net providers face prohibitive costs, lack of infrastructure, workforce limitations, and liability concerns, which restrict their ability to integrate AI tools. These barriers prevent equitable access to AI benefits, risking widening health disparities among vulnerable populations.
Ambient scribing reduces after-hours documentation burden, lowering physician burnout and turnover. Retaining providers ensures timely patient access, which can prevent complex, costly health conditions, yielding long-term cost savings for healthcare organizations.
Current AI pricing models, often based on usage or provider visits, are too expensive for safety-net entities. Limited budgets and unclear ROI inhibit purchasing, and additional costs related to infrastructure and expert personnel further hinder adoption.
Potential solutions include vendor discounts, group purchasing agreements, bulk deals with AI companies, and partnerships to reduce costs. However, infrastructure and staff shortages remain significant barriers needing additional support.
Providers worry about who is financially responsible for AI errors. Without clear accountability from the state or regulators, safety-net organizations fear legal repercussions, which may discourage AI adoption due to liability fears.
Incorporating safety-net patient data helps train AI models to reduce racial and ethnic biases, ensuring AI tools accurately serve diverse populations and advance health equity rather than exacerbate disparities.
Poor broadband connectivity and fragmented data exchange systems hinder AI implementation. Regions like California’s Central Valley and rural areas lack necessary digital infrastructure and struggle with language access, limiting AI’s reach.
Smaller organizations with simpler decision-making feel freer to experiment and create customized AI solutions, while larger systems face complex infrastructure and regulatory challenges that slow implementation.
Policymakers should establish clear accountability guidelines, promote inclusive data sharing, fund infrastructure improvements, and ensure safety-net voices and patients are included in AI policy discussions to foster equitable deployment.
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