Artificial Intelligence (AI) has changed many parts of healthcare in the United States. It is used in tools for diagnosis, talking with patients, and managing office tasks. But as AI gets more complex and can change itself, the rules around it must also change. These rules must make sure AI tools are safe, fair, and work well throughout their use. This article talks about the challenges with changing AI in healthcare, how it is being watched over, and the AI tools used by medical office leaders and IT staff.
Usually, healthcare technology rules assume a product or software does not change after approval. But AI, especially those that learn from data, keeps changing. For example, AI tools like Viz.ai for stroke detection and Duke Health’s Sepsis Watch use real-time patient data to give quick reports. This helps patients but also causes challenges regulators did not face with fixed software.
The Food and Drug Administration (FDA) is in charge of approving medical devices and their safety in the U.S. It is hard for the FDA to control AI that changes its algorithm after approval. Between 2020 and 2021, the FDA saw a big rise in AI medical device applications. Now, almost 1,000 AI or machine learning medical devices have FDA approval. Still, the FDA needs ways to keep an eye on how AI changes, using methods beyond the usual one-time review. This means checking performance often, running safety tests in real time, and having plans ready if AI tools start giving wrong or harmful results.
Many AI systems update and improve by learning from new data all the time. This helps them become more accurate. But it also means their decision-making can change in ways that are hard to predict. Current FDA rules mainly look at the product before it is sold, which is not enough for AI that changes after approval. So, new rules are needed to watch AI after it is approved. This includes ongoing checks and tests to make sure AI stays safe and works as it should.
Doctors and patients need clear reasons for how AI tools make decisions, especially when those decisions affect diagnosis or treatment. However, some AI, like deep learning models, can be hard to understand and seem like “black boxes.” The Centers for Medicare & Medicaid Services (CMS) wants AI systems to be more open, especially those used in deciding if insurance will pay for care. CMS believes explaining AI’s role helps build trust and makes sure that humans can review decisions when needed.
AI trained on biased data can make health inequalities worse. For example, the VBAC calculator used race-based corrections that unfairly affected African American and Hispanic women. This bias was found only after humans checked the tool. The U.S. Department of Health and Human Services (HHS) has rules for healthcare organizations to find and fix unfair AI outcomes. These rules require regular audits and human checks to protect vulnerable patients.
People in medical organizations must keep control over AI results. Without human checks, errors like wrong patient records or bad data analysis can hurt patient care. Also, AI decisions about insurance approvals may wrongly deny needed treatments, sometimes causing lawsuits. Humans must always review AI decisions and have the power to change wrong ones.
Groups like EPIC, Valid AI, MITRE, and CHAI suggest creating “assurance labs” where AI tools are tested fully before and during use in clinics. These labs check safety, accuracy, biases, and other factors. They help make sure AI tools are good before sending them to work and while they are in use. These labs are not yet officially accepted by HHS but may be part of future rules.
After AI devices are approved, regular checks could include reviewing data, asking users for feedback, and using software to spot any problems. Regulators could require this ongoing watch to make sure AI still works well in real settings, not just in tests.
The way healthcare pays for services must change to support AI development and use. Instead of paying for each service done, payments should link to patient health results and care quality. This way, AI tools that help improve diagnosis or reduce paperwork receive rewards tied to the benefits they bring.
Besides clinical applications, AI is used to automate tasks in medical offices. This helps reduce costs and relieve staff burnout. AI systems can handle things like phone calls, paperwork, and billing.
Companies like Simbo AI provide AI phone systems to automate calls with patients. These systems schedule appointments and answer common questions. They reduce staff workload and let patients get quick help on the phone. This works well in busy city or rural clinics, making office work more efficient without losing good communication.
Tools like AI scribes and ChatGPT are used with electronic health records like Epic’s MyChart. They help doctors and nurses spend less time on paperwork. These systems transcribe notes and create discharge instructions automatically. This helps healthcare workers focus more on patients and less on writing reports, which is a major cause of burnout.
Medicare Advantage plans handled over 46 million prior authorization requests in 2022, many with AI help. Almost half of U.S. hospitals use AI for billing, claims, and scheduling. But sometimes, AI decisions wrongly deny coverage. Companies like Humana and UnitedHealthcare faced lawsuits over AI coverage denials. CMS says that use of AI in these decisions must be clear to patients and providers and that humans must check AI decisions.
Automation in revenue management helps speed up claims and reduce mistakes. AI can find errors like coding problems that cause claim denials and fix them faster. But because AI programs keep changing, they need constant review to stop mistakes that affect fairness or money accuracy.
Balancing Innovation and Compliance: The government has pushed for rules that balance new AI technology with safety. Clinics must follow new guidelines without slowing down useful AI tools.
Ensuring Staff Training and Engagement: Even with AI help, humans need to understand AI. Regular training helps staff use AI wisely and catch when it may be wrong.
Policy and Contract Review: Organizations should carefully check contracts with AI vendors. They must focus on how data is used, fairness checks, accountability, and following anti-discrimination rules.
Investment in Monitoring Infrastructure: Clinics should set up systems to watch AI performance all the time. This helps spot problems quickly and keeps AI safe and legal.
Transparency and Patient Communication: Be open with patients when AI is used for diagnosis, billing, or scheduling. This helps build trust and follows CMS advice.
Knowing how AI changes and following new rules can help healthcare groups manage AI risks and benefits better. Using good oversight with workflow automation improves how clinics run and patient care. But paying attention to rules and ethics is still very important. Medical office leaders, owners, and IT managers need to stay informed, watchful, and active to handle AI challenges in healthcare.
AI-enabled diagnostics improve patient care by analyzing patient data to provide evidence-based recommendations, enhancing accuracy and speed in conditions like stroke detection and sepsis prediction, as seen with tools used at Duke Health.
Human oversight ensures AI-generated documentation and decisions are accurate. Without it, errors in documentation or misinterpretations can harm patient care, especially in high-risk situations, preventing over-reliance on AI that might compromise provider judgment.
AI reduces provider burnout by automating routine tasks such as clinical documentation and patient communication, enabling providers to allocate more time to direct patient care and lessen clerical burdens through tools like AI scribes and ChatGPT integration.
AI systems may deny medically necessary treatments, leading to unfair patient outcomes and legal challenges. Lack of transparency and insufficient appeal mechanisms make human supervision essential to ensure fairness and accuracy in coverage decisions.
If AI training datasets misrepresent populations, algorithms can reinforce biases, as seen in the VBAC calculator which disadvantaged African American and Hispanic women, worsening health inequities without careful human-driven adjustments.
HHS mandates health care entities to identify and mitigate discriminatory impacts of AI tools. Proposed assurance labs aim to validate AI systems for safety and accuracy, functioning as quality control checkpoints, though official recognition and implementation face challenges.
Transparency builds trust by disclosing AI use in claims and coverage decisions, allowing providers, payers, and patients to understand AI’s role, thereby promoting accountability and enabling informed, patient-centered decisions.
Because AI systems learn and evolve post-approval, the FDA struggles to regulate them using traditional static models. Generative AI produces unpredictable outputs that demand flexible, ongoing oversight to ensure safety and reliability.
Current fee-for-service models poorly fit complex AI tools. Transitioning to value-based payments incentivizing improved patient outcomes is necessary to sustain AI innovation and integration without undermining financial viability.
Human judgment is crucial to validate AI recommendations, correct errors, mitigate biases, and maintain ethical, patient-centered care, especially in areas like prior authorization where decisions impact access to necessary treatments.
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