Artificial intelligence (AI) is becoming more common in hospitals and clinics in the United States. These places use AI to help improve patient care and make administration tasks faster. For example, companies like Simbo AI create AI systems that answer phones and schedule appointments. But as these tools are used more, it’s important to make sure they work fairly for all patients. A big issue is algorithmic bias—this happens when AI gives different results for different groups of people because of biased data or design. People who run medical practices and IT managers need to know how to find and reduce this bias. This article explains what causes AI bias in healthcare, why it matters, and ways to lower it, focusing on U.S. healthcare organizations.
Healthcare AI often uses machine learning (ML). This means the AI learns from data to help with things like diagnosis, treatment suggestions, or managing phone calls. These tools can improve care and efficiency, but they depend a lot on the data used. If the data is not diverse or is unevenly gathered, the AI might develop bias. That means it works well for some patients but not others, leading to unfair care.
Bias can happen at different stages: when making the AI, testing it, putting it into use, or even later. For example, if medical AI is mostly trained on data from one racial group, it might miss signs of illness in other groups. AI models made in one area might not work well in a different area unless changed. Dr. Harriette GC Van Spall and her team said that bias can come from limited data, design choices, and how clinicians use AI in the real world.
In heart care, such bias can cause wrong diagnoses or risk predictions, often hurting marginalized groups more. Without fixing these issues, AI may make health inequalities worse. Reducing bias is very important to make healthcare fair for everyone.
Research from health systems in San Diego shows how AI works in real life and its risks. At UC San Diego Health, Dr. Gabriel Wardi created an AI model to predict sepsis risk by looking at about 150 patient factors in almost real-time. This AI has helped save around 50 lives each year by spotting sepsis early. But the AI worked differently depending on the hospital. At Hillcrest, the model needed changes to fit its specific patients well. This shows how AI must adjust to local populations.
At Scripps Health, AI tools help doctors by reducing the time spent on paperwork to about seven to ten seconds per patient. This gives doctors more time to focus on patients. Still, protecting data privacy, getting patient permission, and ethical use remain important issues.
Dr. Christopher Longhurst said that people expect too much from AI in the short term. But he believes AI will change healthcare a lot over the next ten years, similar to how antibiotics changed medicine.
Investment money flowing into AI startups shows strong belief in AI’s future. A report from Rock Health said that about one-third of $6 billion invested in U.S. digital health firms in 2024 went to AI-based tools.
To lower AI bias, healthcare groups need to know where it comes from. Causes include:
These issues show how hard it is to create AI that fits many real-world medical situations. Patients and clinics are different, so one solution does not fit all.
Ethics is a key part of dealing with bias. AI systems must be clear about how decisions are made so patients trust them and doctors can be responsible. Patients should know when AI tools are used in their care and agree to have their data processed.
Medical staff must balance AI automation with human checks to avoid mistakes and keep care quality. Dr. Eric Topol, a digital medicine expert, says doctors must review AI results to prevent harm.
Another issue is data privacy, especially with sensitive health records. Laws like California’s SB 1120 require health insurers and providers to meet safety and fairness standards when using AI.
At places like Scripps Health, rules require getting patient permission before recording appointment notes or analyzing health data with AI. This shows growing efforts to protect privacy and rights.
Medical practice leaders and IT teams can take these steps to reduce AI bias:
For medical managers and IT staff, AI can also make front-office work smoother. Tasks like answering phones, setting appointments, and handling patient data can be automated with AI. Companies like Simbo AI use tools that understand speech and language to do this. Automation cuts down on busywork and helps patients get care faster.
But automation must be made carefully to avoid bias or unfairness. For example, phone answering AI should understand many accents and ways of speaking. If it cannot, some patients may have trouble making appointments or talking to staff.
Administrators should:
Using AI for patient communication can lower wait times, manage many calls faster, and let staff focus on harder tasks. Simbo AI and similar companies show how AI front-office tools fit with clinical work and support better patient access.
Healthcare in the U.S. serves many different groups by race, language, insurance, and location. Algorithmic bias matters more when it hurts groups that already face healthcare challenges. Medical leaders should pay attention to:
AI in healthcare can improve how patients are treated and how operations run. Reducing AI bias is key to avoid harm and make sure all patient groups get the same benefits. By choosing good data, testing well, checking AI often, following ethical rules, and training staff, U.S. health providers can use AI to support fair care.
Companies like Simbo AI that automate front-office tasks also help by lowering paperwork and improving patient interactions. But they must make sure their AI works fairly for everyone. AI tools that discriminate can make existing problems worse instead of better.
By using a full approach to reduce AI bias and combining it with smart automation, healthcare groups can work toward a future where technology serves all patients fairly and carefully.
Clinics in San Diego, like UC San Diego Health and Scripps Health, are early adopters of AI because it has the potential to improve diagnoses, manage patient data, and enhance the overall healthcare experience while saving significant time for healthcare providers.
AI is used for predicting sepsis risk, transcribing appointments, summarizing patient notes, generating post-exam documentation, and identifying conditions from images, among others.
AI tools have helped reduce documentation time, allowing physicians to spend more time with patients, thereby rehumanizing the examination experience.
Concerns include data privacy issues, potential job displacement, the accuracy of AI predictions, and whether patients are aware when AI is used in their care.
AI models analyze approximately 150 variables in near real-time from patient data to generate predictions on who may develop sepsis, significantly improving early detection.
Investors are increasingly funding AI in healthcare, with a third of nearly $6 billion in digital health investments going to AI-driven companies, signaling confidence in the technology’s future.
Ethical concerns focus on whether patients fully understand AI’s role, the protection of their health data, and how AI decisions may affect treatment recommendations.
Addressing algorithmic bias involves using diverse data sets tailored to specific populations, which can help enhance the accuracy of AI applications and reduce disparities in care.
Human oversight is crucial in using AI; clinicians must review AI-generated content to ensure accuracy and appropriateness in patient care, preventing potential errors.
Experts project that AI will dramatically change healthcare delivery within the next decade, potentially improving diagnosis accuracy and reducing medical errors significantly.
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.
