Preventive health actions help find and manage diseases early, like colorectal cancer (CRC). The fecal immunochemical test (FIT) is used to screen for CRC and helps lower death rates. But many people still do not take part in these screenings, especially those who do not speak English well.
AI tools that send messages to patients could help fix this problem. These tools can automate communication and send messages in the patient’s own language. This can raise awareness, get more people involved, and increase screening rates. A study with a bilingual AI care agent called “Ana,” made by Hippocratic AI and tested at WellSpan Health, involved 1,878 patients. It included 517 Spanish speakers and 1,361 English speakers. The study showed how language and other factors affect how people respond to AI outreach in healthcare.
The WellSpan Health study found that Spanish-speaking patients were much more involved with AI outreach than English-speaking patients. The rate of opting in for the FIT test was 18.2% for Spanish speakers and only 7.1% for English speakers. This means Spanish speakers were more than twice as likely to choose screening when contacted by the AI.
Other ways Spanish speakers were more engaged include:
These differences are important and show that AI communication in the patient’s language works better to get attention and responses.
Spanish-speaking patients were usually younger (average age 57) than English-speaking patients (average age 61). Also, more females were among Spanish speakers (49.1%) than English speakers (38.4%). But when analyzing the data, only Spanish language preference and call length predicted who would opt in. Age, gender, and location did not predict this significantly after other factors were considered.
These results go against the idea that AI might hurt non-English speakers in healthcare. Instead, bilingual AI agents that talk to patients in their language can help reduce gaps in access to preventive care.
For medical office managers and IT leaders, especially those serving diverse or Hispanic communities, using AI tools that work in many languages is important. The WellSpan Health study supports other research that shows how natural language AI agents can remove common barriers, like language and culture, that block care access.
Clinics with patients who speak little English should think about using AI tools with language features. For example, Simbo AI’s phone automation handles routine tasks like setting appointments, reminders, and giving health education in different languages. This system helps get more patients involved, cuts down missed visits, and makes outreach easier without adding work for front-office staff.
IT managers can use AI to make front-desk work more organized. Automating patient calls about screening or vaccinations in the patient’s preferred language saves time, lowers human mistakes, and makes sure patients get follow-ups on time. Since longer calls led to more opt-ins, it shows AI agents that spend time educating and answering questions affect patients in a good way.
AI can help more people, especially those who do not speak English well, take part in health screenings. But there are some problems to watch out for. One big problem is algorithm bias. Some studies say that AI tests work 17% less well for minorities because the training data is not complete or balanced. Healthcare groups need to check and update AI systems often with data from all people they serve.
Another problem is the digital divide. About 29% of adults in rural U.S. areas cannot use AI health tools because they lack internet, skills, or infrastructure. This stops AI from reaching people in rural or poor places. So, AI should be added alongside efforts to improve internet access and digital skills.
Only 15% of AI health tools include patient community input during their creation. Getting patients involved can make AI tools more accepted and useful. Medical leaders must support ways to get feedback and make sure AI does not cause problems like too many tests or excluding people.
Finally, many studies only check AI results for less than one year. We need more long-term research on whether patients follow through with screenings and if AI leads to better health results, like finding cancer early.
Using AI in healthcare is not just about reaching patients. It also helps offices work better. Simbo AI shows how AI can make front-office phone work easier. This is important because patients contact clinics this way.
Automated Patient Communication: AI calls can do many routine tasks like confirming appointments, refilling prescriptions, and reminding about care. This helps staff have less work and keeps messages consistent.
Multilingual Support: AI systems support many languages. This helps clinics talk better with patients from different backgrounds. Calls in a patient’s language build trust, help patients understand health information, and make patients happier.
Personalized Patient Engagement: AI can use health records to customize talks based on patient age, health history, and screening needs. This makes the messages more useful and get patients to act, shown by better engagement in the AI care agent study.
Data Capture and Analytics: AI calls collect data like call length, answers from patients, and how many opted in. This data helps clinics improve outreach and plan better programs.
Compliance and Security: AI phone systems follow privacy laws like HIPAA, so patient information stays safe. This means adding AI does not lower data protection.
Using AI for front-office tasks helps clinics reduce staff workload, improve patient contact, and make care more consistent. Good technology use supports both medical goals and patient experience.
The WellSpan Health study shows results from a big healthcare system in Pennsylvania and Maryland. These findings matter for healthcare leaders and IT staff in this area or similar places.
Pennsylvania and Maryland have both cities and rural areas and many people who speak different languages, including many Spanish speakers. Using AI programs in many languages fits well with public health aims to increase screenings and reduce gaps in care.
Healthcare leaders in these states saw that the bilingual AI agent got call answer rates near 89% in Spanish speakers. This is a big improvement over old outreach ways. Using AI tools like Simbo AI’s can help state health programs that focus on finding cancer early and preventing chronic diseases.
Healthcare offices face staff shortages, many patients, and high costs. AI automation for patient calls offers a way to save resources by taking over repetitive tasks.
Medical managers often try to improve care but must watch budgets. Using AI phone systems can save money by cutting staff time spent on calls and lowering missed appointments with reminders.
The study showed that longer calls led to more people agreeing to screenings. AI agents can have steady conversations without the unpredictability of human calls. This improves patient contact and lets staff handle harder or sensitive matters.
By using AI tools with these goals, healthcare managers and IT leaders can help make medical care easier to reach instead of harder for patients.
The primary objective was to evaluate the effectiveness of a multilingual AI care agent in engaging Spanish-speaking patients for colorectal cancer screening compared to English-speaking patients.
The study included 1878 patients eligible for colorectal cancer screening; 517 were Spanish-speaking and 1361 were English-speaking patients without active web-based health profiles.
The AI agent made personalized telephone calls in the patient’s preferred language, provided education about colorectal cancer screening, and facilitated fecal immunochemical test (FIT) kit requests.
The primary outcome was the fecal immunochemical test (FIT) opt-in rate to gauge patient engagement with colorectal cancer screening.
Spanish-speaking patients had significantly higher engagement: FIT opt-in rates were 18.2% versus 7.1%, connect rates were 69.6% versus 53.0%, and call durations averaged 6.05 minutes versus 4.03 minutes for English speakers.
Yes, Spanish language preference was an independent predictor of FIT test opt-in with an adjusted odds ratio of 2.012, meaning Spanish speakers were twice as likely to opt-in after controlling for demographic factors and call duration.
Spanish-speaking patients were younger (mean age 57 vs 61 years) and more likely to be female (49.1% vs 38.4%) compared to English-speaking patients.
The findings suggest that language-concordant AI outreach can reduce longstanding disparities in preventive care access by significantly increasing engagement among non-English-speaking populations.
Limitations included being conducted in a single healthcare system, a short study duration, and the absence of follow-up data on whether patients completed screenings after opting in.
Future research should focus on assessing long-term adherence to screenings and determine whether increased engagement with AI outreach translates into improved clinical outcomes for patients.
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