Federally Qualified Health Centers and other safety-net organizations provide essential medical services no matter if patients can pay or not. These providers often face special problems, such as higher rates of long-term illnesses among their patients and limited access to specialists. This creates a strong need for operational efficiency and patient-centered care that AI solutions can help with.
A key effort to study and support AI use in these settings is the “AI in Action: Practical Applications for Safety Net Providers” monthly Zoom series. It is co-hosted by the Center for Care Innovations and the Health AI Partnership. This series focuses on applying AI within FQHCs and safety-net organizations to improve patient results, make care providers’ work easier, and get better operations where resources are limited.
One recent session, held in October 2025, collected data from 16 focus groups and 4 online bulletin boards with 172 participants across California. Most were from low-income and historically underserved communities. This research gave useful patient views on how they accept, trust, and worry about AI uses like ambient scribing, predictive analytics, and AI-driven triage.
Documentation is one of the most time-consuming tasks for clinical providers. Detailed notes are needed for each patient visit, but this often takes doctors and nurses away from direct patient care. Ambient scribing is an AI-driven technology that captures and transcribes conversations and clinical interactions as they happen. It greatly reduces the manual documentation work.
In healthcare settings with few resources, ambient scribing helps by cutting down the time spent recording notes per patient. This lets providers see more patients and spend more time on clinical care. This is very important where there are staff shortages and tight appointment schedules.
Patients in the California study were somewhat hopeful about ambient scribing but also had worries about privacy and accuracy. They said clear communication from providers about how the technology works and how patient data is kept safe is needed to build trust.
The California Health Care Foundation (CHCF) Innovation Fund, represented by senior program investment officer Stella Tran, is supporting investments in technologies like ambient scribing. The goal is to reduce care costs and increase access for low-income groups. Transparency and cultural understanding are key since these communities often have little exposure to AI tools and more questions about their use.
Predictive analytics looks at patterns in past and current data to predict patient needs or risks. In places with limited resources, this helps healthcare providers expect patient admissions, find high-risk patients, and plan resources better.
For example, predicting asthma attacks in children, which is common among underserved groups, helps providers act earlier. This reduces visits to the emergency room and hospital stays. These AI models use patient histories, environmental data, and social factors to give alerts or care advice.
But using predictive analytics needs good data, which many FQHCs and small health centers have struggled to get. In the European Union, the European Health Data Space (EHDS) initiative shows how safe, connected health data can help AI grow. In the U.S., data fragmentation and privacy worries still make adopting analytics hard.
Security and trust stay very important. CHCF research shows patients especially want proof that data will not be misused or biased to make health gaps worse. Cecilia Cordoba, founder of Culture IQ and an expert on multicultural consumer insights, says the voices of diverse groups must guide AI’s design and use to keep it fair and inclusive.
Triage means deciding how urgent a patient’s condition is to give care in the right order. AI triage tools analyze patient symptoms, medical history, and other data to help staff send patients to the right places.
For healthcare providers with few staff and many patients, like FQHCs, AI-supported triage can lower wait times and stop unnecessary emergency room visits. Triage algorithms can also point out patients who need quick care or different treatment paths, making better use of resources.
Despite benefits, patients in the California study worried about AI replacing human judgment. Trust in AI triage depends on clear information, supervision, and assuring that technology helps rather than replaces clinical decisions.
Besides uses like scribing, analytics, and triage, AI also helps automate healthcare workflows to improve operations.
AI-powered automated answering services and phone systems can handle routine tasks like booking appointments, callbacks, and patient questions. This lets front office staff focus on harder tasks and cuts down phone wait times, which makes patients happier.
Simbo AI’s front-office phone automation shows how AI can reduce admin blockages in medical offices. Their AI can answer calls 24/7, sort patient requests, and route calls to the right departments fast. For FQHCs and clinics stretched thin, this tech lowers no-shows and helps manage patient flow.
AI tools can send reminders for appointments or medicine refills, check if patients follow treatments, and flag missed follow-ups. Automating these messages keeps patients engaged, which is key in underserved groups that often face social barriers to care.
AI-driven documentation and workflow tools help keep records complete and correct, cutting administrative errors. As AI scribing records detailed clinical talks, coding and billing get more accurate. This accuracy affects revenue management, which is important for resource-limited providers to survive.
Patient acceptance is important for using AI well in healthcare settings that serve vulnerable groups. Trust grows when organizations adopt AI with openness, clear communication, and cultural understanding.
Experts like Stella Tran, Michele Cordoba, and Ana Fernández-Rockwell show the need to include community voices—especially from multicultural and underserved groups—in AI design. Their work points out the need for bilingual outreach and patient education to explain how AI tools work, protect privacy, and improve care.
Also, being clear about how data is used and following ethical rules helps ease worries about bias. Patient-centered AI use makes sure these communities benefit fairly from new technology instead of risking being left out or harmed.
While the European Union has clear rules like the Artificial Intelligence Act (AI Act), the European Health Data Space (EHDS), and the Product Liability Directive to control AI’s development, use, and responsibility, the U.S. is still working on its approach to AI in healthcare.
Healthcare providers in the U.S. need to know about privacy and security laws like HIPAA that affect AI data use. Managers must balance new ideas with rules, making sure AI meets standards, especially for patient safety.
In places with limited resources, cost is an important factor for adopting AI. Groups like the CHCF Innovation Fund help pay for technology that lowers costs and improves care for low-income people. This offers possible funding for centers thinking about AI.
Artificial intelligence can change how healthcare providers with few resources operate in the U.S. Tools like ambient scribing cut down on documentation work, predictive analytics help find risk groups, and AI-driven triage improves patient flow. These tools help safety-net providers use scarce resources better, leading to better patient results and happier providers.
Organizations like the Center for Care Innovations, CHCF, and experts such as Stella Tran and Michele Cordoba show that careful, fair, and patient-focused AI use is very important. Addressing patient worries about trust, privacy, and bias is key for success.
For medical practice administrators, owners, and IT managers in the U.S., AI can make operations more efficient, lower costs, and increase care access if done thoughtfully. Paying close attention to communication, cultural fit, data safety, and workflow fit can help AI improvements make a big difference for healthcare providers serving vulnerable and underserved populations.
‘AI in Action’ is a monthly Zoom series co-hosted by the Center for Care Innovations and the Health AI Partnership. It focuses on exploring AI research and implementation in Federally Qualified Health Centers (FQHCs) and safety net healthcare organizations to promote equitable access and improve patient outcomes.
Patient trust is essential because AI tools are newly introduced to healthcare, especially in safety-net settings serving low-income and underserved populations. Trust ensures acceptance and effective use of AI-driven care, making adoption successful and equitable.
Insights were collected through 16 focus groups and 4 online bulletin boards involving 172 participants across California, focusing on low-income and historically underserved communities’ perceptions of AI in healthcare.
Patients reacted to AI tools such as ambient scribing, predictive analytics, and AI-driven triage, giving feedback on their concerns and expectations from providers and technology vendors.
Key insights highlight the need for clear communication about AI’s role, addressing patient concerns on privacy and bias, ensuring transparency, and culturally competent engagement to build trust among underserved communities.
Stella Tran is a senior program investment officer at the CHCF Innovation Fund, investing in technologies that reduce healthcare costs or improve access for low-income Californians. She has a background in impact investing focused on healthcare innovation.
Michele Cordoba leads Culture IQ, a market research firm amplifying multicultural consumer voices, providing strategic insights in English and Spanish to ensure AI solutions address diverse community needs effectively.
Ana’s bilingual, bicultural experience and research focus on U.S. Hispanic populations help capture nuanced patient attitudes, ensuring AI applications consider linguistic and cultural factors in underserved groups.
Past topics include AI adoption challenges, pediatric asthma care models, product due diligence, leveraging AI in FQHC settings, and AI implementation in safety net hospitals, sharing practical insights and lessons learned.
AI supports operational excellence by streamlining workflows such as documentation via ambient scribing, improving predictive care through analytics, and optimizing patient triage, enhancing provider efficiency and care quality in resource-limited FQHC environments.
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