Infection tracking and disease surveillance have long been important in public health. Using AI agents to watch public health data can give faster, more accurate, and detailed information about disease outbreaks. The Centers for Disease Control and Prevention (CDC) is a main example in the US that uses many AI tools and machine learning models to improve public health work.
The CDC uses AI systems to study large amounts of data, like reports of symptoms in emergency rooms, social media posts, and news stories. AI tools process about 8,000 news articles every day to spot signs of infectious diseases and outbreaks. These AI systems make it easier to find outbreaks quickly and help start responses earlier. This is very important to stop the spread of illnesses like the flu or new infectious diseases.
The CDC’s National Syndromic Surveillance Program uses AI to check real-time symptom data. This helps to spot potential public health dangers faster. This program has improved the ability to understand situations and speed up emergency responses. AI also supports flu forecasting with FluSight by mixing past health data with current epidemiological and online social information. This helps clinics and hospitals prepare and use their resources better.
The effect of AI at the CDC can be measured. Using a generative AI chatbot across the agency saved an estimated $3.7 million in labor costs. This resulted in a 527% return on investment. AI tools also automated the review of thousands of grant reports, saving about 5,500 work hours and $500,000 every year. These savings let public health staff focus more on tough decisions instead of routine data work.
AI agents are also helping medical research, especially in managing clinical trials and finding new drugs. By using AI predictive tools with patient data, disease trends, and genetic information, researchers can better predict infection spread and patient results. This helps find good candidates for clinical trials and watch trial data continuously.
During the COVID-19 pandemic, AI changed many parts of clinical support. Deep neural networks helped speed up diagnosis by analyzing medical images, which let doctors find COVID-19 cases faster. AI-based systems also helped in drug discovery by doing fast virtual drug tests. This quickly found possible medicine candidates and suggested reusing some existing drugs. These uses helped make treatments faster and supported approval and clinical decisions.
Besides diagnosis and drug discovery, AI systems help control epidemics by supporting risk checks and policy plans using real-time social data. These systems look at how communities respond and find outbreaks before usual reports, letting healthcare leaders act sooner.
People from healthcare management, technology, data science, and ethics work together in making AI solutions. They make sure AI tools are trustworthy, fair, and useful in medical settings. Problems with AI like data quality, IT readiness, and privacy and fairness remain important to fix to make AI use safe and effective.
For medical managers and healthcare IT supervisors, AI agents help improve workflow by automating routine tasks, lowering administrative work, and making operations smoother. AI tools handle tasks like patient scheduling, prior authorization, insurance checks, and patient communication, which usually take a lot of staff time.
Salesforce’s Agentforce for Health is an example of an AI platform made to automate front-office healthcare tasks. It offers AI agents that perform eligibility checks, verify insurance coverage, handle prior authorizations, and set appointments with little human help. These AI systems connect with electronic health records (EHR) and insurance platforms to complete these tasks in seconds. This lowers delays and reduces staff workload.
Healthcare workers say that administrative tasks often make them work late hours; 87% say they often stay late because of paperwork, and nearly 60% say this hurts their job satisfaction. AI agents might reduce these problems a lot. Healthcare teams expect a 30% workload cut for doctors, 39% for nurses, and 28% for admin staff. Overall, teams hope to save up to 10 hours a week by using AI in daily work.
Rush University System for Health uses Salesforce’s AI assistants to give 24/7 patient support and automate admin tasks. Jeff Gautney, CIO of Rush University, says AI helps patients find their way through healthcare and connect with providers who suit their needs. This frees human agents to handle harder patient issues, improving both operations and patient satisfaction.
By handling patient talks quickly and correctly, AI agents support care coordinators with full patient summaries. These include medical history, referrals, care gaps, and insurance details. This helps make better care plans and personalized care before appointments.
During sudden public health crises like disease outbreaks, fast data and clear communication are very important. AI agents help make infection tracking faster and more reliable.
At the CDC, AI applications watch symptom data from emergency rooms all over the country. Machine learning algorithms study these data to find patterns that show new outbreaks. This lets officials find threats faster than older ways. AI also spots environmental clues linked to infection sources. For example, it can find cooling towers tied to Legionnaires’ disease outbreaks. This saves more than 280 person-hours a year on investigations.
During the COVID-19 pandemic, AI helped with clinical trial enrollment, predicting patient outcomes, and developing treatments. This helped public health leaders make decisions more quickly. AI models mixed clinical, epidemiological, and genetic data to predict patient risk and use hospital resources better. This helped health organizations plan and carry out focused actions.
In future public health emergencies, AI’s role will probably grow as more data sources are added and analysis gets better. The CDC’s AI Accelerator program helps increase AI and machine learning use that matches public health needs. This program and the AI Community of Practice provide ongoing training so staff can use AI tools well during crises.
Using AI agents well in public health and clinical trials depends on how smoothly they work with current healthcare IT systems. Platforms like athenahealth and Availity work with AI providers like Salesforce to allow real-time talks between healthcare providers and payers.
These partnerships make processes like insurance eligibility checks and prior authorization approvals faster. AI agents send requests and get answers in seconds. This helps healthcare organizations meet Centers for Medicare & Medicaid Services (CMS) rules while cutting down delays.
Healthcare owners and IT managers gain by cutting paperwork, moving patients faster, and lowering billing mistakes. Automating routine front-office jobs also frees up staff time for patient care and harder admin tasks.
Using AI agents in public health monitoring and clinical trial support is still growing. Although benefits are clear, some challenges remain for medical practices in the US:
Health systems and medical practices that handle these challenges and invest in AI automation can improve how they work. Intelligent tools will help make faster, data-based decisions, better public health responses, and improved clinical research results.
Using AI agents for public health monitoring, clinical trial support, and everyday healthcare administration helps medical managers in the US cut down manual work. It also improves accuracy in tracking infections, speeds up medical advances, and ultimately provides better care for patients. These changing technologies offer useful tools to help healthcare organizations manage costs, meet requirements, and deliver patient-focused services in a more complex healthcare system.
Agentforce for Health is a new library of pre-built AI agent skills and actions created by Salesforce in 2025 to address time-consuming administrative healthcare tasks like eligibility checks, scheduling, insurance verification, and prior authorization.
The AI agents handle patient inquiries, eligibility checks, insurance benefit verifications, prior authorizations, scheduling appointments, monitoring infection spread, and supporting clinical trial site analysis and innovation.
AI agents reduce administrative burdens, saving healthcare teams up to 10 hours weekly, with estimated workload reductions of 30% for doctors, 39% for nurses, and 28% for administrative staff, thereby improving job satisfaction.
The agents chat directly with patients to match them with in-network providers and specialists and intelligently schedule appointments via integration with electronic health record systems like athenahealth.
Salesforce partners with athenahealth for scheduling, Availity for direct payer communication and eligibility checks, and Infinitus.ai for electronic benefits verification to streamline prior authorization and insurance validation processes.
Agentforce supports compliance with Centers for Medicare & Medicaid Services interoperability mandates by enabling real-time submissions and receipt of prior authorization decisions within seconds, reducing administrative delays.
AI monitors the spread of infections by auto-classifying cases and accelerates drug and medical device innovation via real-time integrated study data and intelligent clinical trial support.
Agentforce provides care coordinators with patient summaries including medical history, referrals, care gaps, and benefits, enhancing patient access and personalized care management prior to appointments.
Organizations like Rush University System for Health use AI to automate administrative tasks and provide 24/7 patient support, freeing human staff to focus on complex issues and improving the patient experience.
Salesforce executives anticipate a modest revenue contribution from Agentforce in fiscal year 2026, with a more meaningful financial impact expected in the following year, reflecting gradual market adoption.
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