Primary care is the first part of the healthcare system. Patients go to family doctors, internists, and general practitioners for check-ups, managing long-term illnesses, and urgent health problems. But paperwork and not enough staff mean these doctors have less time for patients. AI tools like Tom try to fix this by handling many of these tasks, so doctors can spend more time with patients.
Tom, made by Lumeris as a Primary Care-as-a-Service (PCaaS) platform, uses multiple AI agents to do jobs like scheduling appointments, checking if patients take their medicines, following up after hospital visits, coordinating care, and teaching patients. By automating these tasks, Tom helps reduce daily burdens on providers. Doctors can then spend more time with patients without working harder. This system runs all day and night and is built into regular primary care work.
Healthcare decisions must be accurate because they directly affect patients. Lumeris tested Tom carefully before using it in clinics. The platform was tested with more than 260,000 cases covering clinical and non-clinical situations to find any problems. This extensive testing reduces risks and prepares for rare but important errors that could harm patients.
Choosing the right AI models was very important. Over 60 large language models (LLMs) were checked and tested to find those that work well, follow rules, and stay safe in clinical settings. Sometimes AI can give wrong or made-up answers, called “hallucinations.” Such mistakes can be dangerous in healthcare. So, safety controls were added to prevent errors and make sure AI answers are reliable and correct.
This careful checking helps healthcare groups trust AI. Primary care providers can believe that decisions made or actions taken by AI follow clinical rules and laws. Also, the AI is tested all the time to keep working well, as healthcare rules and data change.
Even after the first tests, AI in primary care needs constant watching to keep it safe and correct. Dr. Adnan Masood, PhD, says trust in AI comes not just from good algorithms but also from real-time monitoring, controls focused on people, and active management.
Continuous monitoring means watching AI behavior as it happens to find mistakes, strange results, or weaker performance. This helps spot problems early so humans can step in or adjust the AI. Unlike systems that run entirely on their own, human-centered controls keep doctors and managers in charge to protect patient safety.
There are rules that guide how AI is used. These include policies, checks for following laws, and ethical standards. Such rules stop misuse and keep trust. This is very important in primary care, where decisions can be hard and patients’ health can change fast. Good oversight makes sure AI helps healthcare work better without causing harm.
Different countries have different rules for AI. In the U.S., the focus is on being open, taking responsibility, and following privacy laws like HIPAA. These rules guide how AI companies build and use their products legally and ethically.
Combining a lot of different data is key for AI to work well in primary care. Tom collects billions of data points from hospitals, pharmacies, labs, insurance claims, public information, wearable devices, and continuous glucose monitors (CGMs). This wide data gathering helps the AI create detailed and personal patient profiles.
Using many data sources lets AI get a full picture of a patient’s health, habits, and social factors. For example, when doing a routine follow-up call, Tom might see that a patient has a history of depression and offer a PHQ-9 depression screening. It can also notice if a patient has trouble getting to appointments and help set up rides.
Tom uses up-to-date clinical guidelines built into its processes to make sure any actions it suggests are current and suitable. This data integration happens at many points in the clinical workflow. It helps close gaps in care and keeps patients involved without needing extra work from providers.
AI-driven workflow automation helps make primary care more efficient. Tasks like scheduling, patient reminders, checking insurance, and documentation take a lot of time and clinic resources. AI can take over many of these repetitive jobs, freeing staff for more important work.
For example, AI phone answering and front-office automation tools, like those from companies such as Simbo AI, offer 24/7 answering services. They use machine learning to understand caller needs and direct calls properly. This lowers the need for live operators, shortens wait times, and improves patient experience.
Inside clinics, AI can automatically schedule appointments based on doctor availability and patient needs. This cuts down on missed appointments and helps manage calendars better. AI can also send reminders for check-ups or medicine refills, which supports patient health.
Tom works with electronic health records (EHRs) and clinical support tools like UpToDate. It can access data and guidelines in real time to suggest or begin the next steps in care. AI also helps coordinate follow-ups, manage chronic illnesses, and check on patients after hospital stays. These aids lower hospital readmissions and help with complex care.
For US medical offices and IT teams, using AI for workflow must balance better efficiency with protecting patient data and following rules. Making sure AI works with existing technology, training staff, and reviewing automated tasks are all needed for success.
Using tested AI tools brings clear improvements to primary care work. Less paperwork lets doctors spend more time with patients. AI-driven outreach finds patient needs earlier, helping with prevention and managing long-term illnesses.
By automating standard communication and follow-up tasks, care teams become more efficient while staying connected to patients. This is important as primary care clinics try to handle more patients under value-based care, where quality and outcomes affect payment.
Lumeris reports that AI tools like Tom increase patient capacity. Primary care providers can see more patients without lowering care quality. These platforms adapt in real time to changes in how clinics work, fitting smoothly into busy healthcare settings with little disruption.
Trust is very important for broader use of AI in US healthcare. Healthcare leaders are cautious because wrong AI decisions can lead to harm.
US healthcare rules require clear, consistent, and responsible AI solutions. Leading companies do thorough testing, choose models carefully, explain how decisions are made, and keep humans involved. These steps follow strict data privacy laws and clinical rules.
Efforts to build clear frameworks for AI use let health systems feel safe using AI tools. By watching performance, managing risks with clear safeguards, and keeping humans in control, AI can become a trusted helper rather than an unknown tool.
Tom is a multi-agent AI-enabled primary care platform developed by Lumeris, designed as Primary-Care-as-a-Service (PCaaS) to support primary care physicians, health systems, and risk-bearing organizations in managing clinical and administrative tasks.
Tom addresses the access and capacity gap in primary care, where 100 million Americans lack care, and the system needs approximately 2 billion hours of care versus the existing 500 million available hours, largely due to administrative burdens and limited resources.
Tom automates background tasks such as scheduling, medication adherence follow-ups, post-discharge check-ins, and patient education, thus reducing administrative burden on providers and enabling more patient touchpoints without increasing staff workload.
Tom aggregates billions of clinical and non-clinical data points from health systems, labs, pharmacies, claims data, CMS, HIE data, wearables, continuous glucose monitors, and publicly available consumer data to construct comprehensive patient records.
Tom embeds directly into primary care workflows and IT systems such as EHRs, scheduling interfaces, and clinical resources like UpToDate, allowing seamless real-time data access and action without disrupting provider processes.
Tom leverages agentic AI to autonomously decide and act on the best next action for patients in real time, going beyond recommendations to perform tasks, thereby enabling continuous care management and interaction.
Tom uses data-driven algorithms that consider clinical history, social determinants of health, and up-to-date clinical guidelines to tailor interventions, such as administering a depression screening during unrelated follow-ups or arranging transportation when needed.
Lumeris tested Tom extensively with 260,000 test cases, researched over 60 LLMs, implemented guardrails against clinical hallucinations, and maintains a dedicated team to identify and resolve potential failure modes in clinical scenarios.
Tom expands clinician capacity by handling routine tasks, increases patient engagement through more frequent touchpoints, reduces provider burnout, improves care coordination, and enhances overall patient care experience, facilitating panel expansion.
Tom is currently being deployed with select Lumeris health system customers, with plans for wider expansion to scale primary care access and support value-based care models across the U.S. healthcare system.
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