In the United States, healthcare organizations have several challenges. They face staff shortages, more paperwork, and patient care that is harder to manage. Medical practice leaders and IT managers are looking for tools to make their work easier and improve care for patients. Artificial intelligence (AI) is a technology that can help when added properly to systems like Electronic Health Records (EHRs) and clinical workflows.
This article talks about important points, advantages, and ways to add AI to EHRs and clinical work. It focuses on helping patient management in US medical offices. AI can automate tasks, which lowers paperwork, helps doctors and nurses work better, and supports safer patient care.
AI use in healthcare has grown fast. Almost 90% of healthcare leaders say AI and upgrading EHRs are top goals. AI can do many things like find diseases early, help doctors make decisions, and handle routine tasks. This helps make care cheaper and better.
But using AI well means more than just having the technology. Past problems with EHRs show what can go wrong. Early EHR systems made work harder for doctors and nurses and were slow to be accepted. AI works well only if it fits into the normal work, helps the care team, and does not add more trouble.
Good AI integration helps medical offices get the most out of it. By using lots of patient data and smart computer programs, offices can improve care teamwork, cut mistakes, and use resources better with their current IT systems.
EHRs are the main way patient data is kept in the US. They save many details like medical history, lab results, medicines, and visit notes. But managing all this data by hand takes a lot of time and makes mistakes more likely. This adds stress to clinicians.
AI helps with these problems in several ways:
Studies show nearly 800,000 deaths or serious disabilities each year in the US happen because of diagnosis errors. AI decision support can help lower these mistakes by giving extra checks and finding risks earlier.
Some AI platforms show strong results in improving healthcare work in big systems. These can serve as examples for smaller medical offices that want to grow.
For example, Lumeris’ Tom platform is an AI system made to support primary care in the US, where about 100 million people do not have enough access. Tom gathers billions of data points from health systems, labs, pharmacies, insurance claims, wearable devices, and other sources to build patient profiles. It automatically handles tasks like scheduling, chronic disease follow-ups, medicine checks, and patient education. Tom works all day, every day, fitting into primary care work to reduce paperwork and help providers focus on care.
Microsoft’s Dragon Copilot focuses on nursing work. It uses AI that listens during nurse-patient talks and turns these into notes for the EHR. This cuts the nursing time spent on paperwork, which is usually over 25% of their shift, and helps reduce burnout reported by many US nurses. Dragon Copilot can also add other AI apps for coding and patient interaction, making AI tools work smoothly in the workflow.
Pieces Technologies’ AI platform was chosen by MetroHealth System to improve clinical notes and workflows. This platform creates ready-to-use progress notes, discharge summaries, and other documents that save doctors 40-50 minutes and case managers about an hour each day. It uses a process with human checks to keep notes safe and accurate. MetroHealth also works with Pieces on research about AI tools for cancer patient care.
These platforms show that AI works best when tests back it up, rules are followed, and it fits directly into daily work, rather than working alone.
Medical offices in the US have many choices and steps when picking and adding AI tools:
IT managers should check if vendors have proven results and a history of successful uses in similar healthcare places.
AI-driven automation targets repeated tasks that take up much time for doctors and staff. Automation not only cuts these tasks but helps standardize care. This lets offices see more patients without lowering care quality.
Main areas of automation include:
These tasks can save doctors and nurses hours each week, giving them more time to focus on patients and lowering burnout. For example, MetroHealth says doctors save about 45 minutes daily on notes, and nurses have much less paperwork thanks to AI listening.
Automation also helps with remote patient monitoring and telehealth by using AI tools to keep patients involved between appointments and improve care for long-term illnesses.
Healthcare groups that use AI platforms report better clinical work, safer patient care, and cost savings.
Still, many leaders say their organizations are not fully ready to use AI well. Problems include weak plans and lack of readiness. These problems can be fixed by changing workflows, involving clinicians early in AI design, and testing usability carefully.
The American College of Cardiology (ACC) says that both doctors and patients need a say in how AI tools are made. Designs should meet clinical needs and ensure fair and safe care for all.
Medical practice leaders and IT managers in the US should focus on these when adopting AI:
When chosen and used carefully, AI can help care teams serve more patients and improve patient management. This supports goals to improve access, quality, and value in US healthcare.
By focusing on practical ways to add AI and improve workflows, healthcare leaders can better prepare their offices to handle the growing needs of patient care in a complex system.
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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