Electronic health records were made to help improve patient care by keeping health information in one place. But many doctors and nurses find EHRs hard to use. There is a lot of data, different ways of writing notes, and complicated screens. Doctors often spend time looking through long patient histories, test results, and notes instead of seeing patients.
For example, when patients move between hospitals, they bring hundreds of pages of papers. This makes it hard for doctors to find the important information fast. Doctors say they spend too much time searching all parts of the records. This slows down their work and sometimes delays patient care.
These problems affect not only doctors but also nurses, physician assistants, and others who check charts, add notes, and help with care. This can cause stress and extra work for healthcare staff.
AI natural language query tools, like Stanford Medicine’s ChatEHR, change the way doctors use EHRs. Instead of scrolling or searching by hand, doctors can ask questions in simple English. The tool gives quick, clear answers or summaries right away.
For example, a doctor might ask, “What are the patient’s allergies?” or “Summarize recent lab results.” The AI finds and shows the information immediately. These tools use natural language processing (NLP) to understand human words and machine learning models trained on medical data to give correct answers.
These tools are built into current EHR systems. They use safe access methods to get data from medical records while following privacy rules like HIPAA. This means doctors don’t have to learn new software or change how they work.
At Stanford Hospital, 33 clinicians including doctors, nurses, and assistants are testing ChatEHR. It helps reduce the time spent on reviewing charts and paperwork. Doctors said the tool helps them quickly find important patient information like allergies and test results, especially during emergencies or transfers.
Dr. Sneha Jain said the AI lets doctors spend less time looking for information and more time helping patients. Dr. Jonathan Chen noted the benefit of letting AI summarize large amounts of patient data, which helps with fast decisions.
Use of AI tools in healthcare is growing quickly in the United States. A 2025 survey by the American Medical Association said 66% of doctors used AI in their work, up from 38% in 2023. Among them, 68% felt AI helped improve patient care.
The U.S. AI healthcare market was worth $11 billion in 2021. Experts expect it to grow to nearly $187 billion by 2030. This shows strong interest in using AI to lower paperwork and improve care results.
Microsoft’s Dragon Copilot is a voice AI assistant for healthcare. It combines voice dictation and AI automation, saving doctors about five minutes for each patient. This time saving lowers burnout—70% of users said they felt less tired—and helped keep staff, with 62% less likely to leave their jobs.
Hospitals like WellSpan Health and The Ottawa Hospital use Dragon Copilot to simplify work in places like inpatient wards and emergency rooms. These AI tools improve notes, reduce workloads, and help patients have better visits. In fact, 93% of patients said they had better interactions when providers used ambient AI technology.
Besides natural language tools, AI can also automate many routine tasks in clinical and administrative work. Automation powered by AI lowers mistakes, speeds up steps, and frees medical staff to do more important jobs.
In electronic health records, automation can include:
Systems like Cognome’s platform combine both organized and free-form data with tools like SEARCH and Elastex. They give real-time analyses and support clinical decisions without changing providers’ normal work. These platforms connect well with major EHR systems like Epic, lowering manual data work.
In telemedicine, AI helps by recording patient data and notes during video visits. This lowers mistakes and delays, letting doctors spend more time talking with patients.
Even with clear benefits, adding AI natural language query tools and automation into U.S. healthcare is still hard.
Big challenges include:
Government rules, like FDA oversight and responsible AI programs, make sure AI is safe, fair, and accountable. For example, ChatEHR at Stanford follows responsible AI guidelines and offers training and help to clinicians during its pilot to ensure safe and effective use.
Better access to patient data directly affects care quality and safety. AI natural language tools offer:
These improvements not only cut down paperwork but also help patients by allowing more meaningful talks with their doctors. When doctors spend less time finding data, they can give more personal care, helping with diagnosis, treatment plans, and follow ups.
AI tools will keep getting better and offer more functions such as:
In the U.S., with more patients and fewer healthcare workers, using AI to cut paperwork and improve data access can help manage public health better and keep systems running well.
Medical practice leaders should think about how AI automations fit their overall goals. Beyond simple queries, AI in workflows brings clear benefits like:
Products like Microsoft Dragon Copilot use natural language and machine learning to make communication better. For medical managers, this improves call handling, appointment setting, and initial symptom checks, all helping clinic flow.
AI chatbots and virtual helpers can also support mental health by checking symptoms and giving first-level help, making patient triage faster.
To succeed, clinics must have good cooperation between IT and clinical teams, thorough training, strong privacy protection, and ongoing checks of AI tools’ work.
The use of AI natural language query tools and workflow automation in electronic health records shows promise to help doctors work faster and give better access to patient data in U.S. healthcare. Studies and pilot tests show these tools can cut paperwork, speed up decisions, and improve patient care. Facing challenges with care and involving providers will be key to getting the full value of AI in healthcare work.
ChatEHR is an AI software developed by Stanford Medicine that allows clinicians to interact with patient medical records through natural language queries. It helps expedite chart reviews, automatically summarize medical charts, and retrieve specific patient data directly from electronic health records, thereby improving workflow efficiency for healthcare providers.
ChatEHR is integrated directly into the electronic medical record system, allowing clinicians to seamlessly query patient data within their existing workflow. This embedding ensures the AI tool uses medically relevant data securely and efficiently, making it practical and accurate for clinical use without disrupting routine practices.
At present, ChatEHR is accessible to a pilot cohort of 33 clinicians at Stanford Hospital, including physicians, nurses, and physician assistants, who are testing its performance and refining its accuracy. The goal is to eventually expand access to all clinicians who review patient charts, following responsible AI guidelines and providing needed educational resources and support.
ChatEHR can answer specific questions about patient histories (e.g., allergies, lab results), summarize comprehensive patient charts, and support time-sensitive decision-making such as in emergency situations. It can reduce administrative burdens by quickly providing relevant patient information and assisting with tasks like determining transfer eligibility or recommending post-surgical care.
In emergency cases, ChatEHR speeds up the retrieval of comprehensive patient histories, which are critical for diagnosis and treatment. For transferred patients carrying voluminous medical records, ChatEHR summarizes complex histories into relevant insights, easing the provider’s burden and enabling quicker, informed decisions in urgent or complex clinical scenarios.
No, ChatEHR is designed as an information-gathering tool to assist clinicians by organizing and summarizing medical records. All clinical decisions and medical advice remain the responsibility of healthcare professionals. The AI supports but does not replace the expert judgment of clinicians.
Future enhancements include the development of automation tasks that evaluate patient records for specific clinical decisions, such as transfer eligibility or hospice care needs. Additional features under development are accuracy verification methods, including citation tracking that shows clinicians the source data within medical records, to improve transparency and trustworthiness.
ChatEHR’s development follows responsible AI guidelines emphasizing accuracy, performance, security, and clinical relevance. Rollout includes educational resources and technical support to ensure clinicians can use it effectively and safely, minimizing risks associated with AI errors or misuse in sensitive medical contexts.
Researchers at Stanford Medicine, led by data scientists and clinicians such as Nigam Shah and Anurang Revri, developed ChatEHR starting in 2023 by leveraging large language model capabilities. Their goal was to create a clinically useful and secure AI tool embedded in health records to augment physician workflows and improve patient care.
By making electronic health records more user-friendly and accessible through natural language queries, ChatEHR reduces time spent searching for information, allowing clinicians to focus more on patient interactions and clinical decision-making. This leads to more efficient care delivery, less administrative burden, and potentially better patient outcomes.
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