Doctors in busy clinics get a lot of messages from patients. A study by UC San Diego Health says doctors get about 200 patient messages each week. This number has gone up because more people use electronic health records (EHR) and patient portals to send messages directly to their doctors.
A study published in the Journal of the American Medical Association’s Network Open looks at how AI helps write responses to patient messages inside the Epic Systems EHR platform. The study found that AI can make longer and more caring replies. Doctors then edit and personalize these replies before sending them. Although AI does not always cut down the time it takes to answer messages, it helps reduce mental tiredness. AI gives doctors good drafts so they can focus their mental energy on harder patient problems while still keeping communication good.
Christopher Longhurst, MD, who led the study, says a lot of patient messages add to doctor burnout. Using AI to help write message drafts lowers the mental load. This is important to keep doctors healthy and maintain good patient care over time.
Marlene Millen, MD, who also worked on the study, notes that AI drafts stay consistent and caring even after long clinical days. This helps doctors give thoughtful answers without getting worse because of tiredness. The messages tell patients that AI helped draft the reply. This keeps trust and clarity between doctors and patients.
These results show that AI supports better patient communication. It helps doctors have deeper talks with patients without getting overwhelmed. This may change how healthcare talks happen on a larger scale.
AI has moved from simple machine learning (ML) to more complex deep learning (DL), making it more useful in healthcare. It helps in communication, diagnosis, and managing workflows.
Deep learning can handle large and mixed data better than earlier AI tools. For example, it can look at medical images and big sets of patient records to give helpful information for clinical decisions. Deep learning also powers chatbots that understand and create patient messages using natural language processing (NLP).
A 2024 review in Current Research in Biotechnology explains this growth and its meaning. The authors point out that AI tools, like chatbots similar to ChatGPT, help interpret complex health information and communicate with patients and staff. Moving from ML to DL is still hard for many healthcare places because of technical and setup challenges, but many clinical leaders see the possible benefits.
For medical office managers in the U.S., it is important to learn about AI progress—from rule-based to learning-based systems. These tools affect how patients talk with doctors digitally and reduce work by managing large clinical databases in health records and information systems.
Natural Language Processing (NLP) is a part of AI that helps computers understand human language. This is useful in healthcare for reading doctor notes, converting speech to text, and picking out important details from messy data.
AI systems like IBM’s Watson, which started healthcare work in 2011, helped bring NLP into clinics. NLP can find treatments, risk factors, and disease patterns automatically. This helps doctors make better diagnoses and create personalized care plans.
NLP supports both office and clinical tasks today. AI chatbots use NLP to help patients with questions, monitor if they take medicines, and give support at any time. This raises patient involvement and shares communication work that would normally burden office and clinic staff.
NLP also helps predict how diseases might get worse by studying patient histories and current health details. This allows doctors to act early, lowering risks and helping patients stay healthier longer.
Still, there are challenges in fully using NLP and other AI tools. Privacy issues with patient health data need strict rules like HIPAA. Providers must use strong encryption, control who accesses data, and check security often to keep data safe.
AI is helping automate office tasks in many medical clinics. Tasks like scheduling, registering patients, checking insurance, billing, and answering calls can take a lot of time.
Simbo AI is a company that uses AI to handle phone calls and front desk work automatically. This helps clinic workers focus more on patient care instead of office work. AI can remind patients about appointments, reschedule, refill prescriptions, and answer basic questions. This makes clinics work better and patients happier.
Because many patients want quick answers, automation cuts down waiting times and common delays in reaching healthcare providers. For clinic managers, tools like Simbo AI save money and use resources better, which is important in a competitive healthcare system.
AI systems can work well with electronic health records and office software. Automated calls and messages update patient records right away, making data more accurate and reducing mistakes compared to typing it in by hand.
This mix of AI communication help and office automation gives healthcare providers a good way to handle more patient needs and complex work without adding more staff or reducing care quality.
One big problem in healthcare is doctor burnout. Too many office tasks and more patient messages are main causes. Studies from UC San Diego Health show AI can help lessen burnout, not by cutting time spent, but by lowering mental workload.
AI creates draft replies that are clear and caring. This helps doctors get past writer’s block during busy times and keeps communication steady during the day. Having a detailed draft from AI lets doctors focus more on the medical parts of their answers rather than typing every message from scratch.
Experts like Dr. Ming Tai-Seale see AI as a way to reduce mental tiredness for doctors. By helping with messages, AI supports doctors’ health and helps keep a stable working environment.
Even though AI has benefits, many doctors are cautious about using it for healthcare communication. Surveys show about 83% of U.S. doctors think AI will help providers in time, but near 70% worry about AI’s role in diagnosis and patient data safety.
Building trust means being open about how AI works. For example, messages drafted by AI often say so. This honesty helps patients and doctors know that a real doctor checked the final message.
Ethical concerns include privacy, informed consent, bias in AI, and accuracy of AI-made content. AI works with large amounts of protected health information like speech and texts, so following HIPAA and strong data protections is very important. Clinics using AI for speech or messages must use good encryption, control access, and do regular security checks.
Healthcare groups need to watch AI tools all the time, handle bias in AI models, and train doctors on both the limits and benefits of AI. These actions keep ethics high and avoid mistakes that could harm patient trust or safety.
In the future, AI will become more common in healthcare communication and office work. Market studies say the AI healthcare field may grow from $11 billion in 2021 to about $187 billion by 2030. This growth shows ongoing investments and use of AI in clinics.
One challenge is that big hospitals often have more AI tools than small community clinics. Experts like Mark Sendak, MD, MPP, at HIMSS25 say it is important to close this gap. Bringing more AI tools to smaller clinics will help improve patient care across the U.S.
Future AI models might provide real-time data analysis and decision help. These tools can improve communication and clinical work. Early disease detection through AI may help doctors give better personalized care.
Clinic managers and owners should get ready by investing in AI tools, training staff, checking that vendors meet rules, and slowly adding AI in ways that fit their workflows.
The study focuses on the use of generative AI to draft compassionate replies to patient messages within Epic Systems electronic health records, aiming to enhance physician-patient communication.
The study found that while AI-generated replies did not reduce physician response time, they did lower the cognitive burden on doctors by providing empathetic drafts that physicians could edit.
The senior author is Christopher Longhurst, MD, who is also the executive director of the Joan and Irwin Jacobs Center for Health Innovation.
It evaluated the quality of communication and the cognitive load on physicians, suggesting that AI can help mitigate burnout by facilitating more thoughtful responses.
AI is seen as a collaborative tool because it assists physicians by generating drafts that incorporate empathy, allowing doctors to respond more effectively to patient queries.
The COVID-19 pandemic led to an unprecedented rise in digital communications between patients and providers, creating a demand for timely responses which many physicians struggle to meet.
Generative AI helps by drafting longer, empathetic responses to patient messages, which can enhance the quality of communication while reducing the initial writing workload for physicians.
A greater response length typically indicates better quality of communication, as physicians can provide more comprehensive and empathetic replies to patients.
The study suggests a potential paradigm shift in healthcare communication, highlighting the need for further analysis on how AI-generated empathy impacts patient satisfaction.
UC San Diego Health, alongside the Jacobs Center for Health Innovation, is testing generative AI models to explore safe and effective applications in healthcare since May 2023.
Simbo is using AI agents to automate all phone related workflows. Connect now to know more.
Schedule AI Agents Demo Now© Since 2019, Simbo AI.
