Natural Language Understanding (NLU) is a part of natural language processing (NLP) that focuses on understanding the meaning behind spoken or written words. Basic NLP mainly looks at words and sentence structure, but NLU tries to understand the context, purpose, and feelings in what is said or written. This is useful in healthcare because patients often express things with emotions or in ways that are not straightforward.
Healthcare workers get messages from patients through phone calls, telehealth visits, electronic health records (EHRs), online portals, and social media. AI systems using NLU analyze these messages to find the patient’s purpose or feelings like frustration or confusion. Traditional systems might miss those feelings.
By understanding patient requests well and giving replies that fit the situation, NLU makes automated talks with patients smoother and quicker. This helps patients feel better supported and lets healthcare workers focus on cases that need more attention.
Healthcare groups in the U.S. use NLU to improve how they talk with patients. Systems using NLU let patients have natural talks on the phone, chat, or messaging apps without going through tricky menus. This is a change from old systems called interactive voice response (IVR) to smarter agents that handle questions more like people do.
An example is Nuance Patient Engagement Solutions, which handles over 31 billion patient talks every year. Staff at University of Michigan Health-West say this technology helps workers and also gives patients personalized, clear answers.
The benefits of using NLU in patient talks include:
Patients can do tasks like setting or changing appointments, refilling prescriptions, getting telehealth instructions, and using patient portals with little help from staff. These systems work all day and night, giving steady patient support and easing the work for office staff.
NLU also helps talk and text in different languages. This makes it easier to care for patients from many backgrounds in the U.S.
Besides simple tasks, NLU is important for analyzing patient feedback. Many healthcare groups use surveys, but those often miss feelings and hints in messages from calls or social media.
Companies like Authenticx use AI with NLU to study recorded talks between patients and providers. This finds main topics, feelings, and patient moods that show how happy or unhappy patients are. For example, it can catch frustration or sarcasm that normal tools might miss.
This information helps healthcare leaders find problems, improve patient care methods, and build better relationships with patients. Amy Brown, CEO of Authenticx, says turning patient talks into useful reports lets providers “make smart choices that improve patient happiness.”
Real-time analysis like this lets medical offices know patient concerns quickly and change how they work or talk with patients.
A key strength of NLU systems is their ability to connect smoothly with current healthcare IT tools like electronic health records (EHR), customer relationship management (CRM) software, and billing systems. This connection helps automated replies give personal and useful answers based on the patient’s health history.
For example, if a patient asks about a lab result or wants a prescription refill, the NLU system can safely check EHR data and answer quickly and correctly. This stops patients from repeating facts and saves staff from searching records for simple requests.
Healthcare groups using NLU systems report less need for staff to jump in on simple tasks and smoother patient workflows.
Besides improving patient talks, AI and automation can cut down the many paperwork and admin duties medical staff face. Doctors in the U.S. spend almost eight hours a week on tasks not related to patient care like notes, scheduling, billing, and data entry. These tasks cause burnout and reduce time with patients.
AI chatbots and voicebots can take care of many routine jobs automatically. For instance:
Research shows AI and NLP reduce doctor’s paperwork burden, raise quality of care, and improve patient safety. Automating tasks also makes data more correct and lowers staff tiredness.
For administrators and IT managers, these tools can be added specifically to current systems through APIs. This keeps workflows smooth without expensive system upgrades. Rules like HIPAA and GDPR keep patient data safe during these automated tasks.
The healthcare industry in the U.S. is quickly adopting NLP and NLU tools. The U.S. healthcare NLP market is expected to grow from about $1.44 billion in 2024 to $14.7 billion in 2034, growing around 26% each year. Worldwide, the market may rise from $6.7 billion in 2024 to $132 billion in 2034.
This growth is due to how well NLP and NLU help in many areas like:
About 72% of healthcare groups use NLP to automate clinical notes, improving note-taking by 67% and cutting manual data entry by 63%.
Big tech companies like Microsoft, Google Cloud, AWS, and Oracle are spending a lot on healthcare NLP. They work on multilingual clinical notes and voice recognition. Epic Systems and Microsoft launched an NLP-enhanced clinical documentation tool in 2025, helping many hospitals and clinics in the U.S.
The University of Michigan Health-West shows how NLU makes care better. Leaders like Josh Wilda, Chief Digital and Information Officer, say Nuance Patient Engagement Solutions support patients through the whole care process. Their system uses AI for simple tasks and sends tougher questions to human staff. This makes good use of resources and keeps patient experience smooth.
Dr. Lance M. Owens, Chief Medical Information Officer, said that using AI tools lets their organization do more than expected. The tools help care teams and patients and build trust in the community.
Many healthcare groups using NLU and AI automation report better operations, happier patients, and patients who follow treatment plans better because communication is clearer and timely.
For healthcare leaders and IT workers in the U.S., Natural Language Understanding offers useful ways to fix communication, work processes, and patient engagement. It provides:
As healthcare needs grow, practices using NLU and AI automation can handle more patients while keeping good care and patient satisfaction.
By using advances in Natural Language Understanding and AI workflow automation, medical practices in the U.S. can improve patient talks, increase efficiency, and support care teams better in a competitive healthcare system.
Nuance Patient Engagement Solutions utilize NLU-powered AI technology to automate personalized interactions, allowing patients to communicate naturally and efficiently, unlike traditional voicemail systems which often require users to navigate through menus.
AI enhances patient engagement by providing 24/7 access to information, automating routine tasks, and enabling personalized interactions that make patients feel valued and connected to their care providers.
NLU allows AI systems to understand and interpret patients’ natural language, facilitating smoother and more efficient conversations, thereby surpassing the capabilities of basic IVR systems.
By automating interactions and minimizing the need for live agents, AI helps streamline operations, reduces support costs, and optimizes workloads, leading to significant operational savings.
The containment rate refers to the percentage of calls that are handled by the AI without needing to be directed to a live agent, which is cited as 40%.
The technology reduces average handle time (AHT) by streamlining patient interactions and quickly resolving their needs, which has been shown to decrease AHT by 53 seconds.
Automated appointment management enables real-time appointment reminders, confirmations, cancellations, and rescheduling via multiple channels, reducing the care team’s administrative burden and improving patient experience.
Patients can self-serve their prescription refill requests through the AI system, which automates the approval process and provides updates on refill status.
Integrating AI with electronic health records (EHR), CRM, and other systems enriches patient interactions and provides contextually relevant experiences that enhance care management.
AI facilitates proactive outreach through personalized communications, such as care gap reminders and health promotion messages, promoting adherence to care plans and improving patient satisfaction.
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