Applications of Natural Language Processing in Healthcare: Improving Patient Literacy and Engagement Through Translation of Complex Medical Terminology

Healthcare communication uses specific and exact words. Words like “myocardial infarction” or “degenerative disc disease” appear in medical records but can be confusing or scary to patients. Many patients find it hard to understand their diagnoses, treatments, and care instructions. This can cause them not to follow their treatment plans and feel less satisfied with their care.

Hospitals and clinics in the U.S. serve many different people, including those who have trouble understanding health information. Studies show that almost 9 out of 10 adults in the U.S. struggle with health information. This makes it very important to have tools that can turn medical language into simpler words without losing meaning.

Natural Language Processing and Large Language Models: Simplifying Medical Language

Natural Language Processing (NLP) is a type of artificial intelligence that helps computers understand and work with human language. Large Language Models (LLMs) are a special part of NLP. They learn from lots of data to predict and create language based on context.

LLMs are good at changing difficult medical terms into easy words. For example, “myocardial infarction” can be changed to “heart attack.” A complex MRI report about the spine can be put in words that anyone can understand. This helps patients feel less worried and makes medical visits easier.

A recent study from May to November 2024 tested how well LLM-based AI helped patients understand spine MRI reports. The study had 102 patients at a U.S. pain and spine clinic. It found that patients understood the reports better when the AI simplified them. Scores for understanding were 8.50 out of 10 with AI help, versus 6.56 without. Clarity and patient involvement also went up.

This study shows two key points for medical office managers and IT staff:

• NLP can give patients clearer information, which helps them feel more satisfied.
• AI results, when checked by healthcare experts like certified neuroradiologists, stay accurate. The study showed that 82.4% of AI-generated reports were of high quality.

However, the study also found some mistakes, so human review is needed to keep communication safe and correct when using AI tools.

Efforts and Innovations Across Healthcare Institutions

Stanford University’s AI for Health program is a key example of NLP made for healthcare. Led by Professor James Zou, this program creates AI that is fair and easy to understand. It aims to make healthcare better and easier for patients.

One main project, ALTE (AI for Literacy, Transparency, and Engagement), uses NLP to turn medical texts into patient-friendly language. This saves doctors time and helps patients understand their health better. The program works with medical experts, computer scientists, and companies to create useful AI tools for healthcare communication.

Companies like John Snow Labs, Cerner, IBM Watson Health, Google Health, and Microsoft Healthcare use similar technologies. They apply NLP and LLMs to:

• Automate patient summaries,
• Help with clinical documentation,
• Support diagnosis,
• Assist with drug research.

Examples include:

• Cerner automates medical coding and creates personalized patient summaries for easier communication.
• IBM Watson Health helps decide cancer treatments by analyzing data and medical reports.
• Google Health builds AI tools that translate medical imaging results to help patients.
• Microsoft Healthcare uses AI for managing population health and genomics with easy-to-understand interfaces.

These advances show how AI and NLP can help medical offices automate and improve clinical information sharing.

AI and Workflow Automation: Streamlining Communication and Front-Office Operations

For healthcare managers and IT staff, AI does more than explain medical words. It can improve front-office tasks like phone calls, scheduling, and messaging. Automated phone systems are becoming very important in busy clinics across the U.S. AI can handle many phone calls, book appointments automatically, and give basic info. This lets staff focus on harder tasks.

Simbo AI is one company that uses NLP to automate phone answering and messaging. Their AI listens to patient questions, schedules appointments, and responds correctly in many cases without a person. These AI helpers understand natural speech, reply quickly, reduce waiting times, and improve patient experience.

When these AI tools connect with electronic health records (EHR) and management systems, they help by:

• Lowering the number of calls front desk staff must answer,
• Cutting down missed appointments with automatic reminders,
• Giving clear AI-made explanations of office rules or simple medical advice,
• Allowing patients to get service anytime, day or night.

Using AI for routine work and standard communication helps clinics run better without adding more staff. This is important because many parts of the U.S. have more patients and fewer healthcare workers.

Addressing Ethical Considerations and Ensuring Quality in AI Applications

Using AI and NLP in healthcare means we must think carefully about safety, privacy, and fairness. AI must provide clear, fair, and easy-to-understand info to all patients, no matter their age, gender, or background.

Stanford’s AI for Health group focuses on:

• Making sure AI treats all patient groups fairly,
• Ensuring AI decisions are explainable so doctors and patients can understand them,
• Having humans check AI results to avoid mistakes,
• Protecting patient data with strong privacy and security rules.

Healthcare IT leaders should consider these points when choosing AI tools or adding NLP to their systems.

Improving Patient Literacy and Healthcare Outcomes

By making medical language simpler and automating communication, NLP-based AI helps patients understand their health better. This makes patients more involved in decisions, more likely to follow treatments, and less stressed.

Key benefits for medical offices include:

• Better patient satisfaction and trust through clearer communication,
• More efficiency by automating simple tasks,
• Improved health results from patient involvement,
• Less work for medical staff,
• Ability to manage more patient communication without needing lots more staff.

Medical practice managers and IT teams in the U.S. need to see that investing in NLP is not just helpful but becoming necessary to meet patient needs and legal requirements for patient education.

The Future of NLP in Healthcare in the United States

As NLP grows, the U.S. healthcare system will get more accurate and personal communication in real time. Future AI models will be able to:

• Translate medical talks live during telemedicine visits,
• Adjust communication for different cultures and languages,
• Automate detailed medical notes to save doctors’ time,
• Support better diagnosis with smart tools,
• Expand AI phone systems to handle more complex questions and multiple languages.

Healthcare leaders who use these tools early will provide better, patient-focused, and clearer care as healthcare changes fast.

Summary

Natural language processing in healthcare is changing how medical information is given to patients in the U.S. It helps people understand medical terms better and engage more in their care. Along with better front-office automation, AI helps clinics run smoother and give better patient experiences. As AI tools get better, ongoing review and human checks will be very important to keep care safe and effective for both patients and providers.