NLP in Healthcare: Benefits, Applications & Implementation

1 week ago

Canadian healthcare facilities struggle with many challenges throughout the day, including staff shortages and high patient inflow.

In this chaos, they forget the most important thing— optimal utilization of patient data. 

During the treatment of the patient, there are tons of data stored into the hospital system. 

A big chunk of this—around 80%—is unstructured text which is somewhere lost in electronic health records (EHRs).

This data has some valuable insights and amazing predictions.

But It’s challenging to dig through them with standard methods.

This is where Natural Language Processing (NLP) steps in.

NLP is a branch of AI, which uses its computational intelligence to pull insights from unstructured text and give valuable information. 

NLP in healthcare leads to better patient care, improved health outcomes, and more efficient healthcare systems overall.

Let’s Understand the Difference Between NLP, NLU, and NLG in Healthcare

1. Natural Language Processing (NLP)

Teaching a human language to a computer is not easy. 

To make it understandable, computer science, artificial intelligence, and linguistics are combined. This combination is called NLP. 

In healthcare, NLP can be used to process various types of unstructured data, like:

  • Doctor’s notes
  • Medical records
  • Research articles
  • Patient conversations

NLP uses various techniques to analyze text and pull out important information. 

A common technique in healthcare NLP is named entity recognition, which identifies key terms like names, places, dates, and medical conditions. 

This helps computers to organize and analyze clinical data.

2. Natural Language Understanding (NLU)

NLU is the extended process of NLP.

It helps computers to understand the deeper meaning behind text.

  • Apart from reading the words, NLU analyzes the syntax (grammar) and semantics (meaning) of sentences. 
  • Recognizes context in medical phrases (e.g., understanding that “relative at bedside” means a patient has support, even if the word “support” isn’t mentioned)
  • Uses ontologies, which define relationships between words and concepts, for better comprehension of healthcare-specific language

NLU provides context to the data so that computers can interpret text like humans do.

3. Natural Language Generation (NLG)

It is also a part of NLP. It helps the computer to generate human-like text from the data, commonly used in:

  • Generating summaries of complex data
  • Creating patient reports or explanations in easy-to-understand language
  • Text-to-speech applications

NLG allows healthcare data to be turned into clear, human-like text.

Healthcare NLP: Benefits, Applications, and Real-World Use Cases

1. Benefits of NLP in Healthcare

Unlocking Value from Unstructured Data

In any healthcare facility, most of the data is unstructured but it contains some valuable insights that NLM can offer. 

For example, it can identify patients who are at risk for readmission by analyzing discharge notes and highlighting key information.

Improving Efficiency and Accuracy

NLP can automate different tasks, including medical coding and billing. 

It saves the staff time, cost and also reduces errors.

Enhancing Patient Care

NLP looks into the patient's history and current condition to give a clear view to the provider. 

This helps the doctor to give a personalized treatment. 

Chatbots, another application of NLP give easy information access to patients in a few clicks.

2. Applications of NLP in Healthcare

Streamlining Clinical Documentation

There are already many NLP applications which are available in the market. 

For example, Dragon Medical One lets doctors dictate their notes, which are automatically transcribed. It saves time, improves accuracy & reduces errors.

Supporting Clinical Decision-Making

Have you heard about a popular NLP tool from IBM Watson for Oncology? 

It's an amazing tool. It analyzes data from patient records to suggest evidence-based treatment options. This helps doctors to make an informed decision.

Predictive Analytics

If you can predict the disease earlier, you can treat it better. And it's now possible because of NLP. 

It can analyze the patient data and flag those who are at risk of readmission or developing chronic conditions like diabetes. An early intervention can help them.

3. Use Cases of NLP in Healthcare

Extracting Insights from EHRs

NLP is like a detective for all the messy, unstructured data in Electronic Health Records (EHRs). 

It digs through heart failure stats, COVID-19 risks, and even severe pregnancy complications, finding clues that might have been hidden. 

So, doctors get the vital info they need without the hunting and pecking!

Patient Interaction and Support

Ever talked to a chatbot about your symptoms? Well, NLP is what makes that possible! 

Mount Sinai uses it to power symptom checkers that give patients instant info about what's going on with their health. 

No waiting for a doctor's appointment — just quick, clear answers at your fingertips.

Critical Care and Personalized Information

In the ICU, communication can be chaotic. But NLP is there, smoothing out the info flow and making sure everyone’s on the same page. 

Plus, it’s like a personal assistant for postpartum moms, offering support and advice tailored just for them. Talk about personalized care!

Implementing NLP in Your Healthcare System: A Practical Guide

Give your medical facility a superpower by implementing NLP. Here's how you can do it.

1. Set Clear Goals

First, you need to find out the problems your organization is facing and how NLP can solve them. Ask a few questions to yourself.

  • What’s slowing us down? Data management? Clinical workflows?
  • Are there manual processes we could automate or fix?
  • How can we use unstructured data (like clinical notes) to make better decisions?

Useful NLP applications that can help you: 

  • Automating Quality Measures: Track quality indicators for conditions like heart failure automatically.
  • Bias Detection: Identify biases in algorithms, especially for things like opioid misuse.
  • Improving Risk Stratification: Spot high-risk patients early for timely interventions.
  • Clinical Trial Recruitment: Find suitable patients for trials by analyzing eligibility criteria.
  • Fraud Detection: Spot fraudulent claims by analyzing medical data.

2. Assess Your Data

Keep your data stored. 

Identify what kind of data you have? What format is it in? What’s the quality like? 

You might have to clean, standardize, and de-identify data before putting it into NLP analysis.

3. Choose the Right Tech

This is the backbone of your system, make sure you pick the right NLP. Consider the below things:

  • Scalability: Can the tool grow with your needs?
  • Accuracy: Does it perform the tasks you need with precision?
  • Integration: Can it work with your existing systems like EHRs?
  • Support: Does the vendor offer solid help during setup and beyond?

Options include

  • Open-source vs Commercial: Open-source is flexible to use but you need to have technical knowledge. Commercial tools come with more features and support.
  • Cloud vs On-premises: Cloud-based tools are easy to use and cost-effective but on-premises give you more control.

To have a clear understanding of the tech stack, you must connect with a healthcare-specific IT team.

4. Train Your Models

Once you've selected the tech, it’s time to train your models. 

Choose between basic tasks (like text classification) and advanced tasks (like machine learning with models like BERT and GPT). Annotate your data, train your model, and keep refining it.

Healthcare-specific NLP techniques will help you extract valuable insights from medical texts — like identifying diseases, medications, and interactions.

5. Integrate and Deploy

Finally, bring everything together. Ensure seamless data flow between your new NLP tool and existing systems. 

Make it easy for your team to access and use the insights. Address any challenges like:

  • Legacy System Compatibility: Make sure older systems work well with your new tools.
  • Data Security: Protect patient info during all stages.
  • Training: Help your team understand how to use the tool effectively.

Keep monitoring the performance once the tool is live. Track key metrics, gather feedback from users, and keep improving the system.

Challenges and Key Considerations for Implementing NLP in Healthcare

Before implementing NLP in healthcare, you should consider a few important things to consider, including fairness, privacy, security, and the ethical use of AI. Let's explore it.

1. Bias and Fairness

Make sure the data you put into NLP, should not be biased otherwise, it can unintentionally perpetuate health disparities. 

For example, if historical data shows Black patients receiving less care as compared to other races, NLP models might continue that trend. This is a major issue in healthcare AI.

To avoid this, we need:

  • Diverse Datasets: During the training NLP model, keep the diversity among patients.
  • Explainable AI (XAI): This helps us understand how NLP systems make decisions. So we can spot and fix any bias.

2. Privacy and Security

Healthcare data is sensitive, make sure you protect it according to PIPEDA or HIPAA regulations. 

If you're in Canada, the rules may vary according to the province.

Keep strong security measured. You can also take the help of compliance experts who have years of experience in local regulations in healthcare project development.

3. Change Management

Introducing NLP often changes how staff work. To make the transition smooth:

  • Train your staff on how to use the system.
  • Focus on the benefits of NLP. Let staff know how it can help save time, reduce workload, and improve accuracy.

Now you can implement NLP in a way that it's ethical, secure, and beneficial for both healthcare professionals and patients.

References