How Natural Language Processing of Unstructured Data is Improving Healthcare Outcomes

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Healthcare generates a vast amount of unstructured data, including clinical notes, patient messages, and research articles. This data contains valuable insights that can significantly improve patient care, but are difficult to include in traditional modeling techniques due to its unstructured format. Natural language processing (NLP) is a subtype of artificial intelligence that is transforming how healthcare systems utilize unstructured data.  

Unstructured Data in Healthcare

 
Clinical data includes a wide range of unstructured sources. These can include:  

• Electronic health records, such as physician notes and discharge summaries, that are recorded in free-text format.
• Radiology or imaging reports, which often includes textual descriptions of imaging written by specialists. This data source can provide crucial diagnostic information that is not elsewhere captured in a structured way.
• Patient feedback, including surveys or messages written to the clinical team.
• Medical research articles, including information on clinical trials or new drug discovery efforts. These additions to the growing body of scientific knowledge can inform treatment decisions, but might be difficult to incorporate into predictive models.

Effective utilization of these data sources is critical to building the best predictive healthcare models and improving patient outcomes.  

Overview of Natural Language Processing

 
Natural language processing (NLP) is a branch of artificial intelligence that seeks to understand and interpret human language. Unstructured text is converted into structured data that can then be further analyzed or models to build predictive models in healthcare.  

There are many common techniques used in NLP. Tokenization is the process by which text is broken down into smaller components, like words or phrases, that the computer can then understand. Specific types of NLP include sentiment analysis, which can determine the emotional tone of text, and classification that can categorize text into predefined groups, such as identifying patients with a disease or who are at high risk.  

Applications of Natural Language Processing in Healthcare

 
There are many specific places where NLP can be applied in healthcare data. A few of these are:

Patient Risk Assessment

NLP can enhance patient risk assessment by automatically extracting clinical information from notes, reports, and discharge summaries. Traditionally, clinicians had to review these documents to determine patient risk, but NLP can do this automatically to identify high-risk individuals in real time. These models can incorporate other aspects of the medical record, such as lab results and medical history, to further refine risk assessment. For example, NLP models can quickly read through all the clinical notes for a patient and identify trends that are recurring or comorbid factors that may indicate increased risk that would otherwise be missed.  

Sentiment Analysis for Patient Feedback

Patient feedback is critical for improving healthcare services, but written reviews or feedback is difficult to properly analyze and synthesize. NLP techniques can easily assess the tone of this feedback to provide healthcare workers with insights into patient satisfaction. NLP can also identify recurring issues to drive decision making and specific areas to focus on to improve patient experiences.  

Population Health Management

NLP can also be vital to identifying trends in population health by extracting data from patient clinical records, public health reports, and news articles. This can be critical during disease outbreaks. NLP was used during the COVID-19 pandemic to track real-time data from medical reports, news outlets, and even social media to identify emerging hotspots and drive public health responses. Improvements to these models can help identify these outbreaks faster and help with resource allocation.  

Ethical Considerations

 
Despite its potential, there are still several challenges and ethical considerations when it comes to applying NLP to healthcare data. There are stringent privacy regulations, including HIPAA, that must be kept in mind. These guidelines are in place to protect highly sensitive medical data and it is critical that any model built using this data also have strong security protections.  

There is also the risk that models developed using NLP will exacerbate existing bias in the healthcare field. A data-driven model will only ever be as good as the data that goes into its development, so if a model is trained predominantly on data from certain demographic groups, or if physicians demonstrate hidden bias within their clinical notes, the final model may perform poorly for minority groups and exacerbate health disparities. Addressing this will require ongoing efforts to improve quality and diversity of training data.  

Future Directions for Natural Language Processing in Healthcare

 
The future of AI and NLP in healthcare is promising. Areas of improvement for NLP specifically include creating larger and more diverse datasets for model development. Increasing access to more personalized data sources like genetic testing or wearables can also improve the predictive power of any AI developed model. Adoption and better understanding of the utility of these models is also an important future step.  

Summary

 
NLP is transforming the field of healthcare by unlocking the potential of previously difficult to analyze unstructured data, including physician notes and patient messages. While challenges like bias and data privacy do exist, the impact of these models on healthcare is expected to grow to further enhance patient care.
 
 

Mehrnaz Siavoshi holds a Masters in Data Analytics and is a full time biostatistician working on complex machine learning development and statistical analysis in healthcare. She has experience with AI and has taught university courses in biostatistics and machine learning at University of the People.