From voice assistants to sentiment analysis, NLP brings new efficiencies and capabilities to business systems. Discover how you use it to enhance customer satisfaction and draw valuable conclusions from unstructured data.
NLP-driven technologies can automatically organize and analyze data in clinical reports to better understand patient outcomes. It also helps to reduce administrative burdens by allowing physicians to focus on patients and their needs.
Detect and Correct Errors in Language
Using NLP, computers can recognize, process, and understand human language. This can be done through voice recognition (such as Siri or Alexa), analyzing written text for sentiment analysis, spam detection, and more.
NLP technology can also detect and correct errors in language. For example, in ecommerce, NLP can help categorize products by tagging them with synonyms and variations of words such as “dress,” “formal dress,” and “black dress.” This helps customers find what they’re looking for faster.
NLP can help healthcare organizations automate tasks and improve patient outcomes. It can assist doctors in efficiently interpreting and organizing their notes, freeing them up to concentrate more on providing high-quality patient care. It can also identify at-risk patients from EHR records, enabling HCOs to meet the demands of value-based reimbursement. It can also help physicians understand their patient’s concerns and needs by understanding their natural language.
As a healthcare organization, knowing who your patients are and their medical needs is essential. This information can be gleaned from various sources, including clinical notes and patient feedback. Fortunately, NLP can process these data sets and extract relevant information.
NLP tools ingest data from various formats, such as structured or unstructured data, and then use tokenization to break it down into smaller parts for analysis. Next, the systems use syntactic and semantic analysis to understand relationships between words and discern nuances in language. Then, Named Entity Recognition (NER) comes into play, identifying key features like disease names and medications.
NLP-powered CDS systems can analyze massive amounts of data and provide evidence-based recommendations to physicians. For instance, a system could identify a patient’s risk of Kawasaki disease and alert clinicians. It can also identify symptom patterns and flag potential drug interactions, improving decision support.
Natural Language Processing in Healthcare
NLP brings computerized systems the ability to understand human language, helping machines to make sense of what’s being said and written. Healthcare NLP applications can help organizations manage patient care, enhance operational efficiency, and accelerate research and development efforts.
EHRs are often laden with free-text notes that contain valuable information but can be challenging to sort through manually. NLP can help transcribe and translate these notes into a more structured format, making searching and sharing data easier.
NLP can automatically identify and match medical terms with the appropriate ICD-10 codes, eliminating manual effort and reducing error rates. This can also improve clinical trial matching and HCC coding, vital in value-based payment models. NLP can also identify PHI content and obfuscate it with semantic tags, protecting patient privacy and aiding compliance with regulatory requirements such as HIPAA. This can be done in real-time and in the background, allowing physicians to focus on patient care.
Clinical Trial Matching
Clinical trial data is scattered across numerous systems and in unstructured narrative form. NLP allows healthcare organizations to extract and analyze this information, helping physicians save time managing the back end of their business and invest more energy in patient care.
NLP can also help with the most pressing issues in clinical research: patient recruitment and enrollment. For example, NLP can interpret participation criteria from plain text and transform them into questions a database can search for.
Additionally, NLP can be used to automate dictation and transcription. This reduces manual effort and error-prone tasks, saving time and resources for healthcare teams. It can also expedite coding procedures, increase reimbursement accuracy, and facilitate regulation compliance. NLP can even help with patient engagement and education through chatbots or virtual health aides.
NLP is at work when you speak to your phone’s virtual assistant or type a search query into your favorite search engine. The technology analyzes a human utterance, determines the intent behind the request, and executes the action.
NLP can transform healthcare processes by automating clinical workflows and facilitating data analysis. It enables clinicians to add detail and specificity to documentation, ensuring the integrity of EHRs, enhancing coding accuracy, and streamlining reimbursement.
NLP can also facilitate research and development by surfacing relevant research papers, clinical trial documentation, and other texts to help identify potential novel treatments. This can speed up the research process and empower researchers to make data-driven decisions. It can also lower administrative costs by eliminating the need for manual data entry and enabling automated regulatory compliance.
NLP is used for sentiment analysis (also known as opinion mining). Written text, such as customer evaluations or comments, recognizes emotional tones, thoughts, and attitudes. It also assists organizations in making data-driven decisions to enhance their offerings.
For example, ecommerce websites can use NLP to analyze customer product reviews and identify positive or negative feedback on specific aspects of a product. This allows the website to provide more personalized recommendations and ultimately increase sales.
Agencies that want to implement NLP should first identify what they need it for. They should then work with a team of software linguists and language engineers to develop the algorithms, applications, and computer programs to help them achieve their goals. They may also need to bring in outside data science expertise if they need in-house capacity. Once NLP is deployed, agencies should continue working with a team of data scientists to ensure the system improves and delivers value.