Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that studies the interaction between computers and humans using natural language. NLP aims to allow computers to understand, interpret, and helpfully generate human language.
According to Statista, the global market for electronic health records is on a clear upward trend and is expected to reach nearly US$18 billion by 2026. The demand for NLP in healthcare grows at a similar rate of 18% per year. Global software research and development leaders such as Microsoft, Amazon, and IBM are key players in the medical NLP market, releasing ground-breaking solutions to make doctors' and patients' lives easier.
NLP can help identify patterns in patient symptoms, predict disease outbreaks, and personalize patient care plans, improving operational efficiency and patient outcomes in the healthcare industry.
NLP is changing how people interact with technology and data by enabling machines to understand and respond to our natural language. This article will examine how Natural Language Processing (NLP) is used in the healthcare industry.
NLP in Healthcare:
Natural language processing (NLP) is becoming more widely used in healthcare due to health systems' recognition of its ability to search, analyze, and interpret massive amounts of patient data. Using advanced medical algorithms and machine learning, NLP technology has the potential to extract relevant insights and concepts from clinical notes that the healthcare industry previously thought were buried in text data form. NLP in healthcare can accurately voice the healthcare universe's unstructured data, providing incredible insight into quality understanding, method improvement, and improved patient outcomes.
Physicians spend a lot of time recording the how and why of their patients' experiences in chart notes.
These notes are not easily extractable in ways that allow computers to analyze the data. When the doctor sits down with you and documents your visit in clinical notes, those narratives are entered into electronic health record systems (EHRs) and stored as free text.
Large amounts of unstructured patient data are entered into electronic health records every day, but physicians must assist computers in aggregating that critical data.
Healthcare natural language processing employs specialized engines capable of sifting through large amounts of unstructured data to uncover previously missed or incorrectly coded patient conditions.
Natural language processing of medical records using machine-learned algorithms can identify diseases that have not previously been coded, an important feature for discovering HCC diseases.
Uses of NLP in Healthcare:
1. Electronic Health Records (EHRs)
NLP structures and analyzes unstructured text in EHRs, allowing healthcare providers to extract meaningful data for patient care, research, and decision-making.
2. Drug discovery and development.
Natural language processing (NLP) analyzes scientific literature, clinical trial notes, and other research documents to identify potential drug candidates and therapeutic targets faster than traditional methods.
3. Insurance and Billing
Streamlining insurance claim and billing processes by extracting and analyzing relevant information from unstructured data.
4. Deep Personalization in Communication
Using deep learning, NLP could provide hyper-personalized communication strategies tailored to the vendor profile and the individual representative's preferences and behaviors, thereby improving relationship management.
5. Clinical Documentation
Automating the creation and understanding of clinical documentation saves healthcare providers time and improves patient record accuracy.
6. Automated dispute resolution
By analyzing communication patterns, contract terms, and historical data, NLP systems can propose solutions to disputes and discrepancies between healthcare facilities and their suppliers and vendors, reducing the need for lengthy negotiations and legal intervention.
7. Automated dispute resolution
By analyzing communication patterns, contract terms, and historical data, NLP systems can propose solutions to disputes and discrepancies between healthcare facilities and their suppliers and vendors, reducing the need for lengthy negotiations and legal intervention.
8. Medical research and literature analysis
NLP is used to sift through vast amounts of medical literature and research data, aiding in the discovery of new medical insights and synthesizing existing knowledge.
9. Personalized Communication
NLP can tailor communication to specific suppliers' and vendors' histories and preferences, resulting in stronger relationships and more successful negotiations.
10. Social determinants of health (SDOH)
Analyzing unstructured data from various sources to gain insights into the social determinants of health affecting patient populations to inform more comprehensive and effective intervention strategies.
Popular areas for implementing NLP in Healthcare:
NLP's outstanding text processing and analysis capabilities make it suitable for use in a variety of healthcare areas:
Pharmaceuticals. Pharma companies can use NLP capabilities to process clinical notes and reports, extract text from scientific literature, and retrieve data from patient records. Furthermore, NLP can assist in ensuring regulatory compliance by automating document processing and identifying potential deviations.
Healthcare providers. The introduction of medical NLP algorithms simplifies the management of patient records. NLP tools can update electronic health records (EHRs), parse test results, generate reports, and improve clinical decision support systems. Another common application for NLP is medical imaging, in which language processing algorithms analyze and describe images (such as X-rays) to identify anomalies.
Biotechnology. In this field of medicine, NLP is primarily used for text mining. Algorithms enable researchers to quickly locate information in public databases, patents, clinical trial records, and other scientific literature. Automating such tasks significantly accelerates data collection, reducing companies' time required to conduct research.
Telemedicine. NLP can be a chatbot that helps doctors treat patients. While such a chatbot should not be a substitute for a proper medical consultation, it can handle preliminary questions, data collection, and record creation.
NLP Techniques For Healthcare
NLP algorithms have two primary functions: parsing incoming unstructured text and creating machine-readable content. They use various techniques to achieve these goals.
1. Optical Character Recognition(OCR)
OCR, or text recognition, is when a computer "reads" handwritten or printed text and converts it into a digital format, such as scanning a physical document and converting it to a PDF. OCR is also used to scan unstructured data sets like images or text files, extract text and tables, and present it in an understandable format.
Once formatted, the data can be fed into an NLP pipeline for further analysis. OCR is widely used in the healthcare industry to digitize clinical notes, medical history records, patient intake forms, discharge summaries, medical tests, and other documents.
2. Tokenization
When parsing natural language, an NLP application divides text into smaller units (words or sentences) that can be processed separately. These units, known as tokens, define the structure of the text for use by NLP algorithms.
Tokenization enables NLP applications to identify patterns and extract useful information from input text. Introducing structure to previously unstructured text increases the efficiency of NLP models.
3. Named Entity Recognition (NER)
NER is an information extraction technique that divides named entities (real-world subjects like a person, location, organization, or product) into predetermined categories. NER is also called entity chunking, entity extraction, or entity identification. We'll look at some healthcare-specific NER applications further down the page.
4. Semantic and sentiment analysis.
At this point, NLP solutions investigate text units within the context to refine their meaning and reduce ambiguity.
Sentiment analysis identifies and categorizes a text's attitude (sentiment) as positive, negative, or neutral. This is especially useful for parsing patient reviews and experiences, allowing healthcare workers to assess patients' emotions and opinions better.
5. Topic modeling
Topic modeling is a type of statistical modeling and natural language processing (NLP) that classifies collections of documents by grouping them based on common words or phrases to identify semantic structures or "topics." The most common type of topic modeling, latent Dirichlet allocation, employs algorithms to identify semantic relationships between various words and phrases and group them accordingly.
6. Text classification
Text classification, or tagging, is crucial in natural language processing and machine learning. It involves categorizing text data into groups. Classification algorithms assign elements to specific groups based on textual patterns.
Classification helps to accelerate and streamline text search, which is an important benefit for healthcare applications.
How Can NLP Support the Healthcare Industry?
Though there are no limits to how NLP can help the healthcare industry, let's look at three main use cases:
Improving Clinical Documentation: Rather than wasting valuable time manually reviewing complex EHR, NLP extracts critical data from EHR at the point of care through speech-to-text dictation and formulated data entry. This allows physicians to focus on providing patients with the required care while ensuring that clinical documentation is accurate and current.
Accelerating Clinical Trial Matching: Using NLP, healthcare providers can automatically review massive amounts of unstructured clinical and patient data to identify potential clinical trial participants. This gives patients access to experimental care that could dramatically improve their condition and lives and supports innovation in the medical field.
Supporting Clinical Decisions: NLP enables physicians to quickly, easily, and efficiently access health-related information when needed, allowing them to make more informed decisions at the point of care.
