Many healthcare data sets are unstructured but include valuable information. Using NLP, healthcare workers may unlock the value of unstructured data, especially in EHRs. By mining massive databases, NLP improves decision-making and medical treatment. What natural language processing (NLP) is in healthcare, how it functions, its advantages, and its applications of NLP in healthcare are covered in this article.
By 2025, the global healthcare and life sciences natural language processing (NLP) market will reach $3.7 billion at a CAGR of 20.5%. Healthcare is evolving, requiring cutting-edge technologies like natural language processing.
What is NLP in healthcare?
Natural language processing (NLP) lets computers understand, interpret, and process unstructured human discourse. NLP uses deep learning models, linguistic rules, and machine learning algorithms to identify speech patterns and get insights.
NLP benefits many industries by analysing unstructured data, automating repetitive tasks, and providing real-time insights. NLP has transformed how organisations receive and use information, increasing retail customer experiences and financial decision-making. Technology helps healthcare use unstructured data like patient records and clinical notes.
How Does the Healthcare Industry works?
NLP systems in the medical field handle medical papers by comprehending their meaning in addition to identifying words and characters. For instance, NLP can determine whether a document contains information about more than one patient or even differentiate between a patient’s name and a symptom of an illness.
Following input analysis, NLP formats and extracts data from the medical records using a variety of AI approaches. Information like patient IDs, medicines, and symptoms can be precisely segmented by the system and mapped to the appropriate fields in Electronic Health Record (EHR) systems.
Advanced NLP systems that combine AI and machine learning can become increasingly intelligent and effective at processing complex medical data over time by learning from data patterns.
Benefits of NLP in healthcare
NLP provides many benefits for healthcare businesses. Here are some of the primary Benefits of this technology may offer healthcare businesses.
Better Clinical Records
NLP reduces errors and speeds up documentation by automating the extraction of crucial information from sources like handwritten clinical notes, which account for over 80% of medical data. More trustworthy patient records result from increased data accuracy, which facilitates better decision-making and higher-quality treatment.
Better diagnoses and care
Doctors can focus on patients’ most urgent needs because NLP automates data extraction and entry. By quickly detecting, organising, and formatting medical data from unstructured documents, doctors can prioritise patient needs and improve care.
Good Data Extraction and Analysis
AI-powered NLP algorithms extract and compress crucial information from massive amounts of unstructured medical data, including research publications and clinical trial reports. NLP finds patterns and links in medical datasets to improve decision-making, healthcare innovation, and patient care.
Simplifying Administrative Procedures
Healthcare personnel can relieve repetitive strains by automating repetitive operations like data entry and clinical documentation, which drastically cut down on the amount of time spent on manual labour. Physician burnout is decreased by lighter administrative tasks, which allows them to concentrate more on patient care.
Applications of NLP in Healthcare
Natural language processing’s capacity to decipher and evaluate intricate medical data is revolutionizing the healthcare industry. Let’s look at eight strong application cases that demonstrate how it has affected this industry.
Documentation for Clinical Practice
Clinical documentation is a major NLP use in medicine. NLP can automate time-consuming EHR documentation so clinicians can focus on patient care. Advanced solutions like Nuance and M Modal incorporate team-based capabilities and formalized vocabularies for accurate data capture at the point of care, while speech-to-text dictation and structured data entry improve the speed and efficiency of documentation.
By obtaining important information from speech recognition software, NLP additionally aids in analytical endeavors and enhances the calibre of insights utilized for Population Health Management (PHM) and Value-Based Care (VBC). In order to assess the Social Determinants of Health (SDOH), evaluate wellness initiatives, and spot general health trends, emerging applications of natural language processing (NLP) seek to analyze public databases and social media.
The Ability to Speak
NLP has advanced significantly in speech recognition, enabling physicians to easily transcribe notes for effective EHR data entry. Physicians can dictate notes immediately using front-end speech recognition, which improves mobility and streamlines productivity without requiring a desktop. To provide more accuracy and dependability in the finished documentation, back-end technology automatically finds and fixes transcription problems before forwarding notes for human proofreading.
Startups that use cutting-edge technology like deep learning (OpenAI’s Whisper, for example) are upending the voice recognition industry, improving transcription accuracy, and broadening the range of possible medical uses for speech recognition.
Research of Data Mining
Data mining is necessary to improve healthcare decision-making and reduce subjectivity. Data mining, big data analytics, and healthcare technology can help healthcare companies acquire insights from massive datasets. These revelations offer practical medical information that can greatly improve patient care and facilitate more precise clinical judgements.
NLP-driven data mining’s efficacy, however, is highly dependent on the calibre of the datasets and the algorithms’ resilience, underscoring the significance of clean, organised data for the best results. When done right, data mining turns into an ongoing cycle of knowledge discovery that aids healthcare systems in spotting patterns and trends that spur innovation and ongoing development.
Registry reporting that is automated
One important use of natural language processing (NLP) in the healthcare industry is automated registry reporting, which aids in the extraction of data for regulatory reporting. Reporting metrics like ejection fraction are frequently difficult for health IT systems to handle, particularly when they are not kept as discrete data values. In clinical notes, NLP finds references to the ejection fraction and stores the information in a structured format for use in analytics software.
Because different clinical note formats and terminologies might make automated registry reporting difficult to implement, iterative training of NLP models can simplify the process by efficiently extracting and organising data. Healthcare companies can increase the precision and effectiveness of regulatory reporting by utilising NLP domain-specific expertise.
The Support of Clinical Decisions
Clinical decision support (CDS) systems are being greatly enhanced by advances in natural language processing (NLP), which enables physicians to make more precise and well-informed judgements. Better supervision techniques are still needed in sectors like medical errors, despite the fact that these solutions are growing more sophisticated. Recent research suggests that NLP can diagnose infections, aiding clinical decision-making.
Top suppliers like M*Modal and IBM Watson Health are adding NLP to their CDS systems to improve clinical results and diagnostic accuracy. Isabel Healthcare uses NLP to help doctors diagnose and assess symptoms, improving clinical decision-making. Advanced disambiguation approaches are still needed, nevertheless, for problems like managing confusing terminology in medical situations.
Matching Clinical Trial Participant
Clinical trial matching is being greatly advanced by NLP, making it possible to find trial-eligible patients more quickly. Healthcare systems can find qualified applicants by integrating NLP and machine learning, which is an essential step in furthering medical research.
To improve productivity and decrease manual labour, NLP engines are being developed to automate the trial matching process. Businesses like IBM Watson Health and Inspirata are using natural language processing (NLP) in oncology trials to increase patient matching speed and accuracy. Clinical research results and trial success rates may be further enhanced by extending NLP capabilities to rare illness trials or other under-represented areas.
Chatbots with AI and Virtual Scribes
Although a comprehensive solution has not yet been devised, AI chatbots and virtual assistants are becoming more popular in the healthcare sector. It is anticipated that speech recognition applications will help in editing clinical paperwork; Google Assistant and Alexa from Amazon are excellent examples of such devices. Large corporations like Google and Microsoft are collaborating to achieve this goal, and it is probable that other behemoths like Amazon and IBM will follow suit in creating these technologies.
Chatbots are being utilised more and more in the healthcare industry to expedite patient intake by identifying symptoms and connecting patients with the right practitioner. In addition, advanced virtual assistants employ conversational natural language processing (NLP) to gather personal health information, compare it to evidence-based recommendations, and deliver diagnostic recommendations that support healthcare providers in making well-informed decisions.
Using Computational Phenotyping
Because NLP makes it possible to more accurately phenotype patients for clinical research, it is transforming clinical trial matching. Although phenotyping was previously dependent on the knowledge of medical practitioners, NLP now enables phenotypes to be established using a patient’s current medical conditions as recorded in their medical records.
NLP may also examine speech patterns, providing diagnostic information about neurological diseases including dementia, Alzheimer’s, and other psychological and cardiovascular issues. Collaborations like the one between BeyondVerbal and the Mayo Clinic are attempting to find vocal biomarkers for coronary artery disease, and Winterlight Labs is using linguistic pattern analysis to further the diagnosis of Alzheimer’s disease. But for these methods to be widely used, extensive validation is still necessary.