Adopting natural language processing (NLP) in healthcare is quickly becoming essential, not just a trend. The sophisticated systems decipher complex human language by combining NLP with machine learning (ML), facilitating efficient data handling. Such adaptability makes the tech a valuable tool for various stakeholders in the medical field, from busy healthcare professionals and care providers to insurance companies and even sales and marketing teams.
According to a report by MarketsandMarkets, the global market for NLP in healthcare and life sciences is on a trajectory of exponential growth, projected to reach $7.2 billion by 2027. Meanwhile, Research and Markets reports an even more exciting figure, projecting a market size of $11.5 billion by 2030, growing at a CAGR of 18.5% from 2023 to 2030. This surge in tech usage within the industry is due to a worldwide shift towards digital transformation and the adoption of data-driven methodologies that promote intelligent systems.
Nowadays, major technology firms like IBM, Microsoft, and Amazon are developing innovative NLP solutions. Such tools simplify processes for medical experts and enhance patient care, ensuring their reliability. So, let’s examine the key NLP applications in healthcare to understand how modern medical facilities can leverage the tech more efficiently.
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6 Major NLP Techniques Utilized in Healthcare
NLP algorithms play a crucial role in healthcare. They focus on two essential functions: interpreting unstructured text and creating machine-readable content. These functions are achieved through various techniques, making NLP a great player in the medical industry.
Optical Character Recognition (OCR)
OCR helps recognize and digitize text from various documents, including prescriptions, doctor’s notes, and test results. By extracting text from digital images, presentations, and scans of handwritten or printed notes, OCR is essential for integrating these documents into EHRs.
Tokenization
Tokenization, a process that involves breaking down text into smaller units, such as words or sentences, as tokens, is a key player in the efficiency of NLP models. This process structures the text, making it easier for NLP algorithms to analyze. By identifying patterns and extracting meaningful information, tokenization enhances the efficiency of NLP models as it transforms unstructured text into a structured format.
Named Entity Recognition (NER)
NER is a crucial step in natural language processing, with a tremendous impact on patient care. It identifies critical entities in the text, such as names, dates, locations, and organizations. In the practical application of healthcare, NER can be trained to recognize the names of doctors and patients, diagnoses, and medical terminology. This capability aids in compiling detailed EHRs, enhancing patient treatment and management.
Semantic and Sentiment Analysis
In this stage, NLP solutions perform an in-depth text analysis within its context to clarify meanings and reduce ambiguity. Its crucial component is sentiment analysis, which assesses the emotional tone of the text, classifying it as positive, negative, or neutral. Such analysis is particularly useful for evaluating patient feedback and providing healthcare practices with a more accurate understanding of patient sentiments and experiences.
Topic Modeling
Topic modeling enables NLP applications to uncover semantic patterns and structures in text, automatically grouping similar content into topics. This technique helps quickly identify the document type, such as distinguishing between clinical tests and patient records, accelerating processing and offering additional insights.
Text Classification
Text classification, or tagging, is fundamental for NLP and machine learning. It involves categorizing text data into predefined groups based on patterns identified within the content. This method improves the efficiency of text searches, which is beneficial in healthcare settings.
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The Most Common Areas for NLP Applications in Healthcare
NLP’s advanced text processing and analytical abilities make it highly applicable across various medical sectors:
- Pharma. Pharmaceutical companies can rely on NLP to efficiently sift through clinical notes, extract details from scientific literature, and access patient records. NLP’s role in regulatory compliance is also essential, as it streamlines document processing and ensures all necessary information is present. Also, the tech helps identify discrepancies, reducing the risk of non-compliance.
- Medical Providers. NLP is pivotal in modernizing patient record management in healthcare settings. It automates updating electronic health records, interprets test results, generates reports, and enhances clinical decision-support systems. The NLP importance in healthcare, especially in medical imaging, where it analyzes and annotates images like X-rays to identify abnormalities, is also significant.
- Biotech. NLP is a valuable time-saving tool in biotechnology. It assists researchers in swiftly navigating vast amounts of data in public databases, patent archives, clinical trial registries, and other scientific publications. This automation reduces the time needed for data collection, accelerating the research process.
- Telehealth. In telehealth, you can integrate NLP into chatbots that assist healthcare professionals by conducting initial patient interactions. While these chatbots are not substitutes for in-depth medical consultations, they are effective in preliminary data gathering and record creation. That provides valuable support to medical professionals and facilitates smoother patient management.
Such NLP applications in healthcare underscore the tech’s versatility in increasing the efficiency and accuracy of medical services, benefiting healthcare practices and patients.
Top NLP Use Cases in Healthcare
Natural language processing tech has multiple applications within healthcare, mainly in digitizing vast medical documentation. Clinics and other healthcare facilities also utilize NLP in innovative ways. Here is a closer examination.
Clinical Documents
NLP systems in healthcare free clinicians from the burden of manual EHR management, empowering them to dedicate more time to patient care. Technologies like Nuance and M*Modal employ a mix of team collaboration and speech recognition to capture structured data at the point of care. These systems also utilize standardized vocabularies for ongoing usage.
These NLP tools extract crucial data from speech recognition outputs, enhancing the analytical data supporting value-based care (VBC) and population health management (PHM) initiatives. Looking forward, they will revolutionize care by analyzing vast public datasets and social media to assess social determinants of health (SDOH) and evaluate the efficiency of health policies.
Speech Recognition
Over the years, NLP has refined its impact on speech recognition, enabling doctors and other medical professionals to transcribe notes directly into EHRs effectively. With front-end speech recognition, you can record notes at the point of care without manual typing. At the same time, back-end systems ensure accuracy by identifying and correcting errors before the final human review. While the market offers numerous speech recognition solutions, emerging startups are introducing innovative deep-learning algorithms that enhance these technologies’ capabilities.
Computer-Assisted Coding (CAC)
Computer-assisted coding systems are a prime example of NLP in healthcare. They analyze details of procedures and treatments to capture every applicable billing code, enhancing claims processing accurately.
Despite their benefits, the adoption rate of CAC systems is only about 30%, according to HIT Consultant. Besides, maintaining high accuracy is challenging. This challenge stems from the complexity of medical coding and the need for continuous updates to keep up with changes in healthcare regulations. However, ongoing training and updates to the NLP algorithms can help improve accuracy and increase the adoption rate.
Data Collection Research
Integrating data mining with healthcare technology and big data analytics enables organizations to minimize subjectivity in decision-making, providing valuable medical insights. Data collection initiates a continual cycle of knowledge discovery. That helps medical practices create effective business strategies and deliver superior patient care.
Registry Reports Automation
Another application of NLP is in automated registry reporting, simplifying the extraction and storage of essential data. That is useful in scenarios where information like ejection fractions are documented in notes but not stored as discrete values. For efficient automated reporting, health systems must accurately identify and store each data point in a format accessible for analytics and reporting purposes. Since adopting automated registry reporting is complex, leveraging the expertise of professional NLP services is a must to ensure optimal outcomes from the outset.
Clinical Decision Support (CDS)
NLP is transforming clinical decision support by sharpening the accuracy and efficiency of clinical decisions, mainly in managing medical errors. Recent studies have showcased NLP’s effectiveness in automating the detection of infections. Leading companies such as M*Modal and IBM Watson Health are at the forefront, using NLP to power CDSSs. Isabel Healthcare leverages NLP to assist clinicians in diagnosing and checking symptoms more accurately.
Clinical Trial Matching
In clinical trials, NLP improves patient matching processes, enhancing trial efficiency. This tech automates the identification of eligible participants, optimizing the recruitment phase. Innovators like IBM Watson Health and Inspirata are dedicating substantial resources to employing NLP to facilitate oncology trials. That highlights its potential to increase trial efficiency and accelerate research and development in healthcare.
Prior Authorization
The increasing demands of prior authorization pose challenges, often delaying care and increasing administrative burdens. NLP technologies, such as those developed by IBM Watson and Elevance Health, are used within payer networks to streamline and speed up the prior authorization process. By doing so, NLP can reduce wait times for treatment approvals and financial reimbursement, directly addressing a common issue in healthcare administration.
AI-Based Chatbots and Virtual Assistants
Integrating AI with speech recognition techs can reshape clinical documentation and patient interaction. Prospects for that include adaptations of platforms like Amazon Alexa and Google Assistant to assist in medical settings.
MD’s Smart Exam employs conversational NLP to gather personal health data and align it with clinical guidelines, providing diagnostic suggestions to healthcare practices. Woebot, a virtual therapist, uses conversational AI to deliver therapeutic interactions via Facebook Messenger, successfully reducing users’ symptoms of anxiety and depression.
Risk Adjustments with Hierarchical Condition Category (HCC)
Risk adjustment models like Hierarchical Condition Category (HCC) coding are essential in value-based payment models. Predicting future costs based on patient risk is crucial in these models. Natural language processing aids in refining HCC coding by accurately assigning risk scores and enhancing predictions of healthcare expenditures. This NLP application supports the shift towards more effective and economically sustainable medical practices and the transition to value-based care systems.
Computational Phenotyping
NLP in healthcare is changing the approach to clinical trial matching by enhancing the ability to phenotype patients more accurately. That includes defining phenotypes based on current patient conditions rather than solely on clinician insights.
NLP technologies are also explored for their potential to diagnose neurocognitive disorders like Alzheimer’s and dementia through speech pattern analysis. Companies like BeyondVerbal are collaborating with institutions such as the Mayo Clinic to identify vocal biomarkers for diseases like coronary artery disease. Additionally, Winterlight Labs is investigating how linguistic patterns change in Alzheimer’s patients.
Review Management
NLP tools allow for managing and analyzing online healthcare reviews. They can efficiently process and assess large volumes of daily reviews, detecting Protected Health Information (PHI), profanity, and compliance issues with regulations such as HIPAA. They also excel in interpreting the sentiment and context of the reviews. That helps medical providers understand patient perceptions and improve communication by aligning with their vocabulary.
This tech is critical for capturing the patient’s voice, providing valuable insights into how care is perceived and discussed among patients. Finally, it enhances patient-provider communication.
Dictation and Electronic Medical Records (EMRs)
As electronic medical record systems become increasingly data-intensive, many healthcare providers are transitioning from handwritten to voice-recorded notes. NLP systems can swiftly analyze and integrate these voice notes into EMRs, reducing the time spent on documentation. That allows physicians to focus more on patient care. NLP effectively parses these voice notes and extracts pertinent information from various clinical documents, ensuring comprehensive and up-to-date patient health records.
Root Cause Analysis via Predictive Analytics
NLP is fundamental for predictive analysis, offering solutions to common health issues by mining through extensive digital medical records. It identifies patterns affecting specific demographic groups or geographic regions, uncovering health disparities that traditional databases might overlook. The tech facilitates deep dives into socio-cultural factors affecting health. That provides you with a broader understanding of the underlying causes of patient issues and adverse outcomes, thus enabling targeted interventions.
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NLP in Healthcare Adoption Benefits
The powerful impact of natural language processing continues to evolve, driving advancements across the healthcare sector.
- Enhanced Patient-Provider Interactions. NLP techs bridge the gap between complex medical jargon and patient understanding, easing the strain associated with EHRs. Many healthcare providers are adopting NLP as an efficient alternative to traditional methods of entering medical notes, such as typing or handwriting.
- Boosted Patient Health Literacy. Access to health data through EHR systems does not guarantee patient understanding, which often impedes their ability to make informed health decisions. Integrating NLP with healthcare ML can transform patient engagement by making medical data understandable and actionable.
- Higher Care Quality. NLP tools enhance care quality assessment and improvement, which is crucial for value-based reimbursement models. These tools enable medical practices to assess physician performance accurately and pinpoint care delivery discrepancies. By integrating with advanced analytics, NLP facilitates the development of insights that aid healthcare providers in selecting more effective treatment methods.
- Identified High-risk Patients. NLP algorithms can sift through extensive datasets to highlight essential information, equipping physicians with the tools to address complex health issues effectively.
- Increased Data Processing Efficiency. The application of NLP for processing textual data increases the speed and accuracy of converting text into machine-readable formats. That helps boost decision-making and communication within medical settings.
- Refined Data Analysis. In medical research and clinical trials, NLP improves data extraction quality and supports precise analytics, enabling more profound insights and reliable findings.
- Validated Clinical Decisions. Incorporating NLP tools into the clinical decision-making process gives medical professionals a complex view of patient cases. That allows you to make informed decisions based on objective data and the latest research findings.
Finally, through its precision, accuracy, and rapid processing capabilities, NLP is shaping a new era in healthcare by enhancing diagnostic processes and supporting treatment strategies.
Major Challenges of Using NLP in the Medical Domain
Utilizing NLP in healthcare offers numerous advantages, yet integrating this technology into your practice can present several challenges:
The Industry’s Unique Linguistic Characteristics
Medical professionals often employ a specific language in clinical documentation, characterized by dense medical jargon, acronyms, and abbreviations that may lack clear context. Almost one-third of clinical abbreviations in the Unified Medical Language System Metathesaurus have multiple meanings, adding to the complexity.
Moreover, terms like “discharge” can have many interpretations, such as referring to a patient’s release from the hospital or bodily fluids, depending on the context. When expanding NLP to analyze patient feedback on social media, these systems must adapt to the casual language and emoticons that people use to express emotions.
The successful integration of NLP in healthcare hinges on a deep understanding of the unique linguistic challenges. This understanding is best achieved through the collaborative efforts of professionals and NLP experts, underscoring the importance of each role in this process.
Concerns Associated with Human Language Complexity
Deploying NLP algorithms in healthcare is a complex task. While identifying explicit and clear terms is relatively straightforward, human language often presents complexities that demand sophisticated handling. Here are some common issues:
Inferences
NLP models used in healthcare must be able to infer meanings not explicitly stated in the text. For instance, the phrase “relative at bedside” might suggest social support for a patient, although it is not directly stated in their medical records.
Understanding Semantics
The ability to discern the true meaning behind phrases is among the critical aspects of NLP in healthcare. Simple keyword-based systems may fail to differentiate between the phrases “wife helps patient with medication” and “patient helps wife with medication” despite their vastly diverse implications.
Handling Negation
The accurate interpretation of negations is a critical task for medical NLP algorithms. Medical notes often use negation to indicate the absence of conditions, such as the statement “a brain malignancy was ruled out”. It means no malignant brain tumor was found. Misinterpreting such negations can lead to erroneous conclusions about disease presence.
When deciding on sophisticated NLP algorithms, consider the specific requirements of your healthcare applications. If you choose more basic NLP solutions, be aware of their inherent limitations and ensure they suit your possible use cases.
Biases and Explainability Issues
Like all AI technologies, natural language processing in healthcare is susceptible to biases that can emerge from training on unbalanced data sets or adaptive learning during deployment. For example, a well-known concern is gender bias, where algorithms like GPT-4 can associate men with professions requiring higher education, such as doctors. Meanwhile, women are connected with roles perceived as less academically demanding, like nurses. This potential impact on patient care is a matter of concern for all medical professionals and AI practitioners.
Detecting bias in “black box” AI models, which do not explain outputs, is challenging. However, implementing explainable AI is a promising approach to mitigate this, as it compels NLP tools to rationalize their recommendations, enabling checks for potential biases.
Integrating any AI technology into healthcare is not a straightforward process. Beyond these NLP-specific challenges, some general hurdles often arise, adding to the complexity of the task:
- Integrating with Legacy Systems. Many healthcare institutions use outdated systems incompatible with modern AI techs, complicating integration.
- Lacking Adequate Training Data. To address this issue, healthcare institutions can consider collaborating with other institutions to pool data or invest in data collection efforts. That will help ensure a diverse and representative patient population is included.
- Considering Ethical and Moral Concerns. Deploying AI in healthcare brings ethical dilemmas, such as decision-making authority and accountability for treatment outcomes.
Finally, bias can be identified using specially curated datasets that reveal specific biases. While effective, that is less practical for large-scale applications due to its limited scope.
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How to Implement NLP in Healthcare Effectively?
If you are considering adopting NLP in healthcare, here is a structured approach you can follow:
Identifying Potential Applications
Start by pinpointing the issues you want to address with natural language processing. Assess the need for advanced linguistic capabilities, such as negation handling or inferential analysis, to ensure the technology meets your requirements.
Choosing Between Build or Buy
Decide whether to develop a bespoke NLP system or purchase a pre-existing one. Off-the-shelf NLP tools are suitable for companies with limited budgets or those able to adjust processes to fit the platform. A custom solution may be necessary for entities with fixed procedures, scalability needs, or integration requirements with existing legacy systems. In this case, partnering with a reputable healthcare AI solutions provider is advisable.
Preparing Training Information
Preparing a robust training dataset is crucial whether you choose a pre-made or a custom NLP system. This dataset should be free of biases and accurately represent your target demographic to ensure the model’s effectiveness within the healthcare context.
Training and Validating Your Model
Conduct thorough testing to evaluate the model’s bias, functionality, and integration capabilities with your existing workflows.
Ensuring Regulatory Compliance
Compliance is critical when deploying AI in the medical sector. Ensure your NLP tools adhere to relevant rules, such as those set by the US Food and Drug Administration (FDA) and the General Data Protection Regulation (GDPR). Depending on your geographic location and market, additional compliance may be a must.
Integrating with Current Systems
If you opt for a tailored solution, your technology partner must ensure the NLP system is built to align with your existing infrastructure. They should also assist with seamlessly integrating the new platform into your current setup.
Monitoring and Adjusting Continuously
Implementing NLP in healthcare is a collaborative effort. Continuous monitoring and periodic adjustments are essential to maintain accuracy and relevance. Regular audits will help ensure the models perform as expected and adapt to any changes in application or data.
By following these steps, you can effectively harness the power of NLP to enhance care delivery and patient outcomes.
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Final Thoughts
The potential benefits of implementing NLP in healthcare extend beyond typical applications. Although the medical sector needs further enhancement in data handling capabilities before fully integrating NLP techs, they can improve care delivery and streamline operations. Natural language processing and machine learning tools are crucial for overseeing clinical decision support and clarifying patient health.
With data-driven insights, NLP enables medical professionals to make quick, informed decisions that can save lives while adhering to regulatory, legal, and financial constraints. As AI-based technologies become increasingly prevalent among healthcare providers, effectively using them is becoming more critical. That is where SPsoft’s expertise shines. With our experience, we can equip medical organizations to extract maximum benefits from your real-time and historical data.
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FAQ
NLP is extensively used in healthcare to enhance clinical decision support, streamline electronic health record management, and improve patient interaction by simplifying medical jargon. You can also apply the tech in predictive analytics for clinical trials, managing online reviews, and sentiment analysis. That will help to assess patient feedback efficiently. Besides, NLP facilitates advanced research by extracting meaningful data from unstructured sources and supporting root-cause analyses in healthcare disparities.
