Leveraging Natural Language Processing (NLP) for Healthcare and Pharmaceutical Companies

Healthcare and pharmaceutical companies are increasingly adopting natural language processing (NLP) systems to make sense of their data. With text analytics, they can extract valuable insights that help them better understand their customers’ needs. Recently NLP has been heavily used in healthcare and pharmaceutical companies thanks to the development of deep learning algorithms that can understand text data and extract valuable information. NLP has become an integral part of many companies’ activities since it helps them automatically process large quantities of unstructured data generated by doctors, researchers and patients themselves every day through electronic health records, clinical notes, whitepapers, or social media posts.  This blog post will explore how you can use natural language processing solutions for your company’s needs: what problems they solve, why you should invest in them, and different ways to implement them with an eye towards future applications.

What are Natural Language Processing Solutions?

Natural language processing, or NLP in short, is a part of artificial intelligence that deals with the interactions between computers and human (natural) languages. NLP applications are used for different purposes, including data mining, document summarization, text classification, or sentiment analysis. The main goal of natural language processing is to program machines that can understand written instructions in a given context just like humans do.

Natural language processing isn’t only about processing written data but also about the interpretation and manipulation of spoken language through video or speech recognition.

For a layperson, NLP solutions may seem like black boxes that can analyze and process text documents in every possible way, as humans do. Still, they are usually very targeted and concentrate on one or more specific text analysis tasks. NLP platforms target particular problem areas and are typically trained for specific domain knowledge.

NLP tasks are generally divided into two separate fields of natural language understanding and natural language generation. Understanding is the process of extracting and analyzing a piece of text data for various purposes, while generation is about creating new natural language texts that can be used for different purposes, such as summarization or conducting a conversation with a virtual agent.

Best Use Cases of Natural Language Processing in Healthcare and Pharmaceutical Industries

Artificial intelligence and NLP have given healthcare companies a powerful tool to analyze the vast amounts of data generated every day. Thanks to NLP, medical professionals can understand customer needs better and deliver the best possible treatments through ongoing treatments, new drug design and development, and many other processes and actions.

There are many applications where these companies can use NLP solutions to gain better insights into their data and improve their business processes. Learning how to use NLP effectively will help you stay ahead in this game! This section will highlight the prominent use cases of natural language processing in the healthcare and pharmaceutical industries.

Processing Electronic Health Records (EHR)

Patient data (PMR – Personal Medical Record) includes multiple data points about a single patient since birth. Much of this data is generated in a traditional, non-digital form, such as doctor notes, printed laboratory results, or diagnostic images. The Electronic Health Record (EHR or EMR) is the digital version of the PMR. Digitization makes the PMR much easier to analyze for healthcare providers and streamlines further usage by machine learning-based solutions. Electronic health records contain a wealth of information about patient treatments and conditions, with new data points added every day. EHR entries hold data that can be invaluable for the healthcare industry and its decision-making processes.

Today, doctors use different applications to track patients’ treatment progress or make new reports on diseases like cancer monitored by medical staff every day. Tons of valuable health information gathered from those documents daily should not go unnoticed. It may help researchers better understand how certain types of cancers behave over time or the most effective methods to improve overall survival rates among patients with similar diagnoses.

NLP helps you automatically process this unstructured content to identify patterns hidden within vast amounts of textual data without any human intervention. You can do it by building a model that will predict treatment outcomes based on the information from previous cases. This is where your NLP platform comes in and helps you implement such models into your workflow and thus ensure better decision-making processes that directly affect patient care.

NLP tools make it possible for clinicians to search and access medical records using full-text search. This helps physicians instantly browse through the medical unstructured text data using free text and questions rather than phrases that match the query in 100%. It has become more common for people to search the web and documents using natural language queries rather than keywords or exact product names. Therefore there’s an increasing need for technologies that allow healthcare organizations to make sense of this type of user input, which means using NLP tools and machine learning algorithms to get valuable insights out of their customer interactions.

Normalizing Clinical Notes

Each clinical note is an example of unstructured data about patient health and treatment applied. Although it is common for physicians to use earlier prepared templates for their patients’ most prevalent diagnosis, the templates still differ from hospital to hospital and one doctor to another. This means that to get a clear picture of a patient’s condition, you will need to compare different clinical notes provided by other doctors. It is tough when researchers need to analyze and extract data from whole populations or clinical trials participants and compare the clinical documentation.

This is where natural language processing comes into play since it can be used for normalizing textual data from these documents and make them comparable. The process includes extracting relevant information using NLP systems, determining how they should be classified according to their context (e.g., symptoms or diagnoses), and finally, saving the results as structured datasets ready for visualization through dashboards or charts which provide quick insights on what kind of conditions are more common among certain age groups or geographical locations within your catchment area, etc.

Healthcare organizations use natural language processing solutions to create text summaries automatically from unstructured clinical notes gathered by medical personnel when conducting daily screenings among patients. NLP system like this one streamlines daily work and helps keep track of the patients’ health.

AI Chatbots and Virtual Assistants

Another area where NLP is used in healthcare and pharmaceutical companies is chatbots that help patients access the immediate support and information they need.

Chatbots are often used to prescreen patients to triage them and reserve further examination with a doctor appropriately. Such systems can work as virtual assistants that guide patients through treatment options or connect them with healthcare professionals who can answer their specific questions. If a patient needs to have an appointment with a physician, AI chatbots can also be used to schedule it. Another example of if a patient is admitted to the emergency room and needs immediate information on what they should do next, hospitals are setting a chatbot that will answer their questions or request medical records for further examination. Last but not least, virtual assistants are another technological solution healthcare, and pharmaceutical companies use when they need to provide support for their customers 24/7.

In the era of ever-increasing mobile apps popularity, we can see a rise in various self-care chatbots solutions available on the market. One of the most popular use cases is applications that support mental health patients and provide them with support in a mental health crisis. These kinds of assistants rely on the conversational interface and another machine learning technology - sentiment analysis. Sentiment analysis helps to uncover how the text is expressed and understand the application user’s actual emotional state.

Related Case StudyAmygdala is a mobile app designed to help people better manage their mental health by translating evidence-based Cognitive Behavioral Therapy to technology-delivered interventions. Amygdala has a friendly, conversational interface that allows people to track their daily emotions and habits and learn and implement concrete coping skills to manage troubling symptoms and emotions better. This AI-based chatbot holds a conversation to determine the user’s current feelings and recommends coping mechanisms. Here you can read more on the design process for Amygdala with the use of AI Design Sprints.

Speech recognition software is another example of NLP technology. Virtual assistants that work on smart speakers or wearables like Apple Watch etc., are another way for patients to get help when they need it most, such as in case of an emergency while they’re at home or on their daily commute. These systems can be used by doctors and patients alike since they provide tracking options and immediate support without going through long queues in hospitals, all based on speech-to-text technology to enable computers to understand voice commands. They are prevalent for patients with lower mobility, elderlies, and those who cannot interact with computers via written text.

Dictation Solutions for Physicians

NLP can also be used to develop natural language dictation and transcription solutions for physicians. Speech recognition enables them to record, transcribe and share their notes with patients and medical personnel more efficiently. Physicians benefit from this type of system as it speeds up the documentation process by feeding an intelligent voice recognition engine that parses physicians’ speech into searchable text format, allowing doctors to find specific information such as medical history or relevant test results faster than they would on their own.

Pharmacovigilance and Mining Literature for Adverse Reactions

One of the most common things that pharma organizations are interested in is pharmacovigilance - a process through which they monitor various reports regarding their medical products or devices. Pharmacovigilance services are nowadays disrupted by different AI solutions that bring many benefits and open up to new possibilities.

NLP tools can be used to mine scientific literature, clinical trial metadata, or social media for specific adverse reactions and efficacy related to drugs on the market. This allows pharmaceutical organizations to improve drug safety by quickly identifying any adverse events and ensuring that these reactions are officially reported to drug regulators and eventually recorded on the product’s label.

NLP can be applied to a broad set of data, such as free text in social media, news articles, literature, or medical records to detect unexpected benefits of a pharmaceutical product. In no time, this method can identify signals indicating advantages and adverse effects using text analytics tasks like named entity recognition for highlighting names of drugs, dosages, patients info, mapping ICD codes and PV terms and relation extraction to mark whether a particular adverse event refers to a specific medication or treatment.  In the case of drug safety monitoring, named entities can be disease terms related to particular drugs or medicines. In contrast, pharmacovigilance systems use drug vocabularies to extract side effect reports with their corresponding drug names and PV relationships between them. Auto-mining of literature and other unusual data sources allows clinicians to add deeper analysis while enhancing patient care and contributing to a pharma company’s top-line revenues.

Once this data has been collected and organized using NLP tools, pharmaceutical companies gain access to enhanced insights about their products, including how specific treatments affect patient bodies depending on age/gender/geographical location, etc., or what diseases/symptoms require medical attention the most.

Clinical Trials Patient Matching

AI is already being used to make clinical trials more efficient, safer, and practical, which helps scientists better understand and target diseases and treatments faster than ever before. Another typical NLP use case in the pharma industry is patient recruitment and matching for clinical trials. The idea behind it is to identify patients who can participate in a particular clinical study and link them with their data, such as medical history or test results. This allows researchers to make patient recruitment more targeted and discover new insights into how various treatments affect people from different demographics.

In addition to patient matching, multi-center clinical trials can also be supported by NLP solutions due to their ability to identify and extract semantic information from text documents, including but not limited to health data, physicians’ notes, lab results, consent forms, questionnaires, etc.

Clinical Decision Support

Clinical decision support (CDS) is all about helping clinicians make better decisions by providing relevant and accurate information. The process of developing a successful CDS solution involves multiple steps: first, the subject matter experts need to build an ontology that maps out different diseases, treatments, and other terms related to the medical field; second, this data needs to be transformed into a machine-readable form so NLP algorithms can understand it; finally, developers should implement various NLP tasks such as text summarization, full-text search or keyword extraction which help doctors find answers faster than they would on their own.

NLP solutions are designed for knowledge representation to structure unstructured data sets like patient records and documents from literature search queries. The process of information extraction is crucial to developing health care applications that can support patient-physician communication and help medical workers focus on what matters most. NLP for healthcare data coupled with predictive analytics, another AI-based technology, can help long-term plan treatment

Marketing Pharmaceuticals

Pharmaceutical companies use natural language processing solutions to perform sentiment analysis on patients’ testimonials, online reviews, or feedback. This helps them monitor the market and identify any negative trends that might affect their revenue models. NLP algorithms can also be used for predictive modeling, which is useful when developing tailored marketing campaigns to increase brand awareness, identify key opinion leaders (KOLs), understand patient journeys through different stages of diagnosis and treatment, etc. Topic modeling, yet another text analysis task, is used in marketing to identify hot topics among customers. These trends can be used to develop new drugs or new administration ways that match current hype (a scorching topic in supplements and other dietary products development).

Similarly, NLP can improve drug efficacy by analyzing patient-reported outcomes on various digital platforms where pharma companies have access to their customer feedback through surveys or apps they provide for patients while being on treatment. This form of data collection enables pharmaceutical companies to understand how well drugs work among different groups of patients, what kind of side effects patients experience more frequently, etc., which then helps them better target their marketing efforts.

Benefits of NLP in Healthcare

The benefits of using natural language processing in healthcare are pretty significant. First of all, it speeds up communication among patients and physicians by better understanding each other. Secondly, NLP helps medical professionals save time on data management tasks while still accessing information instantly, which means they can focus more on their work instead of spending hours looking for the correct files or notes. Thirdly, doctors are also provided with a more detailed insight into their patients’ medical history and current health status, which helps them better understand the nature of the problem, predict its course, and plan the best possible treatment.

Effective Clinical Decision Support

Effective clinical decision support systems can help physicians diagnose and treat patients more efficiently. Thanks to natural language processing tools, doctors are provided with a better understanding of their patient’s medical history which is crucial for determining proper diagnosis and predicting treatment outcomes in some cases. Additionally, an intelligent CDS system will factor in the patient’s medical data alongside the latest research findings and clinical guidelines. This enables physicians to prescribe the most appropriate treatment for their patients, considering all possible medication options and side effects. All this results in faster diagnosis or even preventing potential complications, eventually leading to better patient outcomes.

Process Health Data in a Fast, Effortless Way

A natural language processing solution for healthcare and pharmaceutical companies can be a compelling way to leverage large amounts of health data in a fast, effortless way. Not only it speeds up communication among patients and physicians, but it also helps medical professionals save time on data management tasks while still being able to access information instantly, which means they can focus more on their work.

Improve Patient Experience and Care Quality

Improving patient experience is one of the main goals for healthcare and pharmaceutical companies to attract more patients, retain existing ones and increase revenue. NLP allows them to improve communication between physicians and their patients and provide a better insight into what both parties need via natural language processing solutions that leverage sentiment analysis algorithms.

Newly developed chatbots for organizing medical appointments or answering frequently asked questions about the services provided streamline the reservation processes. First-aid virtual assistants that work 24/7 make healthcare services more accessible and egalitarian. Some patients might feel more comfortable sharing their problems with a chatbot than a trained specialist for mental health issues.

Accurately Analyze Healthcare Data

NLP is also used to analyze healthcare data that can help in daily doctors’ work but also helps companies in reducing time for clinical trials or drug development. NLP use cases in the healthcare and pharmaceutical industries can provide a wealth of information about patient health, diagnose diseases, etc. Analyzing this big data with other ML-based solutions makes it possible to extract and uncover new insights and create new value for the industry.

Increase Patient Safety

NLP can also be used to increase patient safety. Automated literature mining and social media tracking can help in effective adverse reaction reporting. Using natural language processing for early warning systems can detect side effects or drug reactions due to quality assurance issues or the medication itself before they happen large scale.

Challenges of Integrating Healthcare NLP Tools

While there are many benefits to using NLP solutions in the healthcare and pharmaceutical industries, a few challenges must be considered. First, natural language processing tools and any machine learning or analytics solution can bring about some drawbacks, such as loss of privacy and information security issues due to data breaches.

Although the healthcare industry has the highest adoption rates for AI solutions, the statistic applies mainly to industry leaders. Smaller pharma and healthcare organizations might not be ready to incorporate artificial intelligence solutions as they are still stuck on their digital transformation process. Another challenge with deploying NLP in healthcare is that it takes time and money to implement one. It might take up to several months before an end-to-end solution can be launched, even though the initial implementation itself isn’t very complicated and it’s also easy to maintain.

The Future of Intelligent Text Analytics and Processing in Healthcare

As we can see, intelligent text analytics solutions in the healthcare and pharmaceutical industries have an immense potential to transform how companies work for patients, doctors, and researchers alike. They offer numerous benefits, such as improved communication processes that lead to better treatment outcomes with fewer therapeutic errors. The future of NLP lies not only in automating tasks but also in transforming business models allowing companies to become more customer-focused by developing automated technology-enabled services.

In the future, it is expected that NLP solutions will become more widespread as healthcare providers and pharmaceutical companies realize how much time they can save on data management. Also, some barriers, such as lack of knowledge about AI tools, might be broken down thanks to several new initiatives in this area by various stakeholders, including governments and industry leaders.

Join AI Design Sprint workshops focusing on NLP if you want to learn more about how your company could benefit from intelligent document processing and other custom solutions based on NLP!

References

What Is the Role of Natural Language Processing in Healthcare?- Health IT Analytics

What is Pharmacovigilance? - Technology Networks