The Global NLP in Healthcare and Life Sciences market is expected to grow at a stunning 20.3% CAGR between 2022-2028, reaching the US $6.8 billion figure by 2028: ReportLinker
Understanding the Role of NLP in Healthcare
As the challenges to the systemic functioning of the health sector continue to intensify, artificial intelligence (AI) is on the rise. And it has become quite imperative to get a better understanding of the role of AI in healthcare.
IT is undergoing rapid changes (deep learning, Big Data, Internet of Things) presently. And AI solutions are driving healthcare transformation by easing the workload of exhausted physicians, simplifying administrative tasks, and accelerating clinical decisions and diagnoses.
The medical field is seeing new opportunities emerge (secondary use of health data, precision medicine, drug discovery) and challenges (chronic diseases, aging of the population), and the interactions between these fields are more relevant than ever.
The biomedical field and the discipline of natural language processing have been interacting for more than half a century for mutual benefit. NLP techniques have contributed to medical knowledge discovery and improved clinical practice. And the medical field has been an essential source of interesting use cases.
For instance, extensive document collections (like MIMIC for patient records and MEDLINE for scientific summaries) and detailed lexical resources (UMLS) have contributed significantly to progress in the discipline of NLP.
What is NLP in Healthcare?
Natural language processing (NLP) is a subfield of artificial intelligence devoted to understanding and creating language. Recent advances in NLP technologies allow rapid analysis of large amounts of text, creating opportunities for health research and evidence-based decision-making.
The amount of healthcare data is growing by leaps and bounds, but many healthcare systems are taking full advantage of this abundant data. Since much of this data is unstructured (i.e., vital signs, charges, follow-up appointments, encounters, and symptoms, among others), analysts are not able to extract and analyze much of this information.
Unstructured data usually exists as text and is more complex and larger than structured data. However, natural language processing uses artificial intelligence to exploit much of this unstructured data. Using NLP technology, analysts can extract and analyze this unstructured data to gain meaningful insights.
Natural Language Processing (NLP) is an artificial intelligence technology that enables computers to understand human language. This technology aims to empower machines to read, decipher, understand and make sense of human language.
Traditionally, doctors spend a lot of time understanding how their patients feel and noting all the details in the medical records. Every time patients come in, they look up the chart notes and reanalyze them.
But when they have NLP and EHRs (Electronic Health Records) at their disposal, they can maintain all the recorded data in computerized form. All this data is converted to free-text format, which can be used conveniently, quickly, and repeatedly.
Benefits of NLP in Healthcare
Many data science service providers use NLP to process unstructured data from various sources such as literature, social media, and EHRs, and then make that data available to analytics systems for interpretation.
It is estimated that 80% of health data exists in the form of unstructured text, and NLP, although still evolving, can be used to harness the potential of this valuable source of data.
EHR adoption is proliferating across the healthcare industry, and the amount of health data recorded and stored is also growing rapidly. Although analysts have the ability to use this large amount of data, it is difficult because most of it is unstructured, i.e. in text form.
Nonetheless, NLP promises to unlock the potential of this data by using artificial intelligence to extract useful data from EHRs. Speech recognition capabilities in EHRs are also on the horizon.
Once the text is transformed into structured data by a top natural language processing company like RevolveAI, healthcare systems can gain insights, classify patients, summarize information, and more.
There are four main areas where a data science services provider can improve health functions. These are predictive analytics, EHR usability, quality improvement, and phenotyping.
Let’s look at a few major benefits of NLP in healthcare.
1. Automating Administration
Tedious administration tasks strain your staff and eat into your margins. Fortunately, it is possible to automate up to 40% of the jobs of support teams and 33% of the functions of healthcare professionals.
Automation using AI-powered solutions improves efficiency and frees your teams to spend more time on higher-value tasks. Self-scheduling and natural language processing solutions help you save time and alleviate frustration for your teams and patients while reducing operating costs and generating comfortable margins.
2. Implementing Predictive Analytics Through NLP
One of the exciting areas where NLP can be used is in improving significant population health problems through predictive analytics. For example, recent reports suggest that the suicide rate in the United States has recently increased. As such, healthcare professionals have devoted significant time and resources to understanding who is at risk so they can be treated appropriately.
Researchers did a study to predict suicide attempts using NLP by monitoring social media. The results clearly showed that there were specific indicators of suicide attempts. These ranged from users posting fewer emojis in the text to limiting the use of emojis to increasing angry or sad posts before attempting suicide. The system had a whopping 70% accuracy rate.
3. Improving the Usability of EHR Data with NLP
Typically, EHRs record patient information at every encounter, making it much more difficult to find critical details like social history (a robust indicator of readmissions). However, a data science services provider, particularly a natural language processing company, can create an EHR user interface that makes it easier to find important patient information.
The user interface can be divided into sections containing words related to patient concerns described during the encounters. The interface can then fill the rest of the page with information related to the particular word or phrase. For example, all fever records could be displayed at the top of the page.
This type of interface makes it easier for clinicians to find hidden data and make a diagnosis based on better and more comprehensive information.
4. Clinical Entity Resolver with NLP
Healthcare professionals can use NLP to extract information about various diagnoses and conditions from medical records. They can then be assigned an ICD-10 clinical modification code.
The ICD-10-CM is an invaluable resource in helping physicians make better decisions. It is possible by matching diagnoses and symptoms to the relevant ICD-10-CM codes. Some typical health conditions identified with NLP are gestational diabetes mellitus or HTG-induced pancreatitis.
5. Increasing Phenotypic Skills with NLP
A phenotype can be described as an observable biochemical or physical expression of a particular trait. Phenotyping allows clinicians to categorize or group patients to get a deeper and more focused view of the data.
For example, physicians can group patients who share specific similar characteristics. Currently, most phenotyping work is done on structured data because extracting and analyzing insights from it is easier.
However, NLP allows analysts to extract and analyze interesting elements from unstructured data, which may also include information about follow-up appointments, encounters, charges, orders, and symptoms.
Best Use Cases of NLP in Healthcare
Listed below are some of the best use cases of NLP in healthcare.
1. Clinical Documentation
NLP’s clinical documentation helps clinicians eliminate bulky physical EHR systems and allows them to invest more time in the patient. Experts see both formulated data entry and speech-to-text dictation as a great relief.
Nuance and M*Modal comprise functionalities that involve working on team and voice recognition technologies to obtain structured data in a nursing home and formalized glossaries to be used in the future.
NLP technologies generate relevant data from voice recognition devices that significantly alter the analytical data used to undergo VBC and PMH efforts. Clinicians get better outcomes from this.
In times ahead, we will use NLP tools on various public datasets and social media to conclude the Social Determinants of Health” (SDOH) and the efficacy of wellness-based policies.
2. Data Mining Research
Integrating data mining into health systems allows organizations to reduce the subjectivity of the decision and provide valuable medical knowledge. Once launched, data mining can become a cyclical knowledge discovery technology that can help any healthcare professional build a sound business strategy to provide better patient care.
3. Speech Recognition
Over the years, NLP has advanced in the area of speech recognition by allowing physicians to transcribe notes for user input of EHR data. Front-end voice recognition eliminates the task of doctors taking notes instead of focusing on the problem.
At the same time, background technology works to detect and correct any transcription errors before transmitting them to human-supervised proofing.
The market is almost saturated with voice recognition technologies. Still, many startups are making a mark with innovative deep learning algorithms in mining applications to further the role of AI, particularly NLP in healthcare.
4. Computer-Assisted Coding (CAC)
The CAC records data from procedures and treatments to understand and optimize claims handling. It is one of the most popular uses of NLP. But unfortunately, its acceptance rate is only 30%.
You also need to acknowledge that it has done wonders in speeding up the coding, but not so much in accuracy. This can also be a possible reason for its slow adoption rate.
5. Automated Registry Reporting
Another NLP use case in healthcare is the assignment of values as required for each use case. Many health information systems get under the burden of regulatory reporting, not storing measures like ejection fraction as discrete values.
Automated reporting requires health systems to identify when an ejection fraction is documented as part of a note. In doing so, they also need to keep each value in a form that the automated reporting registry analysis platform can use.
6. Clinical Decision Support
The presence of NLP in healthcare will increase support for clinical decisions. Certain processes require better monitoring strategies, such as medical errors.
A research report reveals that recent research has demonstrated the usefulness of NLP for automatic infection detection. Some prominent vendors are using M*Modal and IBM Watson Health for NLS-powered CDS. Additionally, NLP is easing clinicians in symptom checking and diagnosis using Isabel Healthcare.
7. AI Chatbots
Chatbots or virtual private assistants exist in various forms and areas in today’s digital world, and the healthcare industry is also one of their beneficiaries. Currently, these caregivers can detect symptoms and refer patients to the most appropriate healthcare provider.
8. Clinical Trial Matching
Using intelligent machines and NLP to identify patients for clinical trials can be a critical NLP in healthcare use cases. Some companies try to solve problems in this area by using the natural language process in medical testing machines. With the latest developments, NLP can automate sample matching and make the process hassle-free.
IBM Watson Health and Inspiration have dedicated vast resources to using NLP in healthcare, extending substantial support to cancer research.
9. Computational Phenotyping
NLP is even bettering clinical trial matching in several ways. How about assisting physicians with the complexity of phenotyping patients for examination? For example, NLP will allow the definition of phenotypes based on the patient’s current conditions rather than the knowledge of experts.
10. Sentiment Analysis and Review Management
With innovative sentiment analysis solutions, NLP can help healthcare organizations manage online assessments. You can collect and evaluate thousands of health reviews on third-party listings daily. In addition, NLP identifies PHI or proprietary health information, oaths, or other data related to HIPPA compliance. It can also quickly examine human emotions in the context of their use.
Some systems may even track the patient’s voice in reviews. It helps doctors know how patients talk about their care, assisting them in better formulating a common vocabulary. Similarly, NLP can track patients’ attitudes to understand both positive and negative concepts in evaluation.
11. Root Cause Analysis
Another exciting advantage of NLP is how predictive analytics can provide solutions to common health problems. Applied to NLP, extensive databases of digital health records can help identify subgroups of geographic regions, racial groups, or other sectors of different populations facing different types of health inequalities.
The current administrative database fails to analyze the sociocultural effects of health on such a large scale, but the NLP offers ample space for further research.
Similarly, NLP systems can also help assess unstructured responses and identify the root cause of patient problems.
Top NLP Companies in Healthcare
The application of natural language processing (NLP) can substantially improve all healthcare domains, from diagnostics to treatment. It can support healthcare personnel with various tasks, from administrative workflow to clinical documentation and patient outreach.
That is one of the reasons we see a significant increase in the number of NLP companies in healthcare sector. Some most prominent of them include:
1. Google Deep Mind – Revenue [1.13 Billion USD]
DeepMind Technologies is a UK subsidiary of Alphabet, Inc. with research centers in Canada, France and the United States and is based in London. DeepMind has developed General Artificial Intelligence (AGI) technology that uses raw pixel data as input, learning and improving the outcomes with experience.
DeepMind has created a neural network that learns video games like people. They also have a Neural Turing machine to their credit, which can leverage external memory, creating a computer that mimics the human brain in developing a short-term memory on that input.
2. IBM Watson Health – Revenue [1 Billion USD]
IBM Watson Health is a digital tool for building intelligent health ecosystems with cloud, data, analytics, and artificial intelligence solutions. It helps facilitate healthcare solutions and clinical research using artificial intelligence, cloud computing, and other advanced IT solutions. In 2022, it announced it would sell Watson Heath to the private equity firm Francisco Partners.
3. Babylon Health – Revenue [322.9 Million USD]
Babylon Health is known for combining an artificial intelligence platform with virtual clinical operations for patients. They offer a web and mobile app to assist patients in connecting with healthcare professionals. The company serves more than 20 million people worldwide and provides more than 5,000 consultations per day.
In addition to Babylon Health’s direct health services, users can access various health monitoring tools, such as activity monitoring and ordering blood test kits at home.
In 2021, the company became the leading general practitioner in the UK, with over 100,000 patients and reported a 371% increase in sales compared to the previous year.
4. Komodo Health, Inc. – Revenue [94 Million USD]
Komodo Health, Inc. was founded in 2014 and combines a comprehensive overview of patient interactions and data with innovative algorithms. Their software solution brings transparency and market intelligence to health and life sciences.
Komodo Health provides a comprehensive health insight platform designed to reduce the global disease burden through a functional model. In 2021, the company received $ 220 million in a new round of funding to build a data analysis platform.
5. Corti – Revenue [18 Million USD]
Headquartered in Denmark, Corti lends a digital assistant that improves patient outcomes and internal performance. Corti provides healthcare professionals with an artificial intelligence platform that efficiently analyzes patient interviews to optimize decision-making.
The company has pioneered a new category of AI and machine learning (ML) to improve and accelerate patient consultation.
6. CloudMedX Health – Revenue [US$4.1 Million]
CloudMedX develops predictive care models for healthcare providers, patients, and insurance companies. It is based in the US. The company collects and translates diverse and unstructured health data into a holistic and usable picture of each patient.
Final Word
Natural language processing creates extraordinary opportunities to improve evidence-based decision-making in public health. We anticipate that broader applications of NLP will lead to the creation of more efficient systems that can identify diseases and risk conditions in real-time.
Similarly, with the ability to analyze and synthesize large volumes of information almost instantaneously, NLP is to facilitate targeted health promotion and disease prevention activities. All this will potentially lead to global disease reduction and greater health equity worldwide.
If you also have an idea that you believe can contribute to improving the global healthcare sector, get in touch with RevolveAI and see it evolve into something actionable and profitable.
