Natural language processing in healthcare
Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) that enables machines to understand and communicate in natural language, like humans do. It involves using algorithms and techniques such as machine learning, deep learning and text analytics to interpret and analyze natural language content from audio recordings, documents, images or other sources.
NLP technologies can be used for a variety of computer science applications including automatic summarization, question answering, text classification, named entity recognition, sentiment analysis and more. By understanding the structure and meaning of human language, powerful insights can be derived from large volumes of formulated data entry. This has enabled businesses to gain better access to customer feedback and drive their decision making with greater accuracy. NLP is reshaping how people interact with technology and data capabilities by allowing machines to understand our natural language and respond accordingly. With NLP tools, businesses can create more accurate analysis and increase accuracy of customer insights. This is why it is becoming an increasingly important tool for data science and companies across industries.
NLP in healthcare
The adoption of natural language processing in healthcare is rising because of its recognized potential by health systems to search, analyze and interpret mammoth amounts of patient datasets. Using advanced medical algorithms and machine learning in healthcare, NLP technology has the potential to harness relevant insights and concepts from clinical notes that was previously considered by the healthcare industry as buried in text data form. NLP in healthcare can accurately give voice to the unstructured data of the healthcare universe, giving incredible insight into understanding quality, improving methods, and better results for patients.
Physicians spend a lot of time inputting the how and the why of what’s happening to their patients into chart notes. These notes aren’t easily extractable in ways the data can be analyzed by a computer. When the doctor sits down with you, and documents your visit in clinical notes, those narratives go into the electronic health record systems (EHRs) and get stored as free text data.
Huge volumes of unstructured patient data is inputted into electronic health records on a daily basis by the healthcare industry, but it’s hard for a computer to help physicians aggregate that critical data. Structured data like claims or CCDAs / FHIR APIs may help determine disease burden, but gives us a limited view of the actual patient record. Big data analytics in healthcare shows that up to 80 percent of healthcare documentation is unstructured data, and therefore goes largely unutilized by health systems, since the data science of mining and extraction of this information is challenging and resource intensive. Without natural language processing in healthcare, that unstructured data is not in a usable format for modern computer-based algorithms to extract and use beneficially.
Healthcare natural language processing uses specialized engines capable of scrubbing large sets of unstructured data to discover previously missed or improperly coded patient conditions. Natural language processing medical records using machine-learned algorithms can uncover disease that may not have been previously coded, a key feature for making HCC disease discoveries.
In the healthcare industry, electronic health records and physicians don't always get along well. The additional data input responsibilities create challenges, and can be frustrating. Researchers analyzing health systems often conclude, physicians suffer from EHR burnout and threaten to retire from service early rather than suffer through the many clicks and screens required to navigate their EHR. Medical NLP is steadily proving to be a solution to this challenge since NLP healthcare tools can easily access and accurately interpret clinical documentation like clinical notes. Once the friction of healthcare technology is reduced, we can begin to appreciate more of the benefits of the technology and less of the daily frustrations.
NLP in healthcare is not a one-size-fits-all solution to all medical challenges. It is just one piece of the puzzle when it comes to providing high quality patient care. However, it can be incredibly useful in helping healthcare professionals make faster, more accurate decisions that ultimately benefit patients. With medical NLP’s ability to quickly and accurately uncover hidden patterns in medical reports, healthcare organizations are poised to have improved predictive analytics and gain new insights into diseases or health conditions that may have otherwise gone undiscovered. By understanding these insights, providers and the healthcare industry as a whole will be able to develop better treatments and diagnostic methods for their patients – leading to improved outcomes and quality of life for those affected by disease or illness.
The accuracy of medical natural language processing goes up along with the volume of clinical documentation available for learning. The more a medical NLP platform is used, the more accurate using Artificial Intelligence in healthcare gets, since it’s always learning, and in some cases, can be customizable. Some NLP healthcare systems offered by vendors advertise the ability to screen how the medical natural language processing would initially perform with a specific medical group. Then customize it to the needs of that particular medical group.
A distinct advantage natural language processing medical records offers is the ability for computer assisted coding to synthesize the content of long chart notes into just the important points. Historically, this could take organizations weeks, months, even years, to manually review and process stacks of chart notes from health records, just to identify the pertinent info. Natural language processing software for healthcare can scan clinical text data within seconds and using machine learning models, identify what needs to be extracted. This frees up physicians and staff resources to focus more on the complex matters and reduces the time spent on redundant administrative policy. When computers can understand physician notation accurately and process that healthcare data accordingly, valuable decision support can be obtained. These insights can be of significant use for future drug research and personalized medicine, which is good for patients and providers.
With NLP, healthcare professionals can now quickly identify trends in patient data to inform personalized care plans and better understand clinical outcomes. By using advanced algorithms and NLP techniques to process unstructured data faster than ever before, NLP systems enable healthcare providers to manage patient data more accurately while providing superior services for their patients.
For example, NLP software for healthcare can extract key insights from clinical documentation—such as medication dosage or risk factors that may be relevant to a particular patient’s health—to provide targeted treatments or therapies with greater accuracy than manual processing of such data.
In addition, NLP in healthcare is capable of recognizing the context within which words are used, allowing it to more accurately interpret patient conversations and capture the subtle nuances of a person’s health condition. This helps healthcare professionals manage data about treatments and follow-up procedures. As NLP in healthcare continues to evolve and become increasingly sophisticated, healthcare organizations will benefit from its ability to further refine their patient data management processes. In doing so, they can ensure greater accuracy in the care provided for their patients.
Who is using NLP in healthcare?
Medical NLP is used in the healthcare industry by a wide range of healthcare professionals, including physicians, nurses, pharmacists, and administrators. It helps them streamline workflows, improve predictive analytics and reduce administrative burden. In particular, NLP in healthcare can help medical practitioners save time by automatically extracting the data they need within medical records. This allows them to more quickly identify important information that would otherwise take manual effort to locate. Additionally, natural language processing in healthcare has been used to automate the summarization of clinical notes for improved decision-making support and data mining for population health insights. As a result of its many applications in healthcare settings, the NLP system has become an essential part of clinical workflow optimization and clinical trial matching efforts across the sector.
More broadly, NLP systems are being applied across a multitude of fields beyond healthcare as well. Businesses are finding creative ways to leverage the technology for customer service automation and sentiment analysis while researchers use it to extract valuable insights from large datasets such as social media conversations or academic papers.
Medical notation and NLP
Medical notation is an important skill for medical professionals to master. It allows them to effectively communicate vital health data and maintain accurate records, ensuring that patients receive the best possible care. However, it’s important to remember that physicians within healthcare organizations don’t all “speak the same way”, and should always be aware that their notes and reports will likely be read by their work peers, patients and even computers, according to their organizations privacy policy. Avoiding non-standard language in note creation and management is extremely important.
Most natural language processing healthcare engines are built to accommodate a wide variation of medical notation terminology. However, using uncommon acronyms can confuse NLP coding algorithms and other medical note readers.
NLP negation
NLP negation in healthcare is a process which helps clinicians identify the absence of certain medical conditions or symptoms. It works by recognizing negated words and phrases, such as “not present” or “unlikely” to determine if a patient does not have a condition or symptom. This is accomplished through either a negative rule-based approach that uses predetermined logical indicators to detect whether something has been negated, or through supervised learning algorithms that are trained on narrative clinical text data that contain negations.
In medical notation, a clinical concept may be documented in a variety of ways. Sometimes the concept is documented but associated with a negation term, in those cases it’s not part of the patient's chart. Medical natural language processing must be able to support a diverse set of clinical terms, sometimes all for the same root clinical concept, and all forms of negation. Here’s some examples of a range of phrases that a provider may utilize for the concept breast cancer, and how some trigger words can negate the concept.
Through proper application of NLP negation in healthcare settings, physicians can detect whether treatments are likely to be successful and which medications may be most effective.
Also important to note is that research continues into how NLP negation can be used to detect complex interactions between multiple medical conditions or symptoms and treatments. As more studies are conducted, it is anticipated that the effectiveness of NLP negation in health data will continue to grow, ultimately leading to improved patient outcomes.
Improving NLP capabilities
Efforts to improve natural language processing healthcare data have proven challenging. If the NLP system output displays too many suggested conclusions in the electronic health record, or artificial conclusions that are incorrect, users will learn to ignore the intelligence and end up with a system that can reduce overall business productivity. NLP software for healthcare should center around data conclusions that have the least noise, and the strongest signal about what healthcare providers need to do.
Healthcare organizations can take a few steps to improve the capabilities of their Artificial Intelligence and NLP systems. First, they should focus on developing robust data sets for training models. By ensuring that the training data is comprehensive and accurate, healthcare professionals can create models that are better equipped to handle everyday situations. Secondly, healthcare systems should strive to gain a better understanding of their target audience’s language use by surveying patients and medical staff. This allows them to design more intuitive systems that better accommodate the way people communicate in the health care setting.
Moving forward
Healthcare natural language processing offers the chance for computers to do the things that computers need to do. To do the analytics, the HCC risk adjustment coding, the back office functions, and process unstructured data into structured data, all without obstructing physician communication.
In the future, we can expect medical NLP tools to become an even more integral part of healthcare organizations, serving as an intelligent assistant for healthcare providers that can quickly process large volumes of text-based unstructured data to provide informed decisions and improve predictive analytics. As technology advances and patterns across different areas in medicine are further explored using NLP techniques, the possibilities for leveraging NLP systems will only continue to grow.
Although there is still much work to be done before natural language processing in healthcare reaches its full potential, its current applications in healthcare have already demonstrated its immense potential to improve patient care and streamline healthcare systems. Ultimately, NLP in healthcare will be a crucial component of our health systems moving forward.
ForeSee Medical’s unique combination of machine learning technology and risk adjustment rules delivers industry leading NLP accuracy scores. See how using natural language processing technology can help you capture all appropriate HCC categories and get the Medicare reimbursements you deserve.