Original article was published by Alarsh Tiwari on Becoming Human: Artificial Intelligence Magazine
NLP in Healthcare? Sure, It’s a thing!
What is NLP?
Natural Language Processing, or NLP for short, is broadly defined as the automatic manipulation of natural language, like speech and text, by software.
Or in rather simple terms, we may divide the term into Natural Language and Processing.
Natural language refers to the way we, humans, communicate with each other.
Namely, speech and text.
We are surrounded by text.
Think about how much text you see each day:
- Web Pages
- and so much more…
The list is endless.
And Processing, well in layman terms, one might consider that it refers to operate on the given natural language to understand, interpret, change or express it in the same or a different form.
NLP is important in computer applications because it is a go to approach for making the algorithms ‘understand’ the context behind sentences.
As Yoav Goldberg said in his book, Neural Network Methods in Natural Language Processing:-
Natural language processing (NLP) is a collective term referring to automatic computational processing of human languages. This includes both algorithms that take human-produced text as input, and algorithms that produce natural looking text as outputs.
Where is NLP around us?
We use the term natural language processing (NLP) to refer to the field that aims to enable computers to parse human language as humans do. NLP is not a single technique; rather, it is composed of many techniques grouped together by this common aim. Two examples of NLP at an individual level are International Business Machine’s Watson™ and Apple’s Siri®. For example, Watson used NLP to convert each question on Jeopardy! into a series of queries that it could ask its databases simultaneously. Siri uses NLP to translate speech into commands to navigate the iPhone® or search the Internet.
Other examples could be chatbots that we encounter in websites. They use APIs that are programmed to ‘understand’ the context of any particular query and answer accordingly.
Where does NLP fit in anyway in the Healthcare industry?
The healthcare industry is fast realizing the importance of data, collecting information from EHRs(Electronic Health Records), sensors, and other sources. However, the struggle to make sense of the data collected in the process might rage on for years. Since the healthcare system has started adopting cutting-edge technologies, there is a vast amount of data collected in silos. Healthcare organizations want to digitize processes, but not unnecessarily disrupt established clinical workflows. Therefore, we now have as much as 80 percent of data unstructured and of poor quality. This brings us to a pertinent challenge of data extraction and utilization in the healthcare space through NLP in Healthcare.
This data as it is today, and given the amount of time and effort it would need for humans to read and reformat it, is unusable. Thus, we cannot yet make effective decisions in healthcare through analytics because of the form our data is in. Therefore, there is a higher need to leverage this unstructured data as we shift from fee-for-service healthcare model to value-based care.
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This is where Natural Language Processing, NLP comes in. NLP based chatbots, already possess the capabilities of well and truly mimicking human behavior and executing a myriad of tasks. When it comes to implementing the same on a much larger use case, like a hospital — it can be used to parse information and extract critical strings of data, thereby offering an opportunity for us to leverage unstructured data.
Driving Factors behind NLP in Healthcare.
- Handle the Surge in Clinical Data
The increased use of patient health record systems and the digital transformation of medicine has led to a spike in the volume of data available with healthcare organizations. The need to make sense out of this data and draw credible insights happens to be a major driver.
- Support Value-Based Care and Population Health Management
The shift in business models and outcome expectations is driving the need for better use of unstructured data. Traditional health information systems have been focusing on deriving value from the 20 percent of healthcare data that comes in structured formats through clinical channels.
NLP in Healthcare could solve these challenges through a number of use cases. Let’s explore a couple of them:
- Improving Clinical Documentation — Electronic Health Record(EHR) solutions often have a complex structure, so that documenting data in them is a hassle. With speech-to-text dictation, data can be automatically captured at the point of care, freeing up physicians from the tedious task of documenting care delivery.
- Making CAC more Efficient — Computer-assisted coding can be improved in so many ways with NLP. CAC extracts information about procedures to capture codes and maximize claims. This can truly help HCOs make the shift from fee-for-service to a value-based model, thereby improving the patient experience significantly.
- Improve Patient-Provider Interactions with EHR(Electronic Health Record (EHR)
Patients in this day and age need undivided attention from their healthcare providers. This leaves doctors feeling overwhelmed and burned out as they have to offer personalized services while also managing burdensome documentation including billing services.
Already, virtual assistants such as Siri, Cortana and Alexa have made it into healthcare organizations, working as administrative aids, helping with customer service tasks and help desk responsibilities.
Soon, NLP in Healthcare might make virtual assistants cross over to the clinical side of the healthcare industry as ordering assistants or medical scribes.
- Empower Patients with Health Literacy
With conversational AI already being a success within the healthcare space,a key use-case and benefit of implementing this technology is the ability to help patients understand their symptoms and gain more knowledge about their conditions. By becoming more aware of their health conditions, patients can make informed decisions, and keep their health on track by interacting with an intelligent chatbot.
Natural Language Processing in healthcare could boost patients’ understanding of EHR portals, opening up opportunities to make them more aware of their health.
- Address the Need for Higher Quality of Healthcare
NLP can be the front-runner in assessing and improving the quality of healthcare by measuring physician performance and identifying gaps in care delivery.
Research has shown that artificial intelligence in healthcare can ease the process of physician assessment and automate patient diagnosis, reducing the time and human effort needed in carrying out routine tasks such as patient diagnosis. NLP in healthcare can also identify and mitigate potential errors in care delivery.
- Identify Patients who Need Improved Care
Machine Learning and NLP tools have the capabilities needed to detect patients with complex health conditions who have a history of mental health or substance abuse and need improved care. Factors such as food insecurity and housing instability can deter the treatment protocols, thereby compelling these patients to incur more cost in their lifetime.
Since the healthcare industry generates both structured and unstructured data, it is crucial for healthcare organizations to refine both before implementing NLP in healthcare.
Some specific uses for NLP in Healthcare.
- Summarizing lengthy blocks of narrative text, such as a clinical note or academic journal by identifying key concepts or phrases present in the source material.
- Mapping data elements present in unstructured text to structured fields in an electronic health record in order to improve clinical data integrity.
- Converting data in the other direction from machine-readable formats into natural language for reporting and educational purposes.
- Answering unique free-text queries that require the synthesis of multiple data sources.
- Engaging in optical character recognition to turn images, like PDF documents or scans of care summaries and imaging reports, into text files that can then be parsed and analyzed.
- Conducting speech recognition to allow users to dictate clinical notes or other information that can then be turned into text.
Challenges yet? Sure sir!
Sources of Data for Text Mining.
Patient health records, order entries, and physician notes aren’t the only sources of data in healthcare. There are other sources as well such as:-
1. The Internet of Things (think FitBit data)
2. Electronic Medical Records/Electronic Health Records (classic)
3. Insurance Providers (claims from private and government payers)
4. Other Clinical Data (including computerized physician order entries, physician notes, medical imaging records, and more)
5. Opt-In Genome and Research Registries
6. Social Media (tweets, Facebook comments, etc.)
7. Web Knowledge (emergency care data, news feeds, and medical journals)
Just how much health data is there from these sources? More than 2,314 exabytes by 2020, says BIS Research. But adding to the ocean of healthcare data doesn’t do much if you’re not actually using it. And many may agree that utilization of this data is… underwhelming.
Improving Customer Care While Reducing Medical Information Department Costs.
Every physician knows how annoying it can be to get a drug-maker to give them a straight, clear answer. Many patients know it, too. For the rest of us, here’s how it works:
- You (a physician, patient or media person) call into a biotechnology or pharmaceutical company’s Medical Information Department (MID)
- Your call is routed to the MID contact center
- MID operators reference all available documentation to provide an answer, or punt your question to a full clinician
Hearing How People Really Talk About and Experience, ADHD.
The human brain is terribly complicated, and two people may experience the same condition in vastly different ways. This is especially true of conditions like Attention Deficit Hyperactivity Disorder (ADHD). In order to optimize treatment, physicians need to understand exactly how their individual patients experience it. But people often tell their doctor one thing, and then turn around and tell their friends and family something else entirely. Adevanced text analytics using NLP techniques are surely helping healthcare providers connect with their patients and develop personalized treatment plans.
Guiding Communications Between Pharmaceutical Companies and Patients.
Pharmaceutical marketing teams face countless challenges. These include growing market share, demonstrating product value, increasing patient adherence and improving buy-in from healthcare professionals. Previously, companies relied on basic customer surveys and some other quantitative data sources to create their recommendations. Now with the aid of NLP, companies are trying to categorize large quantities of qualitative, unstructured patient comments into “thematic maps.”
Broad classification of some top current NLP techniques in health care.
1. Mainstay NLP healthcare use cases, or those with a proven return on investment:
- Speech recognition
- Clinical documentation improvement
- Data mining research
- Computer-assisted coding
- Automated registry reporting
2. Emerging NLP healthcare use cases, or those that will likely have immediate impact:
- Clinical trial matching
- Prior authorization
- Clinical decision support
- Risk adjustment and hierarchical condition categories
3. Next-generation NLP healthcare use cases, or those that are on the horizon:
- Ambient virtual scribe
- Computational phenotyping and biomarker discovery
- Population surveillance
Clinical NLP is a specialization of NLP that allows computers to understand the rich meaning that lies behind a doctor’s written analysis of a patient.
Normal NLP engines use large corpora of text, usually books or other written documents, to determine how language is structured and how grammar is formed. Taking models that were learned from this kind of writing and trying to apply it as a clinical NLP solution won’t work.
Clinical NLP requirements:-
There are several requirements that you should expect any clinical NLP system to have:
- Entity extraction: to surface relevant clinical concepts from unstructured data.
- Contextualization: to decipher the doctor’s meaning when they mention a concept. For example, when doctors deny a patient has a condition or talk about a patient’s history.
- Knowledge graph: to understand how clinical concepts are interrelated, like the fact that both fentanyl and hydrocodone are opiates.
Doctors don’t write about patients like you would write a book. Clinical NLP engines need to be able to understand the shorthand, acronyms, and jargon that are medicine-specific. Different words and phrases can have exactly the same meaning in medicine, for example dyspnea, SOB, breathless, breathlessness, and shortness of breath all have the same meaning.
The context of what a doctor is writing about is also very important for a clinical NLP system to understand. Up to 50% of the mention of conditions and symptoms in doctor’s writing are actually instances where they are ruling out that condition or symptom for a patient. When a doctor says “the patient is negative for diabetes” your clinical NLP system has to know that the patient does not have diabetes.
A knowledge graph encodes entities, also called concepts, and their relationship to one another. All of these relationships create a web of data that can be used in computing applications to help them “think” about medicine similarly to how a human might. Lexigram’s Knowledge Graph powers all of our software and is also available directly via our APIs.
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 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 the patient set analysis, all without obstructing physician communication.
NLP in healthcare is creating new and exciting opportunities for healthcare delivery and patient experience. It won’t be long before specialized NLP coding recognition enables physicians to spend more time with patients, while helping make insightful conclusions based on precise data. In the years to come, we’ll hear the news, and see the possibilities of this technology, as it empowers providers to positively influence health outcomes.
So, that was my take on how NLP is involved in the healthcare industry. Please free to contact for any further details/queries.
- What Is Natural Language Processing? – Machine Learning Mastery
- Toward a Learning Health-care System – Knowledge Delivery at the Point of Care Empowered by Big Data and NLP – PubMed
- 7 Applications of Deep Learning for Natural Language Processing – Machine Learning Mastery
- What Is the Role of Natural Language Processing in Healthcare?
- Natural language processing in healthcare
- Global Big Data in Healthcare Market: Analysis and Forecast, 2017-2025 (Focus on Components and Services, Applications, Competitive Landscape and Country Analysis)
- Natural Language Processing in Healthcare Medical Records
- What is HCC Coding? Understanding Today’s Risk Adjustment Model
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