Growing volume of unorganized clinical data is driving the need for NLP (Natural Language Processing) in healthcare.
Integrating NLP in healthcare not only helps in managing data, but also aids service providers to ameliorate patient engagement and experience. According to a report, the global NLP in healthcare and life sciences market is anticipated to reach $6.8 billion by 2028, surging at a market growth of 20.3 percent CAGR during 2022 – 2028 timeframe.
What is NLP?
Natural language processing or NLP is a branch of AI (Artificial Intelligence). NLP deals with the ability of computers to understand spoken words and text in the same way as humans do.
Natural language processing combines computational linguistics (rule based modeling of human language) with ML (Machine Learning) and deep learning models. These technologies together process human language in the form of voice data or text to understand its meaning, along with writers’ or speakers’ sentiment or intent.
Tasks of NLP include –
a. Speech-to-text, or converting voice data into text. This is required for any application that involves use of spoken questions or voice commands.
b. Selection of appropriate meaning of a word that makes most sense in that context, through semantic analysis.
c. Ability to extract subjective qualities from text such as sarcasm, attitude, emotions, suspicion, etc.
d. Identifying when or if two words refer to the same entity or person, for instance determining object or person to which a specific pronoun refers – She = Anna; it also detects an idiom or a metaphor in the text.
Additionally, NLP is a handy technology (not limited to) for spam detection, machine translation, social media sentiment analysis, and text summarization.
Role of NLP in Healthcare
1. Clinical Documentation
NLP in clinical documentation frees up clinicians from EHR systems, thus allowing them to invest their time on patients. NLP’s formulated data entry and speech-to-text dictation features help to carry out clinical documentation.
A research published in the JMIR Medical Informatics, scrutinizes the use of natural language processing on the time spent on clinical documentation, EHR usability, and data quality. The researchers examined 118 documented notes and tested four varied clinical documentation approaches amongst 31 physicians across three specialties.
The researchers concluded – ‘The study involved feasibility of approach to EHR data capture by using NLP to transcribe dictation. This dictation-based approach has the ability to decrease time required for documentation and ameliorate usability while maintaining the quality of documentation.’
However, the researchers question on how NLP enabled workflow will affect EHR usability and whether it can meet structured data and other needs while augmenting users’ usability.
2. EMR/EHR Implication and Usability
On an average, EMR (Electronic Medical Records) run between 50 to 150 MB per million records, and on average clinical note record is 150 times large. To manage this administrative workflow, physicians are replacing handwritten or typed notes with voice notes. NLP can easily carry out this function and add it to the EMR system.
Cloud based EHR software are promoting interoperability in healthcare. NLP can further augment the workflow boosting care efficiency. Physicians can automatically transcribe their interactions with patients with the aid of NLP, thus committing more time with patients.
Further, ideally EHR arranges data as per patient encounter, thereby making it difficult for physicians to locate critical information quickly (allergies or past surgeries). NLP enables EHR interface that helps in finding crucial patient data speedily.
3. Predictive Analytics
NLP in healthcare enables predictive analytics by improving population health. It scrutinizes unstructured data from multiple resources, to ameliorate population health. It can be a gamechanger for social determinants of health (SDOH).
As per an article, the WEF (World Economic Forum), explains that NLP can help researchers and clinicians to tackle the Covid-19 crisis, by going through vast amount of data that’s impossible for humans to analyze, or rather could be time consuming for humans.
Furthermore, data drives predictive analysis in EHRs and rely on surveillance solutions, algorithms, and other applications to generate insights.
Data collection is a challenge as it is collected in varied formats and sources such as lab results, through patients, allergies, medication lists, insurance, and clinical notes. Some of this data is structured, while other isn’t, hence incomplete information affects predictive analytics. Consider extracting complete data prior to conducting predictive analytics.
4. Computational Phenotyping
Phenotype is a biochemical expression of a specific trait in an organism. These traits are related to biochemical processes, appearance, disease, or behavior. Phenotyping helps clinicians and researchers to identify defects in genes if any, and compare patients’ cohorts.
NLP-enabled computational phenotyping encompass applications such as –
a. Novel phenotype discovery
b. Diagnosis categorization
c. Clinical trial screening
d. DDI (Drug-Drug Interaction)
e. Pharmacogenomics
f. Adverse drug event (ADE) detection
g. Phenome-wide and genome-wide association studies
At the University of Iowa, scientists at the Stead Family Children’s Hospital worked on a precision medicine project – Alyssa Hahn (Graduate Student, Genetics) explains how Linguamatics Natural Language Processing (NLP) is used to extract phenotype data from EMR of patients suspected of genetic diseases.
The results of the project were impressive, as manual curation detected an average of 29.1 HPO (Human Phenotype Ontology) terms, while NLP displayed 71.5 HPO terms, which gives a more detailed view of patients’ phenotype. More of the experiment is showcased below –
Accuracy in reports is what separates traditional phenotyping methods from NLP phenotyping, with latter producing more accurate results than former.
5. Surging Clinical Trial Matching
In an ideal clinical trial situation, there’s a heavy burden on staff to find patients’ that meet complicated inclusion/exclusion criteria. Several clinicians struggle to enroll patients, and this is a long complex process. Not to forget expenses to study clinical trials.
Natural language processing has the power to simplify and automate candidate selection and identification process. By applying the inclusion/exclusion criteria, NLP technology can read and understand clinical narrative to quickly detect right type of patients for clinical trials.
The thought of using NLP for detecting eligible patients for clinical trials is rather fantastic. Trial sponsors and investigators/researchers save costs and time through this technology.
Along with this, healthcare systems become more efficient because the ability of data sharing and internal data analysis improve. Additionally, patients are promised novel and successful therapies.
6. Healthcare Chatbots
NLP-enabled healthcare chatbots are capable of understanding the intent behind conversations, and this is followed by generating contextual and relevant responses.
With NLP, chatbots can be trained through multiple content examples and conversations. This allows healthcare chatbots to gain access to a wide range of data to learn from.
Chatbots can then predict questions that are likely to be asked by users, and then formulate answers accordingly.
Healthcare chatbots built by using NLP consist of five steps that explains how it converts text or speech in a code for understanding context and intent.
1. Tokenization
Sentences are broken into words and this is a part of data processing. Tokens are extracted from sentences and are used to prepare vocabulary (which is a collection of tokens).
These tokens help AI to understand fully the context of a conversation.
2. Normalizing
People on an average misspell, use abbreviation, or enter words in uppercase. There’s a lot of randomness to the way people text and communicate with chatbots.
Unless the system is able to get rid of randomness, it won’t be able to provide sensible outputs to a users’ conversation. In normalization, such randomness, irrelevance, and errors are eliminated or converted in their normal format.
Short Form: Cn I book an appointment 4 2morrow?
Converted Text For Chatbot: Can I book an appointment for tomorrow?
3. Recognizing Entities
Here, the system tries to understand the entities in a sentence. Entities are categories to which words belong to, for example names, organization, places etc.
Chatbots understand the subject of a conversation by recognizing these entities.
4. Dependency Parsing
Dependency parsing is the process of recognizing dependencies between phrases in a sentence. This is based on the assumption that every phrase in a sentence is dependent on each other, resulting in determining the right grammatical structure in a sentence.
5. Generation
This is the final step in which NLP-driven chatbots put together all the information gathered from the above four steps and devise an accurate response to be given to a user.
Future of NLP in Healthcare
NLP in healthcare has a long way to go, and with myriad of uses this technology is bound to surpass in the coming years. Semantic big data and cognitive computing, both rely on natural language processing for their growth, and these technologies are witnessing significant NLP-driven innovations.
Further, cognitive computing under NLP application is expected to exhibit substantial growth in the future. Predictive analytics from unstructured data is likely to boost because of applications such as disease tracking, behavior modeling, and financial forecasting.
With these future predictions, NLP in healthcare is likely to meet industry goals and support clinicians in decision making and treatment.
Looking to incorporate AI in your healthcare organization, then connect with Arkenea – an Artificial Intelligence app development company that specializes in NLP, AI chatbots, machine learning, and much more.