Natural language processing or NLP is an artificial intelligence technology with the ability to recognize and process human language (whether spoken or written). Simply put, an application which translates your texts and that smart assistant in your phone are all part of NLP technologies.
In the last decade, natural language processing in medical field has experienced a tremendous boost in EHR and EMR development. Particularly in healthcare, 64% of physicians used EHR to communicate with patients in 2015. Think the number is not high? What if we tell you that it is a 50% boost compared to 2013?
Check the article below to learn more about the benefits of custom-tailored NLP solutions and their types. Learn how Empeek helps businesses grow by improving their NLP technologies and choose the ones which suit your company the best.
History of NLP in Healthcare and Its Functions
The concept of NLP technologies occurred back in the 1950s. Three decades later, NLP pinnacled in the 1980s’ linguistics. It took a few more decades before natural language processing in healthcare became a weighty aspect to invest in.
The latter happened due to the substantial increase in cloud computing. Another factor was the boost of investment by technological companies in the development of algorithms able to recognize human speech.
The factors which NLP influenced in modern healthcare are:
- Improved clinical documentation. Digital recording of health information saves doctors time and prevents human error yet makes the whole treatment more efficient. Meanwhile, speech recognition technologies have improved data processing immensely. No need for pen and paper!
- Accelerated clinical trial matching. Choosing patients who suit the clinical trials and have the lowest health risks is a vitally important task. However, healthcare professionals perform it manually, making the process a long and costly routine. How can NLP help? It adds automation to the candidate identification process;
- Increased clinical decision support. NLP technologies reduce the possibility of medical error. Clinical decision support systems act as programs exploring the info in EHRs. Hence, CDS systems serve as reminders to healthcare specialists in regard to patient care guidelines.
NLP in Medical Field – Key Technologies in Healthcare
NLP technologies help healthcare specialists structure the data, organize clinical notes, convert speech to text, and play many other different roles in healthcare process improvement. What NLP solutions for healthcare can we name nowadays? Check the list below.
Optical Character Recognition Technology
This technology converts the written or printed text into a digital product. Once turned into a digital document, the user can alter the text, copy and share it. Optical character recognition technology saves time when clinicians work with the patient data created and stored in an old fashion.
Speech Recognition Technology(OCR)
Now doctors can communicate the treatment specifics to the patient while letting the program note them down. OCR technologies compose, sort out, and store the data into the computer. They reduce human error and save time for a healthcare specialist.
With the technological revolution on the rise, medical records and NLP technologies go hand in hand. But do you know that NLPs can make predictions about patients? For instance, NLP-based analytics predicts health deterioration in patients with the highest risks for developing post-surgical complications.
Named Entity Recognition (NER)
NER acts as an extraction model which identifies an entity in unstructured data and places it under a specific category. This NLP technology helps organizations during clinical research and trials by identifying and categorizing drug names and diseases. This is especially important in the context of big databases.
Root Cause Analysis
This NLP technology will easily explain why African-Americans are the population category that suffers from obesity the most. Why so? Use the socio-cultural lens. People with the lowest income would buy the cheapest food, which is – bingo! – fast food. Root cause analysis uses the same principles while exploring diseases depending on geographical and racial factors.
This NLP for health records is all about semantic classification. While scanning texts, topic modeling groups documents according to the similar semantic structures or ‘topics’ they contain. Once you leverage topic modeling, the businesses of your healthcare clients will prosper due to more effective EHR practices that save time and money.
The Use of NLP Applications in Healthcare
Do you know that 80% of data in modern healthcare businesses, whether hospitals or pharma companies, is unstructured? Surely, that’s a huge impediment to the development of medical AI. Most of the unstructured data come from medical imaging devices, patient monitoring devices, and patient-and-staff communication (mainly audio units). Check the following NLP applications which clinical settings use nowadays.
Clinical Assertion Model
The goal of a clinical assertion model is to define a specific medical concept and assign it to a special category. Commonly, a CAM is used as NLP for medical records, specifically during the diagnosis stage. For instance, doctors assign symptoms into 3 categories: ‘present,’ ‘absent,’ and ‘conditional.’ By using the classified info, a doctor can effectively diagnose a patient in a short time.
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Clinical Deidentification Model
This particular model as a part of NLP in healthcare is used to make the personal patient data anonymous. It happens during a clinical trial when researchers decide to post the research results. In this case, the use of a deidentification model allows them to respect the participant’s privacy by making details like name and address anonymous. Launching a product with deidentification features is a great idea for your startup if you plan to attract maximum customers.
Clinical Entity Resolver
An entity resolver is used in the search for clinical entities and their further identification in a particular text. Examples of entities are drugs, names of diseases, and symptoms. Here, NLP in healthcare helps clinicians extract the necessary patient data and later use ICD-10-CM and ICD-10-PCS codes. While ICD-10-PCS can be used in inpatient hospital settings, ICD-10-CM is applied in both clinical and outpatient environments. Both systems improve medical reporting and track the efficiency of healthcare services.
Clinical Named Entity Recognition General Model
Based on the entity recognition model, this application extracts medical keywords while scanning the text. Later, it classifies them under various categories. How does medical entity recognition differ from the common one? The medical language contains tons of synonyms and alternative spellings, adding chaos to the whole vocabulary. Yet NLP in the medical field normalizes the medical vocabulary in terms of organization and classification.
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Clinical Relation Extraction Model
This NLP model uses keywords and the semantic context to identify the relation between specific named entities. Mostly used in drug research and prescription, this model defines the connection between the drug’s name and its attributes. By the same principles and a part of digitizing business processes, the model is also applied in the systematization of clinical documentation.
Financial Contract Named Entity Recognition
Like the previous model, this entity recognition application organizes the data in financial documents. It successfully prevents human error and classifies dates, sums, names of payers, and other elements. The model also erases any possibility of fraudulent information.
How to Use NLP for Medical Records in Healthcare
Did you know that 2 hours of actual time physicians spend with patients equals 60 minutes of clerical work after? Summarize these 2 hours with another 60-120 minutes spent in the evening (desk work again, of course), and you’ll get 4-5 hours in total. How to save precious seconds?
With the help of an EHR development service, healthcare specialists can reduce their desktop work by using NLP medicine. One of the recent studies conducted by David R. Kaufman included doctors who dictated notes to measure the required time. The dictation-based NLP technology proved that the time required to dictate a note was 12 minutes less on average if compared to the common write-it-down method.
Yes, the statistics are impressive and promise a bright future for NLPs. Other ways NLP expands EHR possibilities are:
- Improving the internal research in the system. A traditional EHR systematizes the data according to the patient encounter principle. In the end, finding a random detail is challenging. NLPs add functionality to the EHR’s interface by dividing it into sections. Meanwhile, clinicians can enter a word like ‘anxiety’ and see everything related to it in patient encounters. The timeline of the word would appear in a separate section, while the ‘anxiety’ notes will occur in another one. As a result, NLPs help clinicians detect the old or missed data and use it for better treatment.
- Increases patient literacy. We all know trust is key in any business, right? But how do you expect a patient to trust their doctor if they don’t understand a word in their prescription notes? This is where medical records and natural language processing blend. For instance, NoteAid is an NLP system that links terms to their definitions the lay people can grasp;
- Increases patient safety. Some EHR systems may present minor errors but high risks yet cost a patient their life. For instance, a missing decimal in the entry would turn 1,5 milligrams of a drug into 15 milligrams. Such an error can lead to overdose. Meanwhile, NLPs eliminate the possibility of such errors;
- Decreases errors and reports a problem with a system in time. What is called ‘click fatigue’ can easily cause errors and lead to safety issues. NLP technologies allow businesses to build in a reporting program that timely detects a technical issue and reaches the community. Additionally, they solve the problem of cluttered interfaces and messy drug lists.
Consider Empeek Your Trusted Partner
As a software development company, Empeek provides modern tech-product companies with innovative, user-friendly, and secure products. The efficiency of our custom-made technology solutions allowed Empeek to gain the trust of software-developing professionals all over the world. We specialize in EHR/EMR development, mHealth Solutions, Telemedicine, and other fields. Check our resources to learn more.
We are not afraid of challenges. The list of our cases included a US-based hospital that needed a new EHR system to improve the treatment plan and the telemedicine module. To help our client, we introduced an automated authentication process, added a telemedicine portal, and automated the basic hospital activities like billing and visit scheduling. In the end, the hospital’s natural language processing medical records were better organized yet effectively used in the future.
To ease the EHR pain of tech companies that provide products for the healthcare segment, we have created HIPAA-compliant software that unites healthcare providers, patients, and their relatives in one space. The software easily integrates with an EHR system, protects personal data, and stores the latest changes in patient info.
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How can NLP expand my target audience?
NLP opens ‘linguistic’ opportunities for tech startups and medium tech companies. More healthcare businesses will turn to your products. Why so? For instance, they choose NLP-focused translation and explanation of the words. In the end, their cooperation with non-native English speakers (patients) is smooth and efficient, which allows them to attract more non-native speakers.
Which NLP technology should my tech company focus on?
The most popular and efficient NLP solution is free-text software which includes speech recognition and converts written or printed documents into digital texts.
Can I disregard the use of NLP technologies in my healthcare business?
You certainly can, but your business won’t grow. The deskwork will pile up, and the employees will exhaust themselves with manual writing. Meanwhile, NLP for electronic medical records enhances efficient treatment, reduces human error, saves physicians time, and organizes the unstructured data most effectively.
To learn more about the latest NLP solutions, contact Empeek!