Applications of NLP in Healthcare
The Healthcare Industry has always benefitted from technological advancements and enabled value-based treatments over time. Besides technologies like AI, ML, AR, and more, Natural Language Processing (NLP) has taken remarkable strides in improving patient healthcare. When we say medical data, it comprises test reports, medical history, present symptoms, drug prescriptions, and more. NLP technology can help translate raw data to drive valuable insights to improve diagnosis and patient care. Let’s understand the use cases and applications of NLP in healthcare.
Data Management
Various organizations within the healthcare sector have a lot of data that has increased exponentially in recent years. Much of this is being fueled as well as streamlined by the digital transformation and increase in AI technology over the last few years which, in turn, has led to an increase in data volumes with healthcare organizations. The necessity to comprehend the data and extract insights from this data has become crucial to ensure accurate diagnosis and treatment. This is leading to the rising demand for data sourcing companies. Therefore, it's beneficial to use the latest AI systems out there for managing that amount of data. Natural language processing (NLP) is a key player in this field and has proved to be very helpful.
Computer-Assisted Coding
Besides data labelling and annotation services, NLP can help improve CAC to drive accurate and actionable insights pertaining to how codes can be obtained and what medical features can be added to make the change from fee-for-service models to value-based models smoother and simpler. This will help healthcare providers enhance patient care and customer service significantly.
Medical Documentation
It is often hectic to document data into traditional EHR practices and solutions because the data structure is quite complex. NLP can power speech-to-text interpretation helping healthcare provider record data automatically instead of wasting time on manual documentation, while data annotators make data representation super easy.
Electronic Health Record
There are several patients who require constant special attention and personalized healthcare services, along with billing and service records. To cater to this widespread need, several healthcare providers have developed their own virtual assistants to help with administrative tasks, customer service duties, and front-desk burdens, while more staff can be dedicated to providing patient care. NLP can help build smart virtual assistant models to cater to the tasks performed by medical transcribers and ordering assistants. NLP also helps with EHR practices eliminating the additional burden that comes with traditional healthcare ecosystems.
Drug Discovery
Drug discovery is a race against the clock and it's becoming more of a challenge, as researchers are often under strict deadlines. This was evident during the race to find a vaccine for covid-19. The first step in a drug discovery process is identifying the biological origin of a disease. This usually involves understanding the genes involved and searching for existing literature. A huge amount of data from medical journals, patient records, and beyond has made it too difficult for the processing tools necessary to manage all that data. NLP can be used to quickly find information about similar diseases in the drug discovery process. They are able to extract this info from unstructured data sources. NLP-based text-mining solutions are being used by companies to find, access, and analyze medical data from multiple data sources including patient records, trial notes, prescriptions, research papers, patents, knowledge graphs, patient history, and more.
Predictive Analysis
NLP is also solving population health problems by enabling predictive analysis in the healthcare sector. NLP helps analyze electronic medical records to predict which patients are at higher risk. This not only helps in early diagnosis but helps save lives while there is still time. In fact, several case studies have proven NLP-based algorithms to be more accurate compared to manually reviewed clinical notes, helping predict risk with around 90 percent sensitivity.
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