The quality and quantity of healthcare data are continued to expanding. It is improving due to the introduction of new technologies which facilitates the storage and accessing of data or information in an easier form. However, usually maximum of these data are stored in an unstructured form, which becomes difficult to analyze data on a larger scale.
Natural Language Processing algorithm has already done wonders and proved itself the best technology in the Healthcare Sector. It represented a huge potential in easing the documentation of clinics and enabling voice-to-text dictation. The recent report from ‘MarketsandMarkets’ shows that the NLP market is expected to grow at a CAGR of 16 percent until 2021, and results into a $16 Billion of market opportunity. NLP has the ability to process all the available data and identify and fetch the information.
With reference to the existing cases in the healthcare and other markets, NLP is having an increasing effect in the following areas:
1. Clinical Data and Virtual Administrative Assistance: In the future, the impact of NLP will be much more in healthcare, it will become more interactive and easy to understand commands and requests.
Natural Language Processing can improve its effectiveness by using the applications which are easy to interact with and collect information from patients and administrative staff. These can be available in the form of mobile applications which can be presented to the user with a Virtual Assistance, over which user can request items verbally, as well as, available during the kiosk situation where patient and user sit in front of. The kiosk can be minimized with a self-service system which has a computer-based system and can interact with the patient. It might also be true that in future these virtual employees will be selected based on the user preference.
In the present scenario, the NLP has been integrated with the various advanced ‘Interactive Voice Response’ (IVR) System. IVRs are automated-response phone system which interacts with the customer during a phone call in the hospital. Even these IVR interacts with the customer like press button to pay the bill or other inquiries. These Traditional System would become outdated and inefficient compared with an IVR System which uses Natural Language Processing. Virtual Assistance can provide more facility, more interactive, and ease in using for the caller.
2. Data Mining and Analysis: All the researchers and clinics can access clinical information from Electronic Health Record (EHR) System and other systems through Natural Language Processing and Data Mining to retrieve all the details at once for the treatment of the disease. It also allows to fetch and collect information from the available information about the effectiveness of the plan of the treatment as well as the common factors for specific diseases. Through which NLP can help in tagging and categorizing the available information.
3. Data Collection and Extraction: Many healthcare which is updating their system with new Electronic Health Record System to improve the facilities are facing some challenges with their pre-existing system and the data in it. Some data are in unstructured form and in various formats, like voice file, text file, PDFs, and scanned or handwritten documents.
While using Natural Language Processing, it can provide the necessary tools which help the conversion team to extract and mine information. Such capabilities are recognized with the name-entity and are typically used to assist in this case and others for other search and extraction models.
4. Market Analysis: The ability to extract information from various sources like Social Media and other sources which enables organizations to interact with their client and can get a good sense of the market’s overall feelings about the specific organization or the brand. Healthcare departments can access more information through analysis and using NLP with Social Media as well. With this facility, NLP has eliminated the requirement of traditional surveys because it provides a facility to access and understand customer’s response through online posts.
5. Real-Time Translation Services: Real-time Translation Service provides clear and accurate communication, as well as helps in providing more accurate treatment and a better understanding of a patients symptoms. Some providers even use translators through teleconferencing to reduce the expenses associated with the transportation of the translator, but it will change with NLP’s translation capabilities. Having an application capable of machine learning will provide healthcare organizations a new and on-demand way of translation.
Healthcare and clinics have access to a treasure type of useful data, but the maximum of data is stored in a wrong manner or in the form of unstructured text. NLP text-mining will play a huge role in identifying an individual’s risk, allowing provider with more opportunities to proactively address their health demand.