Natural Language Processing is on the Brink of Redefining Healthcare Industry
Healthcare is one of the major industries in adopting artificial intelligence to detect, analyze and diagnose diseases. From automated eye scans and robotic surgery to assessing the cries of new-born babies, faster drug development, personalized medicine, to virtual medical treatment, AI is being utilized.
The industry is fast realizing the significance of data, accumulating information from EHRs, sensors, and other sources. But, to make sense of the data collected always remains challenging for the healthcare space. Currently, the industry is also facing major challenges between the expected end of the EHR (Electronic Health Records) Incentive Programs and the deployment of the MACRA framework, a change that may not come to pass being as smooth as CMS could anticipate.
Despite the uncertain environment, healthcare providers are taking the opportunity to reinforce their big data safety measures and build the technological infrastructure required to meet the looming challenges of value-based reimbursement, population health management, and the overwhelming tide of chronic disease.
This is where Natural Language Processing (NLP) comes in. It’s a focused branch of AI aimed at interpretation and manipulation of human-generated spoken or written data.
Some NLP efforts are attentive regarding beating the Turing test by generating algorithmically-based entities that can mimic human-like retorts to queries or conversations. As NLP is becoming popular for representing and analysing human language computationally, the global market for NLP in the healthcare sector is expected to value US$1,756 million by the end of 2025, from US$486 million in 2018. The market will grow at a whopping CAGR of 20.1% over the projected timeframe.
Rising Facets behind NLP in Healthcare
In the healthcare industry, NLP can assist in improving the precision and completeness of EHRs by converting the free text into standardized data. Usage of an analytic tool and deep learning to construe medical data and advancing clinical decision-making could stimulate the business growth of the global NLP in the healthcare market.
There are few tasks that drive the NLP system in the healthcare industry, including empowering patients with healthy learning; briefing lengthy blocks of narrative text; mapping data elements to present in structured fields from unstructured text in an EHR with the intention of enhancing
clinical data integrity; supporting value-based care; responding free-text queries that require the synthesis of multiple data sources; and recognizing patients who require advanced care.
NLP has a range of potential applications. It can improve the completeness and accuracy of EHRs by converting free text into standardized data. It could be able to make documentation requirements easier by enabling providers to command their notes or create personalized educational materials for patients ready for discharge.
How NLP Benefits Healthcare?
With NLP, healthcare providers can accomplish several benefits, especially the ability to deliver better and life-saving care to patients. The technology can assist in automating and making workflows more effective when it used correctly and aptly. It can also help in encouraging and fostering physicians and other care team members to actually utilize unstructured data to deliver better care to patients.
In decision support, NLP can be utilized to fill potential care gaps, and also supports healthcare organizations to capture risk scores more precisely.
Some healthcare provider organizations leveraging NLP to automate compliance processes. NLP analytics analyze provider documentation and flag those that need clinical coder review, recognizing charts that may have compliance concerns in need of human review.
Despite, there is slow uptake in NLP’s adoption. The first reason behind this is the technology was inadequate for a long time, and clinical technologies weren’t designed to efficiently assimilate with NLP. The second reason is the initial exuberance of large-scale EHR implementations driven by federal funding in the early to mid-2000s led many people to believe EHRs would be able to capture all of the distinct data and more of the unstructured data than they perform.
In a nutshell, NLP could be utilized to examine speech patterns, which could prove to have diagnostic potential when it comes to neurocognitive injuries like Alzheimer’s, dementia, or other psychological or cardiovascular diseases. Its evolution in healthcare industry obviously will increase in the coming years as it offers an array of functionalities.