NATURAL LANGUAGE PROCESSING: HEALTHCARE ACHIEVING BENEFITS VIA NLP

Abstract

The field of Natural Language Processing (NLP) within computer science presents a complex challenge due to the wide variety of linguistic nuances across different languages. NLP involves dividing language into semantic parts like parts of speech and phrases. Its origins trace back to the early 1940s during World War 2, driven by the need for autonomous language translation machinery. NLP, a part of AI technology, employs tools that concentrate on linguistic-conceptual relationships rather than just textual analysis, structuring and extracting meaningful data from unstructured text. One significant application of NLP is the advancement of the healthcare system. Electronic Health Record (EHR) systems revolutionized medical practice, enabling efficient diagnosis, elimination of errors, and faster treatment initiation. NLP's ability to interpret unstructured data from medical records facilitated quicker and more effective analysis, improving patient care. During the COVID-19 pandemic, EHR systems played a crucial role in coordinating patient care and surveillance. NLP also supports Clinical Decision Support Systems (CDSS), aiding medical decision-making by providing tailored clinical knowledge and patient information. Knowledge-based and non-knowledge-based CDSS utilize artificial intelligence, helping prevent medication errors and improving patient safety. The adoption of Voice Recognition (VR) and speech recognition tools, such as Dragon Medical One, surged among medical professionals globally, enhancing clinical documentation quality and saving time on transcription. NLP's impact extends to clinical trial matching, automating the process of selecting suitable patients based on specific criteria, thereby increasing efficiency, accuracy, and patient safety. The Internet of Medical Things (IoMT) is an emerging technology that connects various healthcare devices and wearable, providing real-time monitoring, improved patient outcomes, and remote patient care. Recent innovations, like the AI-based vision therapy software CureSee and AI's role in detecting Alzheimer's disease, have shown great promise in revolutionizing patient care and early disease detection. To support these advancements, organizations like SyTrue use AI, machine learning, and NLP to improve payment integrity, risk adjustment, and chart review processes, leading to increased efficiency and higher ROI. Moreover, AI-powered clinical note generation using services like Amazon Transcribe simplifies the conversion of speech to text, enhancing medical documentation and facilitating data-driven decision-making. In conclusion, the diverse applications of AI and NLP in healthcare have significantly improved the industry, enabling accurate diagnostics, personalized medicine, predictive analytics, drug discovery, remote monitoring, administrative efficiency, and innovative treatment approaches. As AI continues to evolve, its impact on the healthcare system promises to be transformative, leading to better patient outcomes, reduced costs, and improved accessibility to healthcare services.