How Natural Language Processing is Going to Change Healthcare
The adoption of Natural Language Processing in healthcare is rising because of its recognized potential to search, analyse and interpret mammoth amounts of patient datasets. Machine learning in healthcare and Natural Language Processing technology services have the potential to harness relevant insights and concepts from data that was previously considered buried in text form by using advanced medical algorithms. Natural Language Processing in healthcare media can accurately give voice to the unstructured data of the healthcare universe, giving incredible insight into understanding quality, improving methods, and better results for patients. By 2026, the global natural language market is expected to reach US$35.1 billion, growing at a CAGR of 20.3 per cent.
Capable of Leveraging Unstructured Data
Physicians spend a lot of time to write down the how and the why of what’s happening to their patients in chart notes. These notes aren’t easily extractable in ways the data can be analysed by a computer. When the doctor sits down with its patient, and documents their visit in a case note, those narratives go into the Electronic Health Record Systems (EHRs) and get stored as free text.
Huge volumes of unstructured patient data are entered into Electronic Health Record Systems on a daily basis, but it’s hard for a computer to help physicians aggregate that critical data. Structured data like claims or CCDAs may help determine disease burden, but gives a limited view of the actual patient record. Big data analytics in healthcare shows that up to 80 per cent of healthcare documentation is unstructured, and therefore goes largely unutilized, since mining and extraction of this data is challenging and resource intensive. Without Natural Language Processing technology, that data is not in a usable format for modern computer based algorithms to extract.
Natural Language Processing medical records using machine learned algorithms can uncover disease that may not have been previously coded, a key feature for making Hepatocellular Carcinoma disease discoveries. Healthcare Natural Language Processing uses specialized engines capable of scrubbing large sets of unstructured health data to discover previously missed or improperly coded patient conditions.
Saves Time with Fast Processing
Natural Language Processing software for healthcare can scan clinical text within seconds and identify what needs to be extracted. This frees up physicians and staff resources to focus more on the complex matters and reduces the time spent on redundant administrative policy. Value decision support can be obtained, when computers can understand physician notation accurately and process that data accordingly. These insights can be of significant use for future drug research and personalized medicine, which is good for patients and providers. “Our Natural Language Processing models are the best in the world for Indian languages,” says Ganesh Gopalan, CEO of Gnani.ai.
A different advantage offered by medical records of Natural Language Processing is the ability for computer assisted coding to synthesize the content of long chart notes into just the important points. Historically, this process could take organizations weeks, months or even years, to manually review and process stacks of chart notes from health records, just to identify the relevant information.
Challenges and Limitations of NLP
Indian Health System has not yet begun using Natural Language Processing to its full potential. This is mainly because implementing Natural Language Processing successfully comes with significant challenges.
The old saying “garbage in, garbage out” applies to Natural Language Processing. Good, usable data can only be extracted if the data is easy to identify. When digging out data from Electronic Health Record Systems (EHRs), analysts often find a problem with the way data is entered. People commonly enter type information, which increases their tendency to use shortcuts and create templates. Natural Language Processing looks for sentences, not templates, making it difficult to handle data within templates. Cut and pasted text presents another challenge; this shortcut leads to propagating more patient data than is relevant as well as outdated or inaccurate information throughout health records, making clinical notes less useful.
Natural Language Processing runs on text or a series of words strung together. Natural Language Processing systems need to extract meaning from text and infer context, which is not easy to do. If developers don’t manufacture Natural Language Processing systems well to find meaning from the start, the systems won’t scale well.
Sublanguage, a subset of natural language, is another challenge for Natural Learning Process. Medical language is a sublanguage with a subset of vocabulary and different vocabulary rules from the main language. To extract meaning from sublanguage, Natural Language Processing systems must understand the rules of that language. Social media, for example, is a sublanguage. It uses abbreviations and emoticons to express meaning versus using words for the same concepts. With these differences, analysts cannot run Natural Language Processing system trained on newspaper text on social media and expect it to extract the meaning.
Medical language has different sublanguages within it. For example, medical blogs and clinical notes use different language. Because of these differences, health systems should not purchase off the shelf Natural Language Processing systems built for one sublanguage and use it on another. Developers and analysts have to tailor Natural Language Processing systems for use on a specific language (e.g., healthcare) and that tailoring process takes time.
With linguistic variation, there are many ways to say the same thing. For example, derivation, in which different forms of words have similar meaning and synonyms in which one concept, has different words. Natural Language Processing doesn’t yet distinguish linguistic variation.
Future Scope of NLP
Natural Language Processing plays a significant role in accelerating the decision making process in the healthcare sector. However, the real rewards of developing good algorithms will depend heavily on the quality of the data that they acquire and maintain. The faster decision-making process will allow physicians to focus on the value added care of patients. Natural Language Processing with deep learning and computer vision can process a variety of data together to take precise decisions. Collaborative research can lead to a higher level of treatment in Healthcare. Considering the impact of Artificial Intelligence techniques, these systems need to be designed and built very carefully in a larger socio-ecological context of clinical care settings to provide better healthcare to society.