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The Current State of Healthcare Analytics
The current state of healthcare analytics is in a bit of a flux. The industry is still coming to grips with the potential of big data, and there are a lot of different approaches being taken to try and make sense of it all. NLP is one tool that is beginning to be used more frequently in healthcare analytics, as it can help to identify patterns and trends in large datasets.
Much of the current state of healthcare analytics is based on structured data. This includes data that can be easily tabulated and analyzed, such as claims data, laboratory test results, and demographic information. However, there is a growing need for analytics that can make use of unstructured data as well. NLP (Natural Language Processing) is one way to glean insights from unstructured data. For example, NLP can be used to analyze free-text clinical notes. This could provide valuable insights into disease progression, treatment effectiveness, and more. There are many potential applications for NLP in healthcare analytics. However, the technology is still in its early stages of development. There are challenges associated with making NLP scalable and accurate enough for real-world applications. Nonetheless, NLP holds great promise for healthcare analytics and is worth further exploration.
The Potential of NLP
Uncover patient insights buried in medical text with solutions. One of the biggest challenges facing healthcare analytics today is the lack of standardization. There are a plethora of different data sources, formats, and terminologies in use. This makes it difficult to compare different datasets.
Another potential application for NLP is in disease surveillance. By analyzing large amounts of data, NLP can help to identify patterns and trends in disease outbreaks. This information can then be used to help prevent or contain outbreaks.
Challenge is the sheer volume of data that is being generated. It is estimated that by 2025, the global healthcare data volume will reach 2,314 exabytes! That’s a lot of data to sift through! But despite these challenges, healthcare analytics continues to forge ahead. New techniques, such as NLP, are being used to make sense of unstructured data sources, such as doctor’s notes. NLP can help us to automatically extract important information from these notes, such as diagnoses and treatment plans. So, while there are still some challenges to be overcome, healthcare analytics is making great strides. With the help of NLP, we are unlocking the power of big data and using it to improve patient care. There are various phases of language processing or as we like to call them as various levels of NLP as depicted in Fig,1.
Fig 1: Different levels of NLP
(Source: Forbesindia – https://bit.ly/3AfYNws)
The Benefits of NLP
There are several different applications for NLP in healthcare analytics. One of the most promising is its ability to help identify adverse events. Adverse events are a major problem in healthcare, and they can often be hard to spot. By using NLP to analyze patient records, it may be possible to identify adverse events sooner and help prevent them from happening.
Another use is to detect errors in medical records. For example, NLP can flag cases where a procedure was incorrectly recorded, or a medication was prescribed incorrectly. This can help reduce the risk of medical errors and mistakes.
The major benefit to enhance existing NLP systems can be the combination of multiple NLP techniques. For example, text generation can work as a data augmentation method for achieving comparable results in many applications with limited original data, such as training systems and other clinically relevant tasks. Through automatic question generation, questionnaires and surveys for population screening data can be generated from EHRs, which may outperform handcrafted ones. Machine translation has also proven beneficial for various text-based tasks by increasing the availability of multilingual healthcare information, implying the possibility of improving the performance of current systems. In addition, exploration of general knowledge and domain knowledge in the field of NLP for smart healthcare deserves further attention and verification.
The Challenges of NLP
There are also some challenges that come with using NLP in healthcare analytics. One challenge is that there are many different dialects of English used in medical records. This makes it difficult for NLP algorithms to understand the content of these records. Another challenge is that medical records can be full of jargon and abbreviations that are unfamiliar to most people. This can make it difficult for NLP algorithms to accurately interpret the information contained in these records.
The Future of NLP
There are several uses for NLP in healthcare analytics. One common use is to identify keywords in medical records. NLP is still a relatively new tool, and there are still a lot of unexplored potential applications for it in healthcare analytics. As more and more organizations begin to realize the power of NLP, we will likely see even more innovative uses for it in the future. Finally, NLP can be well evolved, when we identify several directions for future works, notably combining multiple NLP techniques, developing end-to-end applications, few-shot learning, and incorporating multimodal and longitudinal data. With more diverse data flowing in and understanding new conceptual trends in market, we’ll be processing and formulating new trends in Clinical. NLP. Fig.2 shows new trends in entity recognition in NLP.
Fig 2: Clinical NLP of Electronic medical record
(Source-NVIDIA: https://bit.ly/3txvH7V)
References:
- Chatbotsmagazine.com (https://bit.ly/3O9bLSq)
- Zhou et al.: Natural language processing for smart healthcare
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