NPL is an acronym for Natural Language Processing which is a computer language. Natural processing language and NLP Analytics is particularly used for sorting through big data. It is an advanced language that is programmed or thought to learn and understand human language and derive meaning from both written and verbal communication. If this sounds strange to you it shouldn’t as you’ve probably come across this technology before or used it in your day today.
The most popular consumer-facing Natural Processing Language is probably Siri and the increasing popular chatbots. So yes you know what NLP is and you use it quite frequently. Lot’s of researchers, coders and data scientists look to create better Natural Language Processing solutions and seek to use NLP Analytics, improve on how well the computers understand human communications.
NPL is a great tool for data analysis which is why is widely used and requested for in the healthcare industry. NPL makes it possible to accurately measure and analyze increasing amounts of unstructured data, like emails, charts, test results, population health records etc to lead to more accurate and structured data and insights. It also provides healthcare researchers and clinicians with predictive models used for predictive analysis.
There are so many ways NLP can be applied and these different ways of application combined with big data are very useful to the healthcare industry. Analyzing healthcare information using NLP can lead to quicker results in symptoms analysis which will lead to better treatment being provided at an increased pace. Also, important documents like doctor notes which are usually handwritten can be categorized into a structured database using NLP where it can be analyzed quickly as numeric elements would.
However, despite its advantages and great potential Natural Processing has some setbacks and should not be relied on solely to solve the problems in the healthcare industry. NLP has been used in the healthcare industry for quite some time now but there hasn’t been any significant impact if any at all. Even when there has been some progress, the fundamental gaps in the healthcare industry data ecosystem and big data cloud has inherently placed limits on how much NLP can achieve.
The healthcare industry does not have a metadata ecosystem to implement appropriate NLP strategies that can achieve significant results. So what is the solution? Perhaps other data processing platforms like Predictive Analysis or Comparative analysis are better suited for the healthcare industry? The answer is “most likely not”. Though these other systems are also widely used, they have their own setbacks.
A major drawback will be that predictive analytics fails to include outcomes. Even though predictive analytics supports risk stratification through case management and other means, without outcomes data prediction is technically nonexistent. Many healthcare organizations and health researchers do not understand how predictive analytics truly works before diving head first into it. There is a general misunderstanding of its technicalities and program specifics. Without protocol and patient-specific outcomes data, predictive analytics is largely just predicting readmissions with no actual solution in sight.
Comparative data analytics has existed for quite some time now in the healthcare industry but there hasn’t been much improvement. Healthcare quality and cost continues to deteriorate and increase respectively with no hope of redemption in sight. Comparative analytics has its advantages though but they might not be enough to drive improvements and advancements in a healthcare organization. There are too many variables and variations in healthcare delivery and data to make comparative analytics as valuable as would be considered ideal.
Even though NLP, Predictive Analytics, and Comparative Analytics have their setbacks as discussed above, having at least one of these systems in a healthcare organization is better than having none at all. As improvements are made in the industry they can and should be updated accordingly.
Natural Language Processing, artificial intelligence, machine learning, and all the other types of technology in this category, all fall under what can be regarded as the future of technology. It represents advancement that is expected to take us leaps into the future. Things like self-driving cars, robot helpers, self-thinking home systems that anticipate our every need, nanotechnology, and non-invasive complex surgeries. There are so many possibilities and mastering AI technology through NLP analytics will be a huge leap for mankind.