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It is not a surprise that the amount of data that is being generated in the world has been growing exponentially. Some studies suggest that we are generating a massive 2.5 quintillion bytes of data each day and this volume is set to grow higher with the proliferation of trends such as the Internet of Things (IoT).
Healthcare is one industry that is responsible for a large chunk of this data that is continuously being generated. Currently, medical records are increasingly getting digitized. Personal fitness trackers and smart devices are recording tons of precious datapoints that can help transform how we look at healthcare today. In addition, there are new kinds of data being released every single day via new research studies, Government reports etc.
With big data analytics and AI, this data has the potential to bring a paradigm shift in healthcare and to transform decision-making as a whole. One big challenge is that this data resides in a variety of unstructured formats such as electronic health records, doctor’s notes, readings from wearable devices, research studies etc., that might not be compatible with each other. This immense diversity in data types and sources is a big hindrance when it comes to adoption of big data because this data is extremely difficult to process and merge into a single database.
Natural Language Processing (NLP) can play an important role in facilitating the extraction of this data from diverse formats and language styles to a common format. NLP works great in these scenarios because it facilitates the seamless interaction between computers and humans using natural language.
Here are a few ways in which NLP can help maximize utilization of healthcare data.
Easier Documentation: Healthcare practitioners typically need to record a ton of data in the course of any treatment process. The use of NLP algorithms can enable general notes and observations recorded by healthcare personnel to be stored in standard formats that are suitable for processing. This could also enable them to deliver greater value to patients in the form of an automatically generated, tailormade set of educational materials and guidelines that patients can take home at the time of discharge.
Automation of Medical Records: Hospitals could run natural language processing algorithms against the unstructured data stored in Electronic Medical Records (EMRs) and automatically extract features or risk factors from the notes in the medical record. This provides a great way to automate the storage of medical records.
Identifying Risk Factors: Unstructured patient data can provide a goldmine of information and generate deep insights about the patient’s condition, which are not apparent in plain sight. Is the patient depressed? What’s the living status of the patient? Are they homeless? Crucial data such as this, which is often buried in structured formats can help providers extract certain risk factors and identify patients who are at a higher risk so that they can be targeted with specific treatments options or programs.
Improving Patient Health Literacy: NLP-based health IT tools can help process electronic health records and other data and integrate the results in formats that are easy to understand for a lay person. This information can be shared with patients through a patient portal, which could improve patients’ understanding of their health information. In the digital age, they say data is the new oil. However, for this data to deliver its true value, it needs to be processed and stored in a format that makes it accessible. NLP could be a great tool in AI and machine learning to help facilitate this, bringing immense value to the healthcare sector.
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