We discussed how Natural Language Generator (NLG) accelerates the entire data-to-decision journey in my previous blog. Let’s take up Natural Language Processing (NLP) and how it, plays a key role in the interaction between humans and computers in natural language.
Believe it or not, 2.5 quintillion bytes of data is generated each day. A leading contributor to this ocean of data is the Pharma and Healthcare industry, thanks to medical records getting digitized every second. This data is gathered from personal fitness trackers, medical smart devices, new research studies, government reports and health records generated every day.
With big data analytics and AI, the healthcare data of today and tomorrow can power and transform decision-making. But there are challenges along the way, one of them being data residing in a variety of unstructured formats. These include electronic health records, doctor’s notes, readings from wearable devices, research studies etc., which might not be compatible with each other. This versatility of data is a big hindrance when it comes to adopting big data because of its size and complexities.
NLP to the rescue
The Pharma industry is deluged with unstructured data. NLP can facilitate the extraction of data from diverse formats, sources and language to a common one. NLP works great in these scenarios because it facilitates seamless interaction between computers and humans using natural language.
Here’s how NLP can help maximize utilization of healthcare data:
Healthcare practitioners record vast amounts of data. NLP algorithms can enable healthcare staff to record and store general notes and observations in standard formats, which can easily be processed. This can also help them deliver greater value to patients by generating an automated and customized sets of educational materials and guidelines at the time of discharge.
Automation treatment of medical records
Hospitals could run natural language processing algorithms against unstructured data in Electronic Medical Records (EMRs). This process would automatically extract features or risk factors from the notes and also automatically store medical records.
Identifying risk factors
Unstructured patient data can provide a goldmine of information and generate deep insights about the patient’s condition. Like, is the patient depressed? What are the patient’s living conditions? Are they homeless? Such crucial data gets buried in unstructured data formats. These can help healthcare providers to extract and identify patients who have a higher risk factor and provide them with targeted treatment programs.
Improving patient health literacy
NLP-based IT tools can help process electronic health records and integrate the results in simplified formats for a layperson. This information can be shared with patients through a portal for better understanding of their illness.
So, in these ways NLP combined with AI and machine learning can be a treasure trove for pharma companies. It can help them go beyond scratching the surface, to deriving actionable insights and accelerating innovation to stay ahead of competition.
In this riveting blog series I uncover the various facets of the role played by AI & Analytics in the life sciences & pharma industry.
Other topics covered in the series:
The AI Remedy Series: Prescriptions for Intelligent Transformations
The AI Remedy Series: Reformulating the Industry with Artificial Intelligence
The AI Remedy Series: NLG – Accelerating Data-to-Decision Journey
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