Vishal Pratap Singh | Wednesday, 16 March 2022
Mainly, pharmaceutical companies are faced with a constant stream of new data flowing into often siloed information systems. Industry experts predict that about 80 per cent of that information exists in unstructured text that is difficult to extract and use, despite its paramount importance in driving clinical and commercial outcomes. Pharmaceutical companies find themselves increasingly overwhelmed with volumes of inaccessible data.
Natural Language Processing
(NLP) is introduced as a tool to overcome the limitations of time consuming, manual searches through mountains of data for pharmaceutical and life sciences companies. The global Natural Language Processing in healthcare and life sciences market is estimated to be valued at US$ 3,071.9 million in 2021 and is expected to increase to US$ 16,778.1 million by 2028, exhibiting a CAGR of 27.4 per cent over the forecast period, 2021-2028. “Researchers and data scientists used to lack effective search tools to find the right information from the unstructured data which is now possible with the help of NLP”, says Malaikannan Sankarasubbu, Vice President, AI Research, Saama Technologies.
Mobilizes Textual Data at Larger Scale
NLP has emerged as an effective and user-friendly technology for pharma sector. Natural Language Processing has enabled organizations to embrace enterprise wide solutions that reduce previous, disjointed department to department approaches to generate insights and evidence.
The core value of enterprise wide NLP technology is that it mobilizes textual data at scale, enabling it to handle the volume, velocity and variety seen in enterprise wide data. Pharmaceutical companies have multiple sources of useful drug and safety data at their disposal, including electronic health records, medical literature, patent filings and social media, to cite just a few.
Since, these companies are overwhelmed with data, they are leveraging NLP to mine unstructured text based documents and then convert that data into structured information that can be effectively analysed and used in decision making or for predictive modelling.
Helpful in Drug Discovery Process
Natural Language Processing algorithms can assist at all stages of the drug discovery pipeline, from analysing clinical trial digital pathology data to identifying predictive biomarkers. As NLP usage becomes more widespread, so too will be its applications. With technological advancements and growing opportunities, the applications of Natural Language Processing continue to rise, which equally increases the speed of its technological advancement.
Natural Language Processing is useful in better drug discovery processes as it has clear ability to speed up and reduce errors in data analysis and development pipelines.
According to World Health Organization (WHO), this can be proved as a good news for the millions of patients affected every year by inaccurate or delayed diagnosis that then add billions in costs to healthcare systems across the globe. Provides Real World Information of Patients
Earlier pharmaceutical companies used to gather information from traditional techniques such as focus groups, interviews and questionnaires which were financial burden and time consuming for them.
Now, Natural Language Processing provides new ways to address even larger population sizes through the complex analysis of online posts and reviews from patients. Using real-world and up-to-date information from social media platforms also helps pharma companies identify and respond to any concerns or developments, as NLP filters through the unstructured data to produce clear breakdowns of information.
Provides Valuable Data Insights
The wealth of information is worthless unless it is used to extract valuable information that drives results. Without using it effectively, one can get drowned in a sea of useless information growing deeper every minute. It requires reimagining the approach to draw value, structure these massive databases and draw valuable insights to streamline their operations.
Reduces Administrative Costs
Natural Language Processing can bring down operating costs by helping extract helpful information from extensive unstructured data such as medical notes and automating billing operations. It removes the scope of human error, which was a high risk situation for the pharmaceutical Industry in past.
Provides Urgent Care to Patients
Natural Language Processing is also helpful in aiding physicians and other healthcare staff with predictions, post-surgery assistance and similar decision making support. It can analyse, compare and process databases to understand the best course of action and create care guidelines best suited for the patients.
Natural Language Processing algorithms, specifically text analytics, work wonders in this scenario. Call operators or chatbots can analyse queries in multiple languages, get the best fitting answers within seconds as well as reduce wait time and distress. It can also help to detect emotional patterns and state of mind and provide urgent care where necessary.
What can be Expected from Future
Like any other technology, the more NLP will be used to automate everyday tasks, the more accurate results will be achieved by pharmaceutical companies. With the ongoing surge of COVID-19, it is imperative to use all resources at hand, including NLP to help the human population in a better way. In the future, it will play a crucial role in improving customer feedback and enhancing medical care and diagnosing previously unknown diseases.
, CDSCO.jpg)
