Today’s industries are storehouses of data. Though big data is valuable, what we have is overflowing data with no real insight. Gathering actionable, data-driven findings that create business value is a herculean task for any industry and it is even more challenging for the pharmaceutical industry. This is due to availability of excessive amounts of data coupled with stringent national and international regulations.
This is where NLG comes in – the AI translator that helps convert data into natural language representation. With technology progressing faster than ever before, next-gen NLG software has the ability to summarize large amounts of data and elucidate the numbers. This is why I say that one NLG paragraph is worth a 1000 words as it is valuable in generating reports that are more than just narrative descriptions of the collected data. The Pharma industry has also started realizing the benefits of implementing NLG.
Some of the benefits offered by NLG to the pharma industry are:
Enhancing Report Generation: With NLG, the data is automatically analyzed, interpreted and the most important parts are identified and the report is then generated in simple language. This process where AI is brought into business intelligence by automating the routine analysis saves the industry a lot of time and money. NLG can make it easier to understand reports that are generated from the hoard of collected data of the pharma industry.
Improving the Understanding of Quantitative Data: NLG offers a clearer understanding of quantitative data, which makes things easier as data analysts can now spend majority of their time to analyze data rather than to create it. The creation of narratives/descriptions by NLG is especially advantageous to the pharma industry as just looking at numbers will not solve anything. These NLG based narratives supporting the numbers are of much more value. The NLG tools make it simpler and less time consuming and by using smart automation for routine tasks, the productivity rises and human effort can be diverted to creative and high return activities.
Effective Patient Communication: Communication is key in any industry and pharma is no different. Healthcare professionals do not have the time to craft documents, describe the information in structured databases and present them in a tabular/graphic form. The good news is that these documents may be automatically generated from structured data using techniques from natural language generation (NLG). Depending on the audience and the situation, the content, organization and the language of the document can be selected. Health education materials, explanations and critiques in decision support systems, medical reports, progress notes etc. can be generated using NLG. This means no more beating around the bush and sending the same type of communication to all patients. The pharma industry can thus use NLG to communicate effectively using patient awareness programs.
Curating Regulatory Documents: In the pharma industry, the patent and R and D process is a long and tedious one and it leaves only about 10 years for a company to commercially leverage a drug. It is critical that at each stage, the drug is resourcefully managed. This also includes documents which churn out in huge numbers at each stage of pre-clinical, clinical and commercialization. Any blunder here can affect the time to market of the drug. NLG makes it a whole lot easier to curate regulatory documents which can take a lot of time and energy if done manually. NLG ensures improved data quality, reduced redundancy and duplication of information, information security, enhanced and easy access to stored records and regulatory compliance by creating audit trails of access and activity performed on stored information.
The Pharma industry invests a considerable amount of money annually into research programs, patient care programs and more, in order to offer better care for patients across the world. However, unless it makes effective use of data collected over the years, it cannot do its work efficiently. NLG has the ability to analyze large amounts of data, pick out the significant data and convert it into easily understandable reports that can be used to augment the pharma industry.